From 0f95e04763557929e4f4c6711c108c0d9cf7b818 Mon Sep 17 00:00:00 2001 From: Pierre Marcenac Date: Wed, 5 Jun 2024 10:31:39 +0200 Subject: [PATCH] Rerun Croissant Health reports for Hugging Face and OpenML (#660) --- health/README.md | 2 +- health/crawler/spiders/openml.py | 4 +- health/visualizer/report_huggingface.ipynb | 850 ++------------------- 3 files changed, 51 insertions(+), 805 deletions(-) diff --git a/health/README.md b/health/README.md index 54616e678..f96250132 100644 --- a/health/README.md +++ b/health/README.md @@ -17,7 +17,7 @@ pip install -r requirements.txt # Test the spider locally. # In huggingface.py you can uncomment the line in -# `start_requests` to produce crawl fake data. +# `list_datasets` to produce crawl fake data. scrapy crawl huggingface # When you're ready, the following commands launch a new job: diff --git a/health/crawler/spiders/openml.py b/health/crawler/spiders/openml.py index a3032b653..85a2ac715 100644 --- a/health/crawler/spiders/openml.py +++ b/health/crawler/spiders/openml.py @@ -20,4 +20,6 @@ def list_datasets(self): def get_url(self, dataset_id: str): """See base class.""" - return f"https://openml1.win.tue.nl/dataset{dataset_id}/croissant.json" + return ( + f"https://openml1.win.tue.nl/{dataset_id // 10000:04d}/{dataset_id:04d}/dataset_{dataset_id}_croissant.json" + ) diff --git a/health/visualizer/report_huggingface.ipynb b/health/visualizer/report_huggingface.ipynb index b66c02ebb..813076bb7 100644 --- a/health/visualizer/report_huggingface.ipynb +++ b/health/visualizer/report_huggingface.ipynb @@ -67,7 +67,7 @@ { "data": { "text/markdown": [ - "Scrapped 108049 datasets for huggingface" + "Scrapped 150210 datasets for huggingface" ], "text/plain": [ "" @@ -78,7 +78,7 @@ }, { "data": { - "image/png": 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cUqVKKdvNi0KFCuHl5cWePXt0zoXfv3+fbdu2UaNGDWV+xKtUsmRJTp48qbMP+/bt07ucM7fve5UqVbCxseGnn37SeZ+2bt2qNxT/Mry9vUlPT9fZflZWFitXrszV+tmjJo//rGm1WpYtW6ZTr2jRotSqVYv169frhcnsdQsVKsRHH33Ezp07cwwNcopAXWRkQGXGjBlDcnIyjRs3pmzZsqSnp3Ps2DF27NiBk5OTcve60qVL06dPH3788Uc6d+5MkyZNMDY25vTp0zg4OBAaGvrM7Xh5eWFkZESfPn3w9/cnMTGRn3/+GVtb2xwnBLq4uDBy5EhOnz6Nra0t69ev58GDB4wbNy5f9rtjx46sWLGC0NBQunXrhr29PVu3blWGsJ/1rfpZSpUqRXBwMFOmTOHWrVt8+OGHmJubc/PmTXbv3s0nn3xCz549+fPPP/n+++/x8/PDxcWFzMxMNm/erPxBztahQwfmzZvHyJEjqVKlCv/880+O38JzUrlyZVavXs2PP/5I6dKlKVq0KJ6enjRv3pzatWtTuXJlbGxsOH36NDt37nzhuwoGBwfz+++/07lzZzp37kyhQoVYu3YtaWlpDB069IXazKsOHTqwc+dOevXqRdOmTbl+/Tpbt27VO/ee2/fd2NiYAQMGMHr0aLp3707Tpk25desWGzZsyPO8kmf58MMPcXd3Z8KECVy/fp2yZcvqnPt/3s9h2bJlKVWqFBMmTCAyMhILCwt27tyZYwj86quv6NSpE23atKFjx444Oztz69Yt9u/fz+bNmwEIDQ3lyJEjfPLJJ3To0IFy5coRGxvLmTNn+OOPP/jrr7/ybd/Fm03CgMoMGzaMiIgIDhw4wNq1a0lPT6dEiRJ07tyZzz//XOdmRIMGDcLZ2ZkVK1Ywbdo0TE1N0Wg0OjP3n6Zs2bLMnDmT6dOnM2HCBOzs7OjUqRNFixblyy+/1Kvv4uLC119/zcSJE7l69SrOzs5MmzYtx6sUXoS5uTlLly5lzJgxLFu2DDMzM1q3bo2HhwcDBgx4qTvKffbZZ7i4uLBkyRJmzZoFPJrU6OXlpdyoR6PR4O3tzb59+4iMjFSO5fz586lWrZrSVr9+/YiOjmbnzp3s2LGD+vXrs2DBAjw9PZ/bj379+nH79m0WLFhAYmIitWvXxtPTk4CAAPbu3cvhw4dJS0ujRIkSBAcH07Nnzxfa3/Lly7Ny5UqmTJnC3Llz0Wq1uLu7M2nSJL17DLwq9erVY/jw4SxevJgffviBKlWqMGfOHCZMmKBTLy/ve9euXdFqtSxevJgJEyZQoUIFZs+ezZgxY/LtjoOFChVi7ty5jB07lo0bN2JoaEjjxo3p168fnTp1eu52jIyMmDNnjjLfpEiRIjRu3JguXbro/V5WqFCBn376iRkzZrB69WpSU1MpUaIETZs2VerY2dnx888/M2vWLH799VdWr16tPKtkyJAh+bLP4u1goM3NmK8Qr5Cvry/ly5dn7ty5r33bS5YsYdy4cRw8eBBHR8fXvn1RMHL7vmdlZeHp6Unjxo0ZM2bMK+vP7t276devH6tWraJGjRqvbDtCPI3MGRCq8eTtWlNTU1m7di0uLi4SBN5huX3fU1NT9eZ9bNq0iZiYGGrXrv3K+pOZmcny5cuxsLCgcuXK+bYdIfJCThMI1ejfvz8lSpSgQoUKJCQksGXLFq5cucLkyZMLumviFcrt+37ixAnGjRuHn58fNjY2nD17lnXr1uHq6oqfn1++9Wf06NGkpKTg4eFBWloau3bt4vjx4wwePPiVPOBLiNyQMCBUw9vbm3Xr1rF161YyMzMpV64c06ZNo1mzZgXdNfEK5fZ9d3JyolixYixfvpzY2Fisra1p1aoVQ4YM0bkj4suqW7cuixcvZv/+/aSmplK6dGm+/vpreUy0KFAyZ0AIIYRQOZkzIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQObma4AnDhw9n48aNT12efZOS9PR05s6dy8aNG4mMjMTR0ZF27drx2Wef6TwPHR49K33atGkcP34crVaLh4cHQ4cOfeYDgOLi4vjoo4+Ijo5mxowZOpc2nTp1ik2bNnHkyBFu3bqFjY0NVatWJTg4OFcPyxFCCCEeJ2HgCR07dtS79atWq+W7777DyclJuUnJ0KFDiYiIoF27dlSpUoWTJ08yY8YM7ty5w+jRo5V1z5w5Q+fOnSlevDj9+/cnKyuLVatW0bVrV37++WflwUBPmjlzpt7NSbItWLCAY8eO4efnh0ajISoqipUrV9K2bVvWrl37zGeYCyGEEHq04rn+/vtvraurq3b27NlarVarPXnypNbV1VU7ffp0nXrjx4/XajQa7blz55Sy3r17a2vVqqWNjo5WyiIjI7XVqlXT9u/fP8ft/fvvv9pKlSppw8PDta6urtodO3boLD969Kg2NTVVp+zq1avaKlWqaENDQ19qX4UQQqiPzBnIhW3btmFgYECLFi0AOHr0KADNmzfXqdesWTO0Wi07duxQyv755x88PT157733lDIHBwdq167Nvn37SExM1Nve2LFj+fDDD6lZs2aO/alevbreTVBcXFwoX748V65cebGdFEIIoVoSBp4jPT2dHTt24OHhgbOzM4DyDPUnnzBmamoKPJojkC0tLS3HW4yamJiQnp7OxYsXdcp37NjB8ePH8/woWK1Wy/3793VChxBCCJEbMmfgOQ4dOkRMTAwtW7ZUyrIn6R07doySJUsq5f/88w8A9+7d06l74sQJMjMzKVSoEPAoIJw6dQqAyMhIpW5KSgoTJ04kMDBQefZ4bm3ZsoXIyEgGDhz4Ansp3kVarZbMzEwyMjIKuitCiFfAyMhI+Vx5WRIGnmPbtm0YGRnpPAPcx8cHJycnJk6ciKmpKZUrV+bkyZNMmzaNwoUL60z869y5M9999x0jR46kV69eZGVlMXv2bKKiogDdJ5jNmzeP9PR0goKC8tTHy5cv8/333+Ph4UGbNm1eco/F206r1RITE0NUVBSZmZkF3R0hxCtkY2NDsWLFMDAweKl2JAw8Q2JiInv27MHb21tn+L1IkSLMnTuX4OBgBgwYAICxsTFDhw5lzpw5mJmZKXU7derE3bt3WbhwoXLJYpUqVejZsydz5szB3NwcgJs3b7Jw4UK++eYbpSw3oqKiCAoKwtLSkhkzZuRbShRvr7t37xITE4OVlRVWVlYULlz4pf9QCCHeLFqtlqSkJGUkunjx4i/VnoSBZ9i9ezfJyck6pwiylS9fnm3btnHp0iViY2MpV64cJiYmjBs3jlq1aunUDQkJoUePHly8eBFLS0s0Gg1Tp04FHk38g0eXEjo6OlK7dm1u3rwJwP379wGIjo7m5s2blChRAkPD/5vmER8fT+/evYmPj2flypU6z2YX6pSZmUlsbCz29vbY2dkVdHeEEK9Q9jy1e/fu4eDg8FJfBiUMPMPWrVsxMzPD19c3x+UGBgaUL19eeX3gwAGysrL44IMP9OpaW1vrXB3w+++/U6xYMeU+A3fu3OG///7jww8/1Ft31KhRAPz9999YWVkBkJqaSp8+fbh27RqLFy+mXLlyL76j4p2Rnp6OVqvN0+iSEOLtlT0SnZ6eLmHgVYiOjuaPP/6gefPmSvp6lpSUFGbMmIG9vb3eJYdP2r59O6dPn+aLL75QvukPGjSImJgYnXoXLlxgxowZ9OrVCw8PD6UfmZmZBAcHc+LECX788Uc8PDxebCfFO0tOCwihDvn1uy5h4Cm2b99ORkZGjqcI4NGHt4ODA+XKlSMhIYH169dz48YN5s2bh4WFhVLv77//ZtasWXh5eWFjY8PJkyfZsGED9erVo1u3bkq9nO4pYGlpCYCbm5vOiMH48ePZu3cvDRs2JCYmhs2bN+us16pVq5fadyGEEOoiYeAptm7diq2tbY5D/vBoEuCGDRtYu3YtJiYm1KhRgylTpug9b8DR0ZFChQqxcOFCEhMTcXZ2Jjg4mMDAQL1nGOTW+fPnAdi3bx/79u3TWy5hQAghRF4YaLVabUF3QgiRP1JSUrh69SplypTJ8WZX4u118+ZNGjVqxLhx42jbtm1Bd0e8IfLrd15GBoQQb5V///2XWbNmcfr0ae7fv4+NjQ3lypXD19eXgICAgu6eeA0OHDjAqVOnlEu7xcuT2xELId4ax44do127dpw/f54OHTrwzTff0KFDBwwNDVm2bFlBd0+8JgcOHCA8PLygu/FOUd3IQExsEnEJOT8aWE2sLEywsTZ7fkUh3iBz5szB0tKSdevWKZfZZnvw4MFLt6/VaklNTX3nTrEkJSXp3AxNiCepLgzEJaSwfttRYuPVGwisLU1o16KGhAHx1rl+/TrlypXTCwIAtra2emWbN29m+fLlXLx4EWNjY1xdXfn888/x9vYGwNfXl/Lly9O1a1emTZvGxYsXCQ0NJTAwkPXr17N582YuXrxIfHw8pUqVomvXrnTu3FlnG9ltBAQEMGnSJK5cuULJkiUJDg6mSZMmOnXj4uIICwtj165dPHjwgOLFi9OhQwd69eqlc0OxuLg4fvjhB3799VcMDAxo1KgRgYGBuTpGGzZsYMSIESxfvpzt27ezc+dOMjIy+Pvvv4FH36rnzp3L2bNnMTAwoFatWgwdOlTnninw6KZr06dP57///qN06dIMGjSIPXv28Ndff7F3714Ajhw5Qrdu3Vi2bBl16tRR1n3a/IbLly8zY8YM/vzzT5KTkylfvjz9+vWjUaNGSp309HTmzp3Lli1buHPnDmZmZpQtW5b+/fvj5eXF8OHDlbu5ajQaZb1///0XgF9++YWFCxdy9epVDAwMcHJyon379nTv3j1Xx0+tVBcGAGLjU4iJTSrobggh8sjJyYnjx49z4cIFXF1dn1k3PDycsLAwPDw8GDhwIEZGRpw8eZI///xTCQMAV69eJTQ0lI4dO/LJJ58oDyJbvXo15cuXx9fXl8KFC7Nv3z5GjRqFVqulS5cuOtu6du0aISEh+Pv706ZNG9avX8+gQYNYsGABXl5eACQnJ9O1a1ciIyPx9/enePHiHD9+nKlTpxIVFcXIkSOBR6MTffv25ejRo/j7+/P+++/z66+/8sUXX+TpWI0aNYqiRYvSr18/kpIe/b3btGkTw4cPx9vbmyFDhpCcnMzq1avp3LkzGzduVJ7MeujQIQYMGEC5cuUIDQ3l4cOHjBgxgmLFiuWpD4+7ePEinTp1wtHRkd69e2NmZsaOHTvo168fYWFhNG7cGHj0vs2dO5cOHTrg7u5OQkIC//vf/zhz5gxeXl507NiRe/fucfjwYSZOnKizjcOHDzN48GA8PT0ZMmQIAFeuXOHYsWMSBp5DlWFACPF26tGjB71796Z169a4u7tTo0YNPD09qVOnDkZGRkq9//77j1mzZtG4cWNmzpyp8637yQuo/vvvPxYsWEC9evV0ylesWKFzuqBr16707NmTxYsX5xgGwsLClJGA9u3b4+fnx+TJk5UwsHjxYm7cuMHGjRuV25D7+/vj4ODAwoUL6dGjB8WLF2fPnj38/fffDB06lF69egGPnnHy+H1JcsPa2polS5Yod6VLTExk7NixdOjQgdGjRyv12rRpg5+fH3PnzlXKJ0+ejK2tLatWrVLud1K7dm169OiBk5NTnvqRbezYsRQvXpz169djbGwMPHqQW6dOnZg8ebISBvbv34+Pj49OHx/n4eGBi4sLhw8f1ruMev/+/VhYWLBw4UJ5TkseyQRCIcRbw8vLizVr1uDr68v58+dZsGABPXv2pH79+uzZs0ept3v3brKysujXr59OEAD9O7Y5OzvrBQFAJwjEx8cTHR1N7dq1uXHjBvHx8Tp1HRwclA8zAAsLC1q3bs3Zs2eVJ5RGRERQo0YNrKysiI6OVv598MEHZGZmKsP4Bw8epHDhwnTq1Elpr1ChQnTt2jVPx+qTTz7R+UD8/fffiYuLo3nz5jrbNzQ0pGrVqhw5cgR4dJ/7c+fO0aZNGyUIwKNj/6K3PY+JieHPP/+kadOmJCQkKNt++PAh3t7eXLt2TXmcu5WVFRcvXuTatWt53o6VlRXJyckcPnz4hfqpZjIyIIR4q7i7uxMeHk5aWhrnz59n9+7dLFmyhEGDBrFp0ybKlSvH9evXMTQ05P33339ue9lD4086evQoYWFhnDhxguTkZJ1l8fHxOh+UpUuX1gsZ2d/+b926hb29Pf/99x///vsvnp6eOW4vOjpap/6Tz5fIPn2RW0/uV/aH69OGy7PvnHr79m3g0T49qUyZMpw9ezZP/YBHcz20Wi0zZsxgxowZOdZ58OABjo6ODBw4kL59+/LRRx/h6uqKt7c3rVq1okKFCs/dTufOndmxYwe9e/fG0dERLy8vmjZtSv369fPcZ7WRMCCEeCsZGxvj7u6Ou7s7Li4ujBgxgoiICPr375+ndnK6cuD69esEBgZStmxZhg8fTvHixTEyMuLAgQMsWbKErKysPPc3KysLLy8vZej/SdnhIb8UKVJE53X26ZGJEydib2+vV/9FhtWfdl/8J49P9usePXrkOAoDUKpUKQBq1arFr7/+yp49ezh8+DDr1q1j6dKljBo1ig4dOjyzP7a2tmzatIlDhw5x8OBBDh48yIYNG2jdujUTJkzI6+6pioQBIcRbr0qVKgDKs91LlSpFVlYWly9f1rtFeG7s3buXtLQ0Zs+eTYkSJZTy7KH0J/33339otVqdD8fsb+LZ59hLlSpFUlLSU29xns3JyYk///yTxMREndGBq1ev5nk/HleyZEmAZ95mHVD297///tNb9mQfsq/qePK0ya1bt3LctpGR0XP3H8DGxoZ27drRrl07EhMT6dq1K2FhYUoYeNbDeYyNjfH19cXX15esrCy+++471q5dS9++fXMc7RCPyJwBIcRb488//9SbAAiPLpcDlEeCf/jhhxgaGjJr1iy9b6m5uQN79rfkx+vGx8ezfv36HOvfu3ePX3/9VXmdkJDApk2bqFixovItvGnTphw/fpzffvtNb/24uDgyMjIAqF+/PhkZGaxevVpZnpmZyYoVK57b72epV68eFhYWzJ07l/T0dL3l2acpHBwcqFixIhs3btT5kD98+DCXLl3SWcfJyYlChQop8x2yPd53eBRAateuzdq1a5XAltO2AR4+fKizzNzcnFKlSpGWlqaUZT/BNS4uTqfuk+saGhoqlx8+vr7QJyMDQoi3xpgxY0hOTqZx48aULVuW9PR0jh07xo4dO3ByclKuaS9dujR9+vThxx9/pHPnzjRp0gRjY2NOnz6Ng4MDoaGhz9yOl5cXRkZG9OnTB39/fxITE/n555+xtbVVJgQ+zsXFhZEjR3L69GlsbW1Zv349Dx48YNy4cUqdnj17snfvXvr06UObNm2oXLkyycnJXLhwgZ07d7Jnzx6KFi2Kr68v1atXZ8qUKdy6dYty5cqxa9cuvW/feWVhYcF3333HsGHDaNu2Lc2aNaNo0aLcvn2bAwcOUL16db755hsABg8eTFBQEJ07d6Zdu3bExMSwYsUKypcvr1ymCI+erOrn58eKFSswMDCgZMmS7N+/P8cbQH377bd07tyZli1b8sknn1CyZEnu37/PiRMnuHv3Llu2bAGgefPm1K5dm8qVK2NjY8Pp06fZuXOnzgTKypUrA49+Hry9vSlUqBDNmzfnq6++IjY2lrp16+Lo6Mjt27dZsWIFFStWzNX8ETWTMCCEeGsMGzaMiIgIDhw4wNq1a0lPT6dEiRJ07tyZzz//XOdmRIMGDcLZ2ZkVK1Ywbdo0TE1N0Wg0uXqqZ9myZZk5cybTp09nwoQJ2NnZ0alTJ4oWLcqXX36pV9/FxYWvv/6aiRMncvXqVZydnZk2bZrO+XFTU1OWL1/O3LlziYiIYNOmTVhYWODi4sKAAQOUCYmGhobMnj2bH374gS1btmBgYICvry/Dhw+ndevWL3X8WrZsiYODA/PmzWPhwoWkpaXh6OhIzZo1dW4OVL9+fWbMmMH06dOZMmUKpUqVYty4ccpNhx731VdfkZGRwZo1azA2NsbPz49hw4bRokULnXrlypVj/fr1hIeHs3HjRmJiYihatCiVKlWiX79+Sr2AgAD27t3L4cOHSUtLo0SJEgQHB9OzZ0+lTpMmTQgICOCXX35hy5YtaLVamjdvzscff8xPP/3EqlWriIuLw97enqZNmzJgwAC9q0qELtU9tfD6rWgWrT6s6psO2Vib0aOTF6WcihZ0V0Q+k6cWvn7ZdyCcO3duQXfllRs+fLjOHQhFwcuv33mJSkIIIYTKSRgQQgghVE7CgBBCCKFyMoFQCCFegprOn48fP76guyBeERkZEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDm5z4AQKhITm0RcQspr366VhQk21mZ5Xm/Lli0sW7aMq1evotVqcXR0pHr16gwePBhbW9tX0NM3T2ZmJosWLWL//v1cunQJrVaLRqNh0KBB1KxZU6duWloa06ZNY8uWLSQmJuLh4cHXX3+tPNo52+XLlxkzZgzHjx/H3NycVq1aERwcjLGx8evcNfEGkTAghIrEJaSwfttRYuNfXyCwtjShXYsaeQ4D8+fPZ8qUKQQGBjJw4EC0Wi0XL15k69at3Lt3TzVhICUlhXnz5tGmTRt69+6NoaEhP/30E926dWPhwoV4enoqdceMGcP27dsZPnw4jo6OzJkzh8DAQH755RflqYixsbF0794dFxcXwsLCiIyMZPz48aSkpCiPMBbqI2FACJWJjU95K57auXz5ctq0acPw4cOVMh8fH3r16kVWVlYB9uz1MjExYffu3VhbWytlXl5etGjRgqVLlyph4O7du6xbt45vv/2W9u3bA+Dm5kbDhg1Zs2YNvXv3BmDNmjUkJiYSHh6OjY0N8Gj0YdSoUQQFBeHo6Ph6d1C8EWTOgBDijRQXF4eDg0OOyx5/Nr1Go2HhwoU6y5csWYJGo9Frb/To0dSvX58qVarg6+vLlClTdOrs378ff39/qlatSq1atQgICODs2bM6bXz33Xd4e3tTpUoV2rZty6FDh3TaOHr0KF26dKFGjRp4eHjQsmVLNm7cmOvlTypUqJBOEMgu02g03Lt3Tyk7dOgQWVlZ+Pn5KWU2NjZ4eXlx8OBBpezgwYN4enoqQQCgadOmZGVlcfjw4af2Q7zbZGRACPFGqly5MmvWrMHZ2ZkGDRpgb2//wm2lpaXRvXt3bt26Rb9+/XB1deXu3bscPXpUqbN9+3YGDx5Mo0aNmDJlCkZGRhw7dozIyEgqVapEWloan376KQ8ePCA4OBhHR0e2bNlCUFAQGzZsQKPRkJCQQFBQEDVq1GDq1KkYGxtz6dIl4uLiAJ67PLcyMjI4efIkNWrUUMquXLmCra2tXnB4//33WbdunU69du3a6dSxsrLC3t6eK1eu5Kkf4t0hYUAI8Ub69ttv6d+/P1999RUAzs7ONGzYkMDAQJydnfPU1qZNmzh79ixr1qzBw8NDKW/Tpg0AWq2WCRMm4OXlxaxZs5TlPj4+yv9v3bqV8+fPs3nzZsqVKwdAvXr1+O+///jxxx+ZMWMGV69eJT4+nsGDBysjE4+f03/e8txasGABkZGRBAYGKmVxcXHKvIDHWVlZERsbq1PPyspKr561tbVOPaEucppACPFGcnV1Zdu2bcybN49u3bphaWnJ8uXL+fjjjzl37lye2vrjjz94//33dYLA465cucLdu3f1vjE/7vDhw7i6uuLi4kJGRoby74MPPuD06dMAlCpVCgsLC7777ju2b99OdHS0ThvPW54bhw8fJiwsjL59+1KlSpU8ry9ETiQMCCHeWMbGxvj4+DBy5Eg2bdrEggULSElJ0fn2nhsxMTFPnX+QvRx4Zp2HDx9y9uxZKleurPNv9uzZ3L17F3j07Xrx4sWYm5szbNgwvLy8CAgI4N9//83V8uc5c+YMAwYMoEWLFvTv319nmZWVFQkJCXrrxMXF6Zw6sLKyIj4+Xq9ebGys3ikGoR5ymkAI8daoV68eFSpU4PLly0qZsbEx6enpOvWePAdvY2PzzA/c7Ml0j0/Ie5K1tTUajYaxY8c+s4/u7u5KaDly5AgTJkygX79+7N69O1fLn+a///6jd+/eeHh4MGbMGL3lZcuW5f79+3of6leuXNG5z0DZsmX15gbEx8cTFRWldz8CoR4yMiCEeCPdv39frywlJYU7d+5gZ2enlBUrVkwnHAD8/vvvOq8/+OADLl++zMmTJ3PcVtmyZSlWrBgbNmx4an8++OADbty4gYODA25ubnr/nmRiYoKPjw+dOnXi5s2bpKam5mn54+7du0ePHj0oXrw4M2fOxMjISK+Ot7c3hoaG7Nq1SymLjY3l0KFD1K9fXymrX78+v//+u05gioiIwNDQEC8vr6f2QbzbZGRACJWxtjR5K7bXsmVLGjZsiLe3Nw4ODkRGRrJixQoePnxI9+7dlXofffQRS5cuxc3NjTJlyrBlyxYiIyN12mrVqhWrVq3is88+o3///pQvX57IyEj++ecfRo8ejYGBAV988QWDBw9mwIABtGrVCmNjY06cOKFcq9+6dWvWrFlDt27d6NGjBy4uLsTHx3P27FnS09MJDQ1l//79rFu3jg8//JASJUpw//59VqxYQfXq1SlSpMhzl+ckJSWF3r178/DhQ0aOHMnFixeVZcbGxlSqVAl4FIrat2/PxIkTMTQ0xNHRkblz52JpaYm/v7+yjr+/P8uXL6dfv34EBQURGRnJxIkT8ff3l3sMqJiEASFUxMri0d0AC2K7edW/f3/27dvH+PHjiY6O5r333kOj0bBkyRLq1q2r1Ovbty8PHjxg1qxZGBgY0LFjR7p168b48eOVOsbGxixZsoRp06Yxd+5cYmJiKFasGM2bN1fqNGvWDBMTE+bMmcPgwYMpUqQIlSpVonHjxkoby5YtIywsjDlz5hAVFYWNjQ2VKlWic+fOwKMJgoaGhkyfPp0HDx5gY2ODt7c3gwcPztXynNy/f5/z588D8Pnnn+ssc3JyYu/evcrrr776CnNzc6ZMmUJiYiLVq1dn8eLFOlcZWFtbs3TpUkaPHk2/fv0wNzenffv2hISE5Pk9Eu8OA61Wqy3oTrxO129Fs2j14bfiDmyvio21GT06eVHKqWhBd0Xks5SUFK5evUqZMmUwMXm9IwBCiNcvv37nZc6AEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihcnI7YiHEGyksLIzw8PAcl4WGhvLZZ5+95h49nUaj0Suzs7Pj8OHDOmWXL19mzJgxHD9+HHNzc1q1akVwcDDGxsY69X7++WcWLFjA7du3KVOmDCEhITRs2PCV7oNQNwkDQqhIakIsaYn6z7x/1YzNLShiYf38ik8wMTFh6dKleuXFixfPj27lq4CAAFq0aKG8fvLJgrGxsXTv3h0XFxfCwsKIjIxk/PjxpKSk8M033yj1fvnlF77++mv69OlD3bp12b59O/3792flypVUq1btde2OUBkJA0KoSFpiAlf2byEtMf61bdPY3JKyDT5+oTBgaGiY5w/AlJSUHO/RnpaWRuHChTE0fLGzo09rN1vx4sWf2dc1a9aQmJhIeHg4NjY2AGRmZjJq1CiCgoKUJwbOnDmT5s2bExwcDEDdunW5cOECs2bNYv78+S/UdyGeR+YMCKEyaYnxpCXEvr5/rzh4aDQa5s2bx6RJk/Dy8sLT0xMAX19fvv/+e+bPn0/Dhg1xd3cnJiaGrKwsfvzxR3x9falSpQp+fn6sWbNGp82wsDA8PDw4deoUHTt2xM3NjZUrV75UPw8ePIinp6cSBACaNm1KVlaWcjrhxo0bXLt2jaZNm+qs26xZM/744w/S0tJeqg9CPI2MDAgh3mgZGRl6ZYUL6/7pWrZsGVWrVmXs2LE69Xft2kXp0qUZOXIkhoaGmJmZMXHiRJYtW8bnn3+Oh4cH+/fv59tvvyUjI4OuXbsq66anpxMaGkpgYCAhISE6H+I5mTdvHlOnTsXU1BRvb2+GDRtGiRIllOVXrlyhXbt2OutYWVlhb2/PlStXlDoAZcqU0an3/vvvk56ezo0bN3j//fef2Q8hXoSEASHEGyspKYnKlSvrla9cuZKaNWsqr62trQkPD8fAwECnXnp6OvPnz8fMzAyA6OhoVqxYQc+ePRkwYAAA3t7ePHz4kFmzZtGpUycKFSqkrBsSEkKzZs2e28/WrVvToEED7OzsuHDhArNnz6Zz585s3rwZa+tHp0fi4uKwsrLSW9fa2prY2FgA5b9P1st+nb1ciPwmYUAI8cYyMTFhxYoVeuVly5bVeV2/fn29IABQp04dJQgAnDp1ivT0dPz8/HTqNW3alG3btnHt2jWdb94+Pj656ueECROU/69VqxY1atSgbdu2/PTTT/Tu3TtXbQhRkCQMCCHeWIaGhri5uT23nq2tba7Ks79Z29nZ6ZRnv46JiVHKTE1NMTc3z0t3FRUqVKBMmTKcOXNGKbOysiI+Xn/+RGxsrDJ6kP3f+Ph47O3tlTpxcXE6y4XIbzKBUAjx1stpVCCn8uzz/g8ePNApv3//vs7yZ7X5osqWLavMCcgWHx9PVFSUMtKR/d8n6125cgUjIyNKliyZr30SIpuEASGEari5uWFkZERERIRO+Y4dO7C1tcXFxSVftnPu3DmuXr2qM6pRv359fv/9d+VbPkBERASGhoZ4eXkBULJkSVxcXPT6t337djw9PfVuTiREfpHTBEKojLG55VuzvaysLE6cOKFXbmtr+0LfkosWLUrXrl1ZuHAhxsbGVKtWjQMHDrBt2za+/vprZfJgXixcuJDr169Tp04dihYtysWLF5kzZw7FihWjQ4cOSj1/f3+WL19Ov379CAoKIjIykokTJ+Lv76/cYwBgwIABDBkyhFKlSlGnTh22b9/OqVOncpw7IUR+kTAghIoYm1tQtsHHBbLdF5GSkkLHjh31ytu3b8/YsWNfqM1hw4ZhaWnJunXrmDNnDk5OTowaNQp/f/8Xaq9MmTLs2rWLHTt2kJiYyHvvvYePjw/BwcE6VwVYW1uzdOlSRo8eTb9+/TA3N6d9+/aEhITotNeiRQuSk5OZP38+8+bNo0yZMoSHh+Ph4fFC/RMiNwy0Wq22oDvxOl2/Fc2i1YeJiU0q6K4UGBtrM3p08qKUU9GC7orIZykpKVy9epUyZco88255Qoh3Q379zsucASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ88mEEJFYpNiiU9OeO3btTS1wNrMOk/rhIWFER4enuOy0NBQPvvss/zoWr4bO3Ysy5Yto0uXLnzzzTc6yy5fvsyYMWM4fvw45ubmtGrViuDgYJ2nEW7fvp0dO3Zw8uRJIiMjGTZsGD179nzduyFURsKAECoSn5zAlr+2EZcU/9q2aWVmyce1W+Q5DACYmJiwdOlSvfLixYvnR9fy3b///sv69euxsNB/MFNsbCzdu3fHxcWFsLAwIiMjGT9+PCkpKTqhISIighs3btCgQQPWrl37OrsvVEzCgBAqE5cUT2xSbEF3I1cMDQ2pVq1antZJSUnJ8YEtaWlpFC5cGEPDFzs7+rR2Hzd69GgCAwPZtGmT3rI1a9aQmJhIeHg4NjY2AGRmZjJq1CiCgoKUxxhPnz5d6aOEAfG6yJwBIcRbTaPRMG/ePCZNmoSXlxeenp4A+Pr68v333zN//nwaNmyIu7s7MTExZGVl8eOPP+Lr60uVKlXw8/NjzZo1Om2GhYXh4eHBqVOn6NixI25ubqxcufKZ/diyZQs3b96kd+/eOS4/ePAgnp6eShAAaNq0KVlZWRw+fFgpe9GwIsTLkJEBIcQbLSMjQ6+scGHdP13Lli2jatWqjB07Vqf+rl27KF26NCNHjsTQ0BAzMzMmTpzIsmXL+Pzzz/Hw8GD//v18++23ZGRk0LVrV2Xd9PR0QkNDCQwMJCQkROdD/EkJCQlMnDiRL7/8ElNT0xzrXLlyhXbt2umUWVlZYW9vz5UrV3JzKIR4ZSQMCCHeWElJSVSuXFmvfOXKldSsWVN5bW1tTXh4OAYGBjr10tPTmT9/PmZmZgBER0ezYsUKevbsyYABAwDw9vbm4cOHzJo1i06dOlGoUCFl3ZCQEJo1a/bcfoaHh1O6dOln1o2Li8PKykqv3NramtjYt+O0jXh3SRgQQryxTExMWLFihV552bJldV7Xr19fLwgA1KlTRwkCAKdOnSI9PR0/Pz+dek2bNmXbtm1cu3aN999/Xyn38fF5bh8vXrzIypUr+emnn55bV4g3lYQBIcQby9DQEDc3t+fWs7W1zVV59jdwOzs7nfLs1zExMUqZqakp5ubmz932+PHj8fPzw8nJibi4OACysrJIT08nLi4OCwsLDA0NsbKyIj5e/yqO2NhYrK3zfqWFEPlJZqoIId56OY0K5FSefd7/wYMHOuX379/XWf6sNp909epVtmzZQq1atZR/d+7c4aeffqJWrVpcvXoVeDSa8eTcgPj4eKKiovRGOoR43WRkQAihGm5ubhgZGREREUGlSpWU8h07dmBra4uLi0ue25w6dSqpqak6ZYMHD6ZatWp069aNEiVKAI9OZcyZM0dn7kBERASGhoZ4eXm9+E4JkQ8kDAihMlZmlm/N9rKysjhx4oReua2tLSVLlsxze0WLFqVr164sXLgQY2NjqlWrxoEDB9i2bRtff/21MnkwL3K6D0KRIkVwdHSkTp06Spm/vz/Lly+nX79+BAUFERkZycSJE/H391fuMQBw6dIlLl26pLy+cOECERERmJqa5moOgxAvQsKAECpiaWrBx7VbFMh2X0RKSgodO3bUK2/fvj1jx459oTaHDRuGpaUl69atY86cOTg5OTFq1Cj8/f1fqL3csra2ZunSpYwePZp+/fphbm5O+/btCQkJ0am3Y8cOndswb9q0iU2bNuHk5MTevXtfaR+FehlotVptQXfidbp+K5pFqw8TE5tU0F0pMDbWZvTo5EUpp6IF3RWRz1JSUrh69SplypR57t3yhBBvv/z6nZcJhEIIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTp5NIISKJMfGkBIX/9q3a2Jliam1Ta7razSa59YZN24cGzduxMzMjLlz575E7wpGXFwcS5cupWnTppQrVy5X66xcuZINGzawfv16ADZs2MCIESP4448/KFr0zbq9uK+vLw0aNOCbb755LdsLCAh4Y38Wjhw5wvHjx+nTp49O+ezZs/nrr79YvHhxAfXs/0gYEEJFUuLiObVxHSnxca9tmyaWVri3aZ+nMLB27Vqd1x07diQgIIAWLf7vIUulSpXC3d0dQ8O3c4AzLi6O8PBwypcvn6swkJyczOzZs/n6669fQ+/ePt9+++0b+7Pw119/sWjRIr0w0KVLFxYsWMCff/5J3bp1C6h3j0gYEEJlUuLjSI6JKehuPFNOjwUuXry4Xvmb9m34Vdq+fTvp6ek0atSooLvy2qSkpOT64Tu5HV15k1hZWdGkSROWLVtW4GHgzYxRQgiRCwEBAQQFBSmvw8LC8PDw4OzZs3Ts2BF3d3fatGnD2bNnSU1N5dtvv6VWrVrUr1+fJUuW6LV3/PhxunXrRrVq1ahRowahoaE8ePBAp05MTAwjRoygTp06uLu74+/vz99//61Tx9fXl++//16nbPfu3Wg0Gm7evMnNmzeVD/VBgwah0WiUZU+zadMmGjVqROHC+t/hrl+/Trdu3ahatSq+vr6sW7dOWbZ37140Gg3Xrl3TWSc2NhZ3d3dWrlz51G1mH99t27bRpEkTqlatSp8+fYiNjeXWrVv07NkTDw8PmjdvzpEjR57aTrbjx4/To0cPqlevjoeHBx06dODw4cMA3Lx5E41Gw4YNG/jqq6+oU6cOHTp0AHJ3zJ/8Wbh79y6DBg3igw8+wM3NDV9fX3744YdcL798+TIhISH4+PhQtWpVmjVrxqJFi8jKylLqZPd58+bNfP/999SqVQtvb28mTJhARkYG8OhnMjw8nKSkJOV9DggIUNrw8/PjwIEDREdHP/f4vUoyMiCEeKekp6fzxRdfEBgYiJ2dHZMnT6Z///5Ur14dW1tbpk+fzp49exg3bhzu7u5Ur14dePRBFRAQgI+PD9OmTSM5OZnp06fTt29f5bRFZmYmvXv35saNGwwZMgQ7OzuWL1/Op59+ypo1a6hSpUqu+ujg4EB4eDj9+/dn8ODB1KlTRynPSUpKCsePH6dVq1Y5Lh88eDAdO3akd+/ebN++nZEjR+Lg4ED9+vXx8fHB0dGR9evXExoaqqyzbds2AFq2bPnMvp49e5aHDx8ybNgwEhISGDNmDF9//TW3bt2idevWfPrpp8ydO5cBAwawb98+zM3Nc2zn6NGjdO/enWrVqjFmzBisrKz43//+x+3bt3XqTZ06FR8fH6ZMmUJWVtYLH/Nhw4Zx7949vvrqK2xtbblz5w7/+9//cr383r17lClThpYtW2Jubs65c+cICwsjKSmJ/v3762xr+vTpNGrUiOnTp3P8+HHCwsIoVaoUnTp1okOHDty9e5dt27axdOlSACwsLJR1PTw8yMzM5K+//sLPz++Z78WrJGFACPFOSU9PZ8iQIfj4+ACQlZVFnz59qFq1KiNGjACgbt26REREEBERoYSBKVOmUKVKFcLDwzEwMADA1dWVFi1acODAAXx8fNi/fz+nTp1iwYIF1KtXDwBvb2+aNGnC3LlzCQsLy1UfjY2NqVixIgClS5fO8bTI486dO0d6evpTJ1a2atVK+VZcr149bty4waxZs6hfvz6FChWibdu2rF+/nuDgYAoVKgTA+vXrady4MVZWVs/cdkJCAnPmzFFOyfz7778sWrSI7777jk6dOgGPQkzLli35448/+PDDD3NsZ9KkSZQuXZqlS5cqffD29tarV6FCBcaOHau83rNnzwsd89OnTzN48GCaNWumlLVu3TrXyz09PfH09ARAq9VSo0YNUlJSWLFihV4YcHd356uvvgLAy8uLI0eOsHPnTjp16kSxYsUoVqwYhoaGOb7PVlZWlChRgpMnTxZoGJDTBEKId4qhoaHyRxzAxcUFgA8++EApK1SoEKVKleLu3bvAo8l5x44dw8/Pj8zMTDIyMsjIyMDFxYXixYtz+vRpAP755x8sLCyUDyUAIyMjGjduzNGjR1/ZPkVFRQFPnyPRuHFjnddNmjThzJkzZGZmAtC+fXuioqL47bffADh//jxnzpyhffv2z912hQoVdLab0/HMLss+nk9KTk7m5MmTtG7dWgkCT9OgQQOd1y96zCtVqsSiRYtYtWoV//33X56Xp6amMnPmTBo3boybmxuVK1dm2rRpREVFkZiYqFP3yVDz/vvvP/VY5MTGxkZ5jwuKhAEhxDvFxMQEY2Nj5bWRkREAlpaWOvWMjIxITU0FHs3sz8zMZNy4cVSuXFnn3+3bt7lz545Sz9bWVm+bdnZ2xMbGvqpdUvr5+H497sk+2dnZkZ6ezsOHDwFwdnbGy8tLmUuwfv16nJ2dczVp7cmRg5yOZ3a/svv5pLi4OLKysp56GuRZ+/Kix3zatGnUrVuX6dOn06RJE/z8/Ni1a1eul0+aNImFCxfSoUMH5s2bx7p16/j8889z3M+cfrbS0tKeu6/ZjI2Nn3rsXhc5TSCEUD1LS0sMDAwICgrKcZj7vffeA8Da2lpvQiHA/fv3sba2Vl4bGxuTnp6uU+dlwkJ223Fxcdjb2+stf/DgAY6Ojjr9MTIyUvoN0KFDB4YMGUJkZCRbt24lICBAOR3yqllaWmJoaMi9e/eeW/fJPuX2mD/JwcGBcePGkZWVxf/+9z9mz55NSEgIERERlCxZ8rnLIyIi6NixI5999pnS5oEDB/Kw17kXHx9P+fLlX0nbuSUjA0II1TMzM6NatWpcuXIFNzc3vX/Ozs4A1KhRg4SEBA4dOqSsm5GRwe7du6lRo4ZSVqxYMS5fvqyzjexZ89myv2Hn5hthmTJlAJ56tcGvv/6q83rXrl1UrlxZZ0i+UaNGWFlZERoaSmxsLG3btn3udvNL9vHdvHmzcuoit3J7zJ/G0NAQd3d3goODycjI0Dsl8LTlqampynsEjyaP/vLLL3nqe7ZnjRRkZWVx+/Zt5T0uKDIyIITKmFg+e8LY2769FzVs2DC6d+9OcHAwzZs3x8rKirt37/L777/Ttm1b6tSpQ4MGDXB3d2fo0KGEhoYqM9vv3bvHzJkzlbY++ugjvvvuO8LDw/Hw8ODAgQOcOHFCZ3v29vZYWVnxyy+/4OzsjLGxMRqNJsdTASVLlsTe3p4zZ84oEyMft3nzZkxMTKhUqRLbt2/n77//Zt68eTp1jIyMaN26NQsXLsTb25vixYvnz4HLpdDQUAIDAwkMDKRz585YW1tz5swZ3nvvvWfOXcjtMX9cfHw8PXv2pFWrVpQpU4b09HSWL1+OlZUVlSpVeu5yeDQn4ueff6ZcuXK89957rFq1Kk9D/497//33ycjIYOnSpXh4eGBhYUHZsmUBuHr1KklJSdSsWfOF2s4vEgaEUBETK0vc2zx/0tir2O6brnr16qxatYqwsDBGjBhBeno6xYoVo27dupQuXRp4NPFw3rx5TJw4kUmTJpGUlETlypVZtGiRziVuHTp04Pr166xevZolS5bQrFkzBg8erHNpn6GhIePGjWPq1KkEBgaSlpbGnj17lFGIJ/n5+XHw4EH69u2rt2zKlClMnTqVWbNmYWtry+jRo3MMDY0bN2bhwoW0a9fuZQ9XntWsWZNly5Yxffp0RowYgaGhIeXLlyc4OPiZ6+X2mD+uSJEiuLq6snz5cu7cuYOJiQlVqlRh4cKFFC1alLS0tGcuB/j666/59ttvGT16NKamprRp04bGjRsrVw3kRcOGDencuTPz5s3jwYMH1KpVi+XLlwNw8OBBnJyccHNzy3O7+clAq9VqC7QHr9n1W9EsWn2YmNikgu5KgbGxNqNHJy9KOann7m1qkZKSwtWrVylTpkyu79wm3g7nz5+nTZs27N69GycnpxdqY8aMGaxatYrffvvtqZMRxevVrl07GjZsqHe5Ym7l1++8zBkQQoi3QIUKFfD19WXZsmV5XvfKlSvs2bOHFStW4O/vL0HgDfH3339z48YNunXrVtBdkdMEQgjxthg6dCh79uzJ83rffvstJ06coF69ejq37BUFKyEhgQkTJjz3xk+vg4QBIYR4S7i4uNCzZ888r5d9flq8WRo2bFjQXVDIaQIhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXJyaaEQ4o0UFhZGeHh4jstCQ0N1niZX0DQajV6ZnZ2d3sOJLl++zJgxYzh+/Djm5ua0atWK4OBgnZsAbd++nR07dnDy5EkiIyMZNmzYC11OKEReSBgQQkUSYpNITkh+7ds1tTDFwtosz+uZmJiwdOlSvfLX/ZCd3AgICKBFixbK68efeAePHmHcvXt3XFxcCAsLIzIykvHjx5OSksI333yj1IuIiODGjRs0aNCAtWvXvrb+C3WTMCCEiiQnJHPol79Jeo2BwMzCFO/mtV4oDBgaGlKtWrU8rZOSkpLjPdrT0tIoXLgwhoYvdnb0ae1mK168+DP7umbNGhITEwkPD8fGxgZ49FjcUaNGERQUhKOjIwDTp09X+ihhQLwuMmdACJVJSkgmMTbptf171cFDo9Ewb948Jk2ahJeXF56engD4+vry/fffM3/+fBo2bIi7uzsxMTFkZWXx448/4uvrS5UqVfDz82PNmjU6bYaFheHh4cGpU6fo2LEjbm5urFy58qX6efDgQTw9PZUgANC0aVOysrJ0Tie8aFgR4mXIyIAQ4o2WkZGhV1a4sO6frmXLllG1alXGjh2rU3/Xrl2ULl2akSNHYmhoiJmZGRMnTmTZsmV8/vnneHh4sH//fr799lsyMjLo2rWrsm56ejqhoaEEBgYSEhKi8yGek3nz5jF16lRMTU3x9vZm2LBhlChRQll+5coVvUcHW1lZYW9vz5UrV/JySITIdxIGhBBvrOxn1z9p5cqV1KxZU3ltbW1NeHg4BgYGOvXS09OZP38+ZmaPTlFER0ezYsUKevbsyYABAwDw9vbm4cOHzJo1i06dOlGoUCFl3ZCQEJo1a/bcfrZu3ZoGDRpgZ2fHhQsXmD17Np07d2bz5s1YW1sDEBcXl+MDaaytrYmNjc3lERHi1ZAwIIR4Y5mYmLBixQq98rJly+q8rl+/vl4QAKhTp44SBABOnTpFeno6fn5+OvWaNm3Ktm3buHbtGu+//75S7uPjk6t+TpgwQfn/WrVqUaNGDdq2bctPP/1E7969c9WGEAVJwoAQ4o1laGiIm5vbc+vZ2trmqjz7G7idnZ1OefbrmJgYpczU1BRzc/O8dFdRoUIFypQpw5kzZ5QyKysr4uPj9erGxsYqowdCFBSZqSKEeOvlNCqQU3n2ef8HDx7olN+/f19n+bPafFFly5bVmxsQHx9PVFSU3kiHEK+bhAEhhGq4ublhZGRERESETvmOHTuwtbXFxcUlX7Zz7tw5rl69qjOqUb9+fX7//Xfi4uKUsoiICAwNDfHy8sqX7QrxouQ0gRAqY2Zh+tZsLysrixMnTuiV29raUrJkyTy3V7RoUbp27crChQsxNjamWrVqHDhwgG3btvH1118rkwfzYuHChVy/fp06depQtGhRLl68yJw5cyhWrBgdOnRQ6vn7+7N8+XL69etHUFAQkZGRTJw4EX9/f+UeAwCXLl3i0qVLyusLFy4QERGBqalprucwCJFXEgaEUBHT/38DoILY7otISUmhY8eOeuXt27dn7NixL9TmsGHDsLS0ZN26dcyZMwcnJydGjRqFv7//C7VXpkwZdu3axY4dO0hMTOS9997Dx8eH4OBgnasHrK2tWbp0KaNHj6Zfv36Ym5vTvn17QkJCdNrbsWOHzm2YN23axKZNm3BycmLv3r0v1EchnsdAq9VqC7oTr9P1W9EsWn2YmNikgu5KgbGxNqNHJy9KORUt6K6IfJaSksLVq1cpU6bMM++WJ4R4N+TX77zMGRBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihcvJsAiFUJCEulqSEuOdXzGdmFlZYWFnnur5Go3lunXHjxrFx40bMzMyYO3fuy3SvQMTFxbF06VKaNm1KuXLlcrXOypUr2bBhA+vXrwdgw4YNjBgxgj/++IOiRfP/9uIBAQFv7PHVaDQMGzaMnj17vpL2Dx8+zPjx47l69SomJib8888/r2Q7T/PVV18BMGbMmNeyPQkDQqhIUkIcv0VsfK2BwMzCinp+bfIUBtauXavzumPHjgQEBNCiRQulrFSpUri7u2No+HYOcMbFxREeHk758uVzFQaSk5OZPXs2X3/99Wvo3Ztv7dq1lChR4pW1P2LECDQaDd9++y1FihR5Zdt5mt69e9O8eXN69eqVb4/WfhYJA0KoTFJCHAlxMQXdjWeqVq2aXlnx4sX1yl/Ft+E31fbt20lPT6dRo0YF3ZU3Qk4/I/klMTGRyMhIBg4cSM2aNV/Zdp6ldOnSVK9enZUrVzJy5MhXvr23M1ILIQSPhrGDgoKU12FhYXh4eHD27Fk6duyIu7s7bdq04ezZs6SmpvLtt99Sq1Yt6tevz5IlS/TaO378ON26daNatWrUqFGD0NBQHjx4oFMnJiaGESNGUKdOHdzd3fH39+fvv//WqePr68v333+vU7Z79240Gg03b97k5s2byof6oEGD0Gg0yrKn2bRpE40aNaJwYf3vcNevX6dbt25UrVoVX19f1q1bpyzbu3cvGo2Ga9eu6awTGxuLu7s7K1eufOo2s0VERPDRRx/h4eFBt27duH79urLsyJEjaDQaTp8+rbNO3759CQgI0Cn79ddf+eijj3Bzc+OTTz7hzJkz1KxZk7CwMKWOVqslPDwcLy8vPDw8GDhwIL///jsajYYjR44o9TQaDQsXLlReZ/8sPKuvAHfv3iUoKIiqVavi4+PDkiVLGDt2LL6+vsCjUy/Vq1cHYOTIkWg0GoYPHw7AokWLaNeuHTVq1MDT05OgoCCuXr2qd7yOHz9Ojx49qF69Oh4eHnTo0IHDhw8ry9PS0pg6dSoNGzakSpUqNG3alK1bt+q14+fnx9atW8nIyHjKO5N/ZGRACPFOSU9P54svviAwMBA7OzsmT55M//79qV69Ora2tkyfPp09e/Ywbtw43N3dlT/8x48fJyAgAB8fH6ZNm0ZycjLTp0+nb9++ymmLzMxMevfuzY0bNxgyZAh2dnYsX76cTz/9lDVr1lClSpVc9dHBwYHw8HD69+/P4MGDqVOnjlKek5SUFI4fP06rVq1yXD548GA6duxI79692b59OyNHjsTBwYH69evj4+ODo6Mj69evJzQ0VFln27ZtALRs2fKZfT137hzR0dEMGTKEzMxMxo8fz9ChQ/VO5TzP2bNnGTRoEA0bNuTLL7/k1q1bhISEkJaWplNv+fLlhIeH06tXL+rWrcuff/6pnD9/nuf1VavV0rdvX+7fv8+oUaOwtLRk4cKF3L59Wznd1KBBAxYvXsynn37K559/ToMGDZQRqLt379K1a1dKlChBQkICa9aswd/fn507d2JjYwPA0aNH6d69O9WqVWPMmDFYWVnxv//9j9u3byv9HDRoEMeOHaNfv368//77HDhwgKFDh2JlZYWPj49Sr3r16jx8+JBz587h5uaWp+OdVxIGhBDvlPT0dIYMGaL8Uc3KyqJPnz5UrVqVESNGAFC3bl0iIiKIiIhQwsCUKVOoUqUK4eHhGBgYAODq6kqLFi04cOAAPj4+7N+/n1OnTrFgwQLq1asHgLe3N02aNGHu3Lk633CfxdjYmIoVKwKPhoOfN+R97tw50tPTnzqxslWrVsoISb169bhx4wazZs2ifv36FCpUiLZt27J+/XqCg4MpVKgQAOvXr6dx48ZYWVk9c9vx8fFs2rRJ+UBMSkpixIgR3L17l2LFiuVqfwHmzp2Ls7MzYWFhygevubk5w4YNU+pkZmYyb9482rZty5AhQ4BHx/fhw4c6ox0v2teDBw9y5swZVq5cqQz/161bFx8fH+U4FC1aVAl1pUqV0nlvvvzyS52+enl54enpyc6dO+nYsSMAkyZNonTp0ixdulQ51t7e3sp6f/75J3v37mXhwoVKuZeXF1FRUYSFhemEgXLlylGoUCFOnTr1ysOAnCYQQrxTDA0N8fT0VF5nT7764IMPlLJChQpRqlQp7t69CzyanHfs2DH8/PzIzMwkIyODjIwMXFxcKF68uDIE/s8//2BhYaEEAQAjIyMaN27M0aNHX9k+RUVFAU+fI9G4cWOd102aNOHMmTNkZmYC0L59e6Kiovjtt98AOH/+PGfOnKF9+/bP3XaFChV0tps92TH72OXW6dOnadCggc6EzyfnP9y9e5eoqChlyP5p9V60r6dPn8bKykpnHoC5ubnOz8uznDhxgk8//ZQ6depQqVIlqlatSlJSknIKJjk5mZMnT9K6dWslCDzp8OHD2NjYULduXeXnLCMjgw8++IBz584p7xlA4cKFsbS05N69e7nq38uQkQEhxDvFxMQEY2Nj5bWRkREAlpaWOvWMjIxITU0FHs3sz8zMZNy4cYwbN06vzTt37ij1bG1t9Zbb2dkRGxubb/vwpOx+Pr5fj3uyT3Z2dqSnp/Pw4UPs7OxwdnbGy8uLdevW0aBBA9avX4+zszN169Z97rafHDnIPp7ZfcqtqKgovTBjYWGhM1P/aaEnp2P+In29d+9ejoEqNxNRb9++TY8ePahSpQqjRo3CwcEBIyMjgoKCdH6OsrKynnq6B+Dhw4fExMRQuXLlHJdHRUXpjLgYGxvn+Vi/CAkDQgjVs7S0xMDAgKCgID788EO95e+99x4A1tbWehMKAe7fv4+19f9dOmlsbEx6erpOnZcJC9ltx8XFYW9vr7f8wYMHODo66vTHyMhI6TdAhw4dGDJkCJGRkWzdupWAgADldMjLyP4wf3J/4+LidNq3t7cnOjpap05CQoLOB132vj1ZL6dj/iIcHBz02s5pezn57bffSEpKIjw8XAkdGRkZOu+rpaUlhoaGz/wmb21tTdGiRZk3b16Oy58MJvHx8cp8hFdJThMIIVTPzMyMatWqceXKFdzc3PT+OTs7A1CjRg0SEhI4dOiQsm5GRga7d++mRo0aSlmxYsW4fPmyzjYen00OefuGXaZMGYCnXm3w66+/6rzetWsXlStX1hmqbtSoEVZWVoSGhhIbG0vbtm2fu93cyP4W+/j+RkdHc+bMGZ16bm5u7N+/n6ysLKVs9+7dem3Z29uzZ88enfIn670oNzc34uLidK7+SExM5I8//njuuikpKRgYGOhczbFjxw6dmf7ZP0ebN2/WGe5/3AcffEB0dDRGRkY5/qw9PvoTHR1NcnKy8v6/SjIyIITKmFk8e8LY2769FzVs2DC6d+9OcHAwzZs3x8rKirt37/L777/Ttm1b6tSpQ4MGDXB3d2fo0KGEhoYqVxPcu3ePmTNnKm199NFHfPfdd4SHh+Ph4cGBAwc4ceKEzvbs7e2xsrLil19+wdnZGWNjYzQaTY6nAkqWLIm9vT1nzpzRmWCWbfPmzZiYmFCpUiW2b9/O33//rffN08jIiNatWysT14oXL54vx61YsWJUrVqVWbNmYWlpSeHChZk/f77eaZmgoCDat2/PgAED+OSTT7h9+zaLFi2iSJEiyghCoUKF+Oyzz/jhhx+ws7OjTp06HDlyRPmwftkbTNWvX5/KlSsTGhrK4MGDsbKyYsGCBZibmz93lCT7lMqIESPw9/fn4sWLLF68WO/URGhoKIGBgQQGBtK5c2esra05c+YM7733Hu3bt8fLy4uGDRvSq1cvevXqhUajITk5mUuXLvHff/8xduxYpa3suSqPB81XRcKAECqSfTfAgtjum6569eqsWrWKsLAwRowYQXp6OsWKFaNu3bqULl0aePRhNW/ePCZOnMikSZNISkqicuXKLFq0SOeywg4dOnD9+nVWr17NkiVLaNasGYMHD9a5tM/Q0JBx48YxdepUAgMDSUtLY8+ePcooxJP8/Pw4ePAgffv21Vs2ZcoUpk6dyqxZs7C1tWX06NE5hobGjRuzcOFC2rVr97KHS8fkyZP56quvGDFiBHZ2dgQHB/PLL78QHx+v1KlUqRLTp09nypQp9O/fn/LlyzN+/Hi6deumExwCAgKIi4tj1apVLF++HE9PT4YOHUpISIhewMgrAwMDfvzxR7755hu++eYbrKys6NatG1evXuXcuXPPXFej0TBu3DjCw8MJCgqiYsWKzJgxg+DgYJ16NWvWZNmyZUyfPp0RI0ZgaGhI+fLlderNnDmTefPmsXr1am7duoWlpSXly5fXG6357bffqFmzJnZ2di+137lhoNVqta98K2+Q67eiWbT6MDGxSQXdlQJjY21Gj05elHJSz93b1CIlJYWrV69SpkwZTExMCro7Ih+dP3+eNm3asHv3bpycnF6ojRkzZrBq1Sp+++23p05GfJ3++OMPAgMDWb58ObVr135qvenTp7N48WKOHDmS7z/XaWlpNG/enJo1a+Y4ebSgZGRk0KBBA4YMGULr1q2fWi+/fudlZEAIId4CFSpUwNfXl2XLlin3S8itK1eucPXqVVasWEHnzp0LLAh89913eHp6YmNjw6VLl/jxxx+pVKmSzqV+ly9fZsuWLXh4eGBkZMRff/3FwoUL6dSpU74EgbVr15KVlUWZMmWIi4tTvp1PnTr1pdvOT9u2bcPc3FzneRyvkoQBIYR4SwwdOlRvcl1ufPvtt5w4cYJ69erp3L75dYuLi2P06NHExMQo92v44osvdOYCmJiYcPz4cVavXk1iYiKOjo707NmTAQMG5EsfihQpwrx587h16xbwKGTNnTv3ld/UJ68MDAwYO3ZsjreffiXbk9ME6iOnCd5dcppACHXJr995ubRQCCGEUDkJA0K8g1Q24CeEauXX77qEASHeIUZGRhgYGJCYmFjQXRFCvAZJSY9OeWffxOpFyQRCId4hhQoVwtramqioKFJTU7GysqJw4cL5cttZIcSbQ6vVkpSUxL1797CxsXnqg5FyS8KAEO+YYsWKYWpqyr1794iLiyvo7gghXiEbG5s8PUr6aSQMCPGOMTAwwMbGBmtra+VxvEKId4+RkdFLjwhkkzAgxDsq+6Eqr+s6ZSHE20smEAohhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBkaNTp07x/fff07x5c6pVq0aDBg0YNGgQV69efeo66enpNGvWDI1Gw8KFC/WWZ2VlMX/+fHx9fXFzc6Nly5Zs27ZNr95PP/1E165d+eCDD6hSpQq+vr6MGDGCmzdv6tVdtWoVAwcOpEGDBmg0GoYPH/5yOy6EECpUuKA7IN5MCxYs4NixY/j5+aHRaIiKimLlypW0bduWtWvX4urqqrfOihUruHPnzlPbnDZtGvPmzeOTTz7Bzc2NPXv2EBoaioGBAc2bN1fqnT17FmdnZ3x9fbGysuLmzZv8/PPP7Nu3j82bN+Po6KjTz8TERNzc3IiKisrfgyCEECohYUDkKDAwkMmTJ2NsbKyUNWvWjJYtWzJv3jwmT56sU//BgwfMmjWLXr16MXPmTL32IiMjWbx4MV26dOGbb74BoEOHDnTt2pWJEyfi5+dHoUKFAPjuu+/01v/www9p164dmzdv5rPPPlPKly9fTokSJTAwMMDDwyM/dl0IIVRHThOIHFWvXl0nCAC4uLhQvnx5rly5old/8uTJlClTho8//jjH9nbv3k16ejqdO3dWygwMDOjUqRN3797l+PHjz+yPk5MTAHFxcXrlBgYGudonIYQQOZORAZFrWq2W+/fvU758eZ3yU6dOsWnTJlatWvXUD+Zz585hZmbG+++/r1Pu7u6uLK9Zs6bOsocPH5KVlcXt27eZNWsWAJ6envm1O0IIIf4/CQMi17Zs2UJkZCQDBw5UyrRaLaNHj6ZZs2Z4eHjkOMkPICoqCltbW72wYG9vD8C9e/f01qlfvz5paWkA2NjY8NVXX+Hl5ZVfuyOEEOL/kzAgcuXy5ct8//33eHh40KZNG6V8w4YNXLhwIcd5Ao9LSUnRO+0AUKRIEWX5k+bPn09qaipXrlxhy5YtJCcnv+ReCCGEyImEAfFcUVFRBAUFYWlpyYwZM5SJfgkJCUydOpWePXtSvHjxZ7ZhYmKifMt/XGpqqrL8SXXr1gXAx8eHRo0a0aJFC8zMzOjatevL7pIQQojHyARC8Uzx8fH07t2b+Ph4FixYoHNZ38KFC5V7C9y8eZObN29y9+5d4NFEv5s3byoBwN7envv376PVanXaz74c0MHB4Zn9KFWqFJUqVWLr1q35uXtCCCGQkQHxDKmpqfTp04dr166xePFiypUrp7P8zp07xMbG6twjINucOXOYM2cOmzZtomLFilSsWJGff/6Zy5cv67Rz8uRJACpWrPjc/qSkpOQ4uiCEEOLlSBgQOcrMzCQ4OJgTJ07w448/5ngNf0BAAB9++KFO2YMHD/jmm29o27YtjRo1wtnZGYBGjRoxbtw4Vq1apdxnQKvVsmbNGhwdHZX2MzIySExMxNraWqfdU6dOceHCBVq0aPEqdlcIIVRNwoDI0fjx49m7dy8NGzYkJiaGzZs36yxv1aoVlStXpnLlyjrl2VcTlCtXTicoFCtWjG7durFw4UIyMjJwc3Nj9+7d/PPPP0yePFmZh5CUlESDBg1o2rQp5cuXx9TUlAsXLrBhwwYsLS3p27evzvb27t3L+fPngUe3Q/7333/58ccfAfD19aVChQr5e2CEEOIdJGFA5Cj7A3bfvn3s27dPb3mrVq3y3OaQIUOwtrZm7dq1bNiwARcXFyZNmkTLli2VOiYmJrRv354jR46wc+dOUlNTcXBwoHnz5nz++efKSEO2Xbt2sXHjRuX12bNnOXv2LPAogEgYEEKI5zPQPjmj6x13/VY0i1YfJiY2qaC7UmBsrM3o0cmLUk5FC7orQggh3gByNYEQQgihchIGVMpQ7ucvhBDi/5M5AypkamKEWeEM4iNvFXRXCpyxuQVFLKyfX1EIId5hEgZUyNioMJnJCdz4M4K0xPiC7k6BMTa3pGyDjyUMCCFUT8KAiqUlxpOWEFvQ3RBCCFHAZM6AEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuUkDAghhBAqJ2FACCGEUDkJA0IIIYTKSRgQQgghVE7CgBBCCKFyEgaEEEIIlZMwIIQQQqichAEhhBBC5SQMCCGEEConYUAIIYRQOQkDQgghhMpJGBBCCCFUTsKAEEIIoXISBoQQQgiVkzAghBBCqJyEASHy6NSpU3z//fc0b96catWq0aBBAwYNGsTVq1f16mZlZbFq1SpatWqFu7s7derUoVu3bpw/f/6p7W/ZsgWNRoOHh0eOy1esWEHTpk2pUqUK9erVY9y4cSQlJeXb/gkh1KdwQXdAiLfNggULOHbsGH5+fmg0GqKioli5ciVt27Zl7dq1uLq6KnW//PJLtm7dSqtWrejatStJSUmcO3eOBw8e5Nh2YmIikyZNwszMLMflkyZNYsGCBXz00Ud069aNy5cvs2LFCi5dusTChQtfyf4KId59EgaEyKPAwEAmT56MsbGxUtasWTNatmzJvHnzmDx5MgDbt29n48aNhIeH07hx41y1PXv2bMzNzalTpw579uzRWXbv3j2WLFlCq1atmDhxolLu4uLC6NGj2bt3L76+vvmwh0IItZHTBELkUfXq1XWCADz6QC5fvjxXrlxRypYsWYK7uzuNGzcmKyvruUP5165dY8mSJYwYMYLChfVz+okTJ8jIyKB58+Y65c2aNQPgl19+edFdEkKonIQBIfKBVqvl/v37vPfeewAkJCRw6tQp3NzcmDp1KjVq1MDDw4NGjRqxffv2HNv44YcfqFOnDj4+PjkuT0tLA6BIkSI65aampgCcOXMmv3ZHCKEycppAiHywZcsWIiMjGThwIADXr19Hq9Xyyy+/ULhwYYYOHYqlpSXLli1j8ODBWFhYUL9+fWX9/fv3c/jwYTZv3vzUbZQpUwaAY8eOUbduXaX8n3/+ASAyMvJV7JoQQgUkDAjxki5fvsz333+Ph4cHbdq0AVBOCcTExPDTTz9RtWpVAHx9fWnUqBGzZ89WwkBaWhrjxo3D39+fcuXKPXU7lStXpmrVqsyfPx9HR0fq1KnD5cuXGTVqFEZGRqSmpr7iPRVCvKskDAjxEqKioggKCsLS0pIZM2ZQqFAh4P+G8p2dnZUgAGBubk7Dhg3ZunUrGRkZFC5cmCVLlvDw4UMGDBjw3O2FhYURHBzMl19+CUChQoUIDAzk77//zvHSRiGEyA0JA0K8oPj4eHr37k18fDwrV67E0dFRWebg4ACAnZ2d3nq2trakp6eTnJwMPLqCoHPnziQkJJCQkAA8GlnQarXcvHkTU1NTbG1tAXB0dGT16tVcu3aN+/fvU7p0aezt7fH29sbFxeUV77EQ4l0lYUCIF5CamkqfPn24du0aixcv1hved3R0xN7ePsfz+Pfu3aNIkSKYm5tz+/ZtkpKSWLBgAQsWLNCr26hRIxo1asSPP/6oU+7i4qJ8+F+6dImoqCjatm2bfzv4EoYPH87GjRufuvzgwYM4Ojpy6NAhtm/fzqlTp7h8+TLFixdn7969T13v+vXrzJgxg99//53ExESKFStG06ZNCQkJeRW7kW/OnDlDWFgYx44dIzU1lZIlS/LJJ5/QrVs3ANLT05k7dy4bN24kMjISR0dH2rVrx2effaZ3Vcm1a9eYMWMGR48eJTY2luLFi9OiRQt69uypTCQV4kVIGBAijzIzMwkODubEiRP8+OOPT71TYNOmTVm2bBmHDx/Gy8sLgOjoaPbs2UPdunUxNDTE1taWWbNm6a27bNkyTpw4wdSpU7G3t39qX7Kyspg0aRKmpqb4+/vnzw6+pI4dO+Lp6alTptVq+e6773ByclJGULZt28b27dupVKmSMpLyNOfOnSMgIABHR0c+/fRT3nvvPW7fvs3du3df2X7kh0OHDtGnTx8qVapE3759MTMz4/r16zr9Hjp0KBEREbRr144qVapw8uRJZsyYwZ07dxg9erRS786dO3To0AFLS0u6du2KtbU1J06cICwsjDNnzjB79uyC2EXxjpAwIEQejR8/nr1799KwYUNiYmL0rgBo1aoVAEFBQezYsYMBAwbw6aefYmlpyerVq8nIyGDw4MHAo8sCP/zwQ71t7N69m9OnT+stGzNmDGlpaVSoUIGMjAy2bdvGqVOnGD9+PCVKlHhFe5w3Hh4eegHpn3/+ITk5mZYtWyplISEhjB49GiMjI4KCgrh48WKO7WVlZTFs2DDKli3LsmXLMDExeaX9zy8JCQl88cUXNGjQgJkzZ2JoqH8l96lTp9ixYwd9+/Zl0KBBAHTq1In33nuPxYsX06VLFypUqADA5s2biYuLY9WqVZQvXx54FLyysrLYtGkTsbGxWFtbv74dFO8UCQNC5FH2cwX27dvHvn379JZnhwE7OztWr17NhAkTWLJkCRkZGVSrVo1JkyYpf+DzqlKlSixdupStW7diYGCAu7s7S5Ys0bnU8E20bds2DAwMaNGihVL2+ByLZzl06BAXLlxg3rx5mJiYkJycjLGxsTJZ8021detW7t+/T0hICIaGhiQlJWFiYqITCo4ePQqQ442kFi1axI4dO5Sflez5JNnzR7LZ29tjaGiIkZHRq9ydfJeYmMjChQs5efIkp0+fJjY2lnHjxumd7vrpp5/YsmULV65cIS4uDgcHB+rUqUO/fv1wdnYuoN6/eyQMCJFHy5cvz3XdkiVLEh4enudtjB8/nvHjx+uVt23b9o2ZG5Bb6enp7NixAw8Pjxf64/3HH38AYGxsTNu2bTlz5gxGRkY0btyYb7/9Fhsbm3zucf74448/sLCwIDIykr59+3Lt2jXMzMz4+OOP+fLLLylSpMhzbyT1v//9TymrXbs28+fPZ+TIkQwcOBAbGxuOHz/O6tWrCQgIeOrzLN5UDx8+ZNasWZQoUQKNRsNff/2VY72zZ8/i7OyMr68vVlZW3Lx5k59//pl9+/axefPmXIdK8WwSBoQQr9ShQ4eIiYnROUWQF9euXQMgODiYevXqERQUxPnz55k3bx537txh9erVGBgY5GOP88e1a9fIzMykb9++tG/fntDQUP766y+WL19OfHw8U6dO1bmRVMmSJZV1s28kde/ePaWsfv36DBo0iLlz5+pMtOzTp88bP4kyJw4ODhw6dAh7e3tOnz5N+/btc6z33Xff6ZV9+OGHtGvXjs2bN/PZZ5+94p6qg4QBoW5v4IfIu2bbtm0YGRnRtGnTF1o/+wZObm5uykOgPvroI0xNTZkyZQp//PEHH3zwQb71N78kJSWRnJyMv78/X331FQBNmjQhLS2NtWvXMnDgQHx8fHBycmLixImYmppSuXJlTp48ybRp0yhcuDApKSk6bTo5OVGzZk0++ugjbGxs2L9/P3PnzsXe3p6uXbsWxG6+MGNj42dOjn0WJycnAOLi4vKzS6omYUCoViFjE1ILGRL74FZBd6XAWZpaYG2W/5PPEhMT2bNnD97e3spzG/Iqe8Lg4/MNsl9PmTKFY8eOvZFh4Gn9btmyJWvXruXEiRO4uLgwd+5cgoODlZtOGRsbM3ToUObMmaMz9P/LL7/wzTffsHPnTooVKwY8ChdarZbJkyfTvHnzFz7Gb4OHDx+SlZXF7du3lStwnrxqRbw4CQNCtQoZGZGYmsgvJ3cTlxRf0N0pMFZmlnxcu8UrCQO7d+/Wu4ogr7IvO3xy4lz26zf126GDgwMXL17U63fRokUBiI2NBaB8+fJs27aNS5cuERsbS7ly5TAxMWHcuHHUqlVLWW/VqlVUrFhRCQLZfH192bBhA+fOnXsjQ1F+qV+/vjLHwsbGhq+++kq5ZFe8PAkDQvXikuKJTYot6G68k7Zu3YqZmRm+vr4v3EblypUB/QcxZZ9Pz/5wfdNUrlyZw4cPExkZSdmyZZXynPptYGCgXC4IcODAAbKysnQ+3O/fv5/jpYPp6ekAZGRk5Ps+vEnmz59PamoqV65cYcuWLcodPEX+kEcYCyFeiejoaP744w8aN278UnfHa9SoEcbGxmzYsIGsrCyl/OeffwZ4Y78NZ8+RWLdunU75unXrKFy4MLVr185xvZSUFGbMmIG9vb3OJYdlypTh7Nmzes+g+OWXXzA0NESj0eTzHrxZ6tati4+PD59++ikzZswgPDycFStWFHS33hkyMiCEeCW2b99ORkbGU08RnD9/XpkV/99//xEfH6/cdrlChQrKaIK9vT19+vRh5syZ9OrVi0aNGvHvv//y008/0aJFC9zd3V/PDuVRpUqVaNeuHevXryczM5NatWrx119/ERERQVBQkHJJ3KBBg3BwcKBcuXIkJCSwfv16bty4wbx587CwsFDa69mzJwcPHqRLly506dJFmUB48OBBOnTooKpL7EqVKkWlSpXYunXrWzdx8k0lYUAI8Ups3boVW1vbp35zP3v2LDNmzNApy37dpk0bnVMLffv2xdramuXLlzNu3Djs7Ozo06cP/fr1e3U7kA9GjRpFiRIl2LBhA7t376ZEiRKMGDGCwMBApU6VKlXYsGEDa9euxcTEhBo1ajBlyhQqVqyo01atWrVYs2YNYWFhrF69mpiYGJycnAgJCaFXr16vec8KXkpKijKHQLw8CQNCiFdi7dq1z1yelxsoGRgY0LVr17fuW6CRkRH9+/enf//+T63Tu3dvevfunav23N3dmT9/fn51742XkZFBYmKi3lyJU6dOceHCBb0rNcSLkzAghBCiQKxYsYK4uDhlUuW+ffuUhzgFBASg1Wpp0KABTZs2pXz58piamnLhwgU2bNiApaUlffv2Lcjuv1MkDAgh3sg7+Il336JFi7h16//u87Fr1y527doFwMcff4yDgwPt27fnyJEj7Ny5k9TUVBwcHGjevDmff/65PJsgH0kYEELlTIxMMMmAhzduFHRXCpSBoSEGRcxJS80s6K4UOFMLUyysX/2zDh6/rfLTjBw58pX3Q0gYEEL1jAsbkZaQyIVt20iJfzNv4PM6WBd3opRPU47sOU1SgnqvYTezMMW7ea3XEgbEm0OVYcDa8u14HvqrYmnx6AlpxuaWBdyTgmVk9uiyLSszdR8HC1OL51cSqiKnjdTHQKvVagu6E0IIIYQoOHIHQiGEEELlJAwIIYQQKidhQAghhFA5CQNCCCGEykkYEEIIIVROwoAQQgihchIGhBBCCJWTMCCEEEKonIQBIYQQQuX+H4J0u8zLEN0lAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -120,7 +120,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -160,7 +160,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (6, 2)
statisticcroissant_num_fields
strf64
"count"79800.0
"null_count"28249.0
"mean"3.436917
"std"7.870688
"min"0.0
"max"234.0
" + "shape: (6, 2)
statisticcroissant_num_fields
strf64
"count"109167.0
"null_count"41043.0
"mean"7.760431
"std"50.790145
"min"0.0
"max"1761.0
" ], "text/plain": [ "shape: (6, 2)\n", @@ -169,12 +169,12 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪══════════════════════╡\n", - "│ count ┆ 79800.0 │\n", - "│ null_count ┆ 28249.0 │\n", - "│ mean ┆ 3.436917 │\n", - "│ std ┆ 7.870688 │\n", + "│ count ┆ 109167.0 │\n", + "│ null_count ┆ 41043.0 │\n", + "│ mean ┆ 7.760431 │\n", + "│ std ┆ 50.790145 │\n", "│ min ┆ 0.0 │\n", - "│ max ┆ 234.0 │\n", + "│ max ┆ 1761.0 │\n", "└────────────┴──────────────────────┘" ] }, @@ -191,7 +191,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (6, 2)
statisticcroissant_num_file_objects
strf64
"count"79800.0
"null_count"28249.0
"mean"1.0
"std"0.0
"min"1.0
"max"1.0
" + "shape: (6, 2)
statisticcroissant_num_file_objects
strf64
"count"109167.0
"null_count"41043.0
"mean"1.0
"std"0.0
"min"1.0
"max"1.0
" ], "text/plain": [ "shape: (6, 2)\n", @@ -200,8 +200,8 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪════════════════════════════╡\n", - "│ count ┆ 79800.0 │\n", - "│ null_count ┆ 28249.0 │\n", + "│ count ┆ 109167.0 │\n", + "│ null_count ┆ 41043.0 │\n", "│ mean ┆ 1.0 │\n", "│ std ┆ 0.0 │\n", "│ min ┆ 1.0 │\n", @@ -222,7 +222,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (6, 2)
statisticcroissant_num_file_sets
strf64
"count"79800.0
"null_count"28249.0
"mean"1.044148
"std"1.797683
"min"0.0
"max"100.0
" + "shape: (6, 2)
statisticcroissant_num_file_sets
strf64
"count"109167.0
"null_count"41043.0
"mean"1.203899
"std"4.117753
"min"0.0
"max"100.0
" ], "text/plain": [ "shape: (6, 2)\n", @@ -231,10 +231,10 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪═════════════════════════╡\n", - "│ count ┆ 79800.0 │\n", - "│ null_count ┆ 28249.0 │\n", - "│ mean ┆ 1.044148 │\n", - "│ std ┆ 1.797683 │\n", + "│ count ┆ 109167.0 │\n", + "│ null_count ┆ 41043.0 │\n", + "│ mean ┆ 1.203899 │\n", + "│ std ┆ 4.117753 │\n", "│ min ┆ 0.0 │\n", "│ max ┆ 100.0 │\n", "└────────────┴─────────────────────────┘" @@ -253,7 +253,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (6, 2)
statisticcroissant_num_record_sets
strf64
"count"79800.0
"null_count"28249.0
"mean"1.044148
"std"1.797683
"min"0.0
"max"100.0
" + "shape: (6, 2)
statisticcroissant_num_record_sets
strf64
"count"109167.0
"null_count"41043.0
"mean"1.203899
"std"4.117753
"min"0.0
"max"100.0
" ], "text/plain": [ "shape: (6, 2)\n", @@ -262,10 +262,10 @@ "│ --- ┆ --- │\n", "│ str ┆ f64 │\n", "╞════════════╪═══════════════════════════╡\n", - "│ count ┆ 79800.0 │\n", - "│ null_count ┆ 28249.0 │\n", - "│ mean ┆ 1.044148 │\n", - "│ std ┆ 1.797683 │\n", + "│ count ┆ 109167.0 │\n", + "│ null_count ┆ 41043.0 │\n", + "│ mean ┆ 1.203899 │\n", + "│ std ┆ 4.117753 │\n", "│ min ┆ 0.0 │\n", "│ max ┆ 100.0 │\n", "└────────────┴───────────────────────────┘" @@ -305,7 +305,7 @@ { "data": { "text/markdown": [ - "### Status 408" + "### Status 400" ], "text/plain": [ "" @@ -317,559 +317,7 @@ { "data": { "text/markdown": [ - "#### Known errors" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "### Status 501" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "#### Known errors" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "#### Unknown errors" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "### Status 401" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "#### Known errors" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "### Status 500" + "#### Unknown errors" ], "text/plain": [ "" @@ -881,7 +329,7 @@ { "data": { "text/markdown": [ - "#### Known errors" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -893,7 +341,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/sicknd444/LainIwakuraV1@2af0e6bce0f7e231de8be925981684696f411db3 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/sicknd444/LainIwakuraV1@2af0e6bce0f7e231de8be925981684696f411db3 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -905,7 +353,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/Luan1/transcript@11ac2fdce5261338e08c4463f15c7649fe2b35e4 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/Luan1/transcript@11ac2fdce5261338e08c4463f15c7649fe2b35e4 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -917,7 +365,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/janPiljan/Wiki-Vital@690931515362fe9789fd5f09818b1232d195f1e3 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/janPiljan/Wiki-Vital@690931515362fe9789fd5f09818b1232d195f1e3 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -929,7 +377,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/JayWay/A1111SD_JupyterKag_custom-files@04e95312b1d10f7764b2f5d5cdb0d8163ff90767 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/JayWay/A1111SD_JupyterKag_custom-files@04e95312b1d10f7764b2f5d5cdb0d8163ff90767 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -941,7 +389,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/Darkme/SakamataChloe@d8fce702674f6b54a317777eb536e89a4e1f1d19 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/Darkme/SakamataChloe@d8fce702674f6b54a317777eb536e89a4e1f1d19 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -953,7 +401,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/nickthelegend/sadcolab@b70daa58126c4653a8be0a919354eea67571db96 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/nickthelegend/sadcolab@b70daa58126c4653a8be0a919354eea67571db96 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -965,7 +413,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/zhangshuai507653/testdataset@93efd427badcea632c6c6329ca9524ed20695419 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/zhangshuai507653/testdataset@93efd427badcea632c6c6329ca9524ed20695419 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -977,7 +425,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/vanziegler/dpr@9d89fc729093c5f6e39c857209f6a9dbc9af98b9 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/vanziegler/dpr@9d89fc729093c5f6e39c857209f6a9dbc9af98b9 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -989,7 +437,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/lilouuch/whisper_predictions_mgb3@5b5fda6dcabf3e3b609a87a28f4f1da5912a9af8 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/lilouuch/whisper_predictions_mgb3@5b5fda6dcabf3e3b609a87a28f4f1da5912a9af8 doesn't contain any data files\\n\"]}" + "- {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -1001,7 +449,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"The dataset is empty.\",\"cause_exception\":\"EmptyDatasetError\",\"cause_message\":\"The directory at hf://datasets/lilouuch/whisper_predictions@b5710a4d419ed1c95fb0e65b5e9a1690775f19c8 doesn't contain any data files\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1506, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, in get_module\\n patterns = get_data_patterns(base_path, download_config=self.download_config)\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py\\\", line 460, in get_data_patterns\\n raise EmptyDatasetError(f\\\"The directory at {base_path} doesn't contain any data files\\\") from None\\n\",\"datasets.data_files.EmptyDatasetError: The directory at hf://datasets/lilouuch/whisper_predictions@b5710a4d419ed1c95fb0e65b5e9a1690775f19c8 doesn't contain any data files\\n\"]}" + "### Status 401" ], "text/plain": [ "" @@ -1025,7 +473,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/ACCA225/starryfrp/starryfrp.py or any data file in the same directory. Couldn't find 'ACCA225/starryfrp' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in ACCA225/starryfrp. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/ACCA225/starryfrp/starryfrp.py or any data file in the same directory. Couldn't find 'ACCA225/starryfrp' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in ACCA225/starryfrp. \\n\"]}" + "- {\"error\":\"Invalid username or password.\"}" ], "text/plain": [ "" @@ -1037,7 +485,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/safgasgfsa/Hitler-Voice/Hitler-Voice.py or any data file in the same directory. Couldn't find 'safgasgfsa/Hitler-Voice' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in safgasgfsa/Hitler-Voice. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/safgasgfsa/Hitler-Voice/Hitler-Voice.py or any data file in the same directory. Couldn't find 'safgasgfsa/Hitler-Voice' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in safgasgfsa/Hitler-Voice. \\n\"]}" + "- {\"error\":\"Invalid username or password.\"}" ], "text/plain": [ "" @@ -1049,7 +497,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/plusbey/rellaria/rellaria.py or any data file in the same directory. Couldn't find 'plusbey/rellaria' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in plusbey/rellaria. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/plusbey/rellaria/rellaria.py or any data file in the same directory. Couldn't find 'plusbey/rellaria' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in plusbey/rellaria. \\n\"]}" + "- {\"error\":\"Access to dataset oscar-corpus/colossal-oscar-1.0 is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1061,7 +509,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/xieyizheng/cv2/cv2.py or any data file in the same directory. Couldn't find 'xieyizheng/cv2' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in xieyizheng/cv2. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/xieyizheng/cv2/cv2.py or any data file in the same directory. Couldn't find 'xieyizheng/cv2' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in xieyizheng/cv2. \\n\"]}" + "- {\"error\":\"Access to dataset indra-inc/docvqa_en_full_train_valid_processed_gtparse is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1073,7 +521,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/LongshenOu/lyric-trans-en2zh-data/lyric-trans-en2zh-data.py or any data file in the same directory. Couldn't find 'LongshenOu/lyric-trans-en2zh-data' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in LongshenOu/lyric-trans-en2zh-data. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/LongshenOu/lyric-trans-en2zh-data/lyric-trans-en2zh-data.py or any data file in the same directory. Couldn't find 'LongshenOu/lyric-trans-en2zh-data' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in LongshenOu/lyric-trans-en2zh-data. \\n\"]}" + "- {\"error\":\"Access to dataset AlekseyKorshuk/crowdsource-v2.0 is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1085,7 +533,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/navagg/PyVHR/PyVHR.py or any data file in the same directory. Couldn't find 'navagg/PyVHR' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in navagg/PyVHR. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/navagg/PyVHR/PyVHR.py or any data file in the same directory. Couldn't find 'navagg/PyVHR' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in navagg/PyVHR. \\n\"]}" + "- {\"error\":\"Access to dataset AlekseyKorshuk/crowdsource-v2.0-prompts is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1097,7 +545,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/shinnosuke7788/rvcbyshinno/rvcbyshinno.py or any data file in the same directory. Couldn't find 'shinnosuke7788/rvcbyshinno' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in shinnosuke7788/rvcbyshinno. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/shinnosuke7788/rvcbyshinno/rvcbyshinno.py or any data file in the same directory. Couldn't find 'shinnosuke7788/rvcbyshinno' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in shinnosuke7788/rvcbyshinno. \\n\"]}" + "- {\"error\":\"Access to dataset yeye776/autotrain-data-brokarry_intent_poc is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1109,7 +557,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/dangvinh77/data_ticket/data_ticket.py or any data file in the same directory. Couldn't find 'dangvinh77/data_ticket' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in dangvinh77/data_ticket. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/dangvinh77/data_ticket/data_ticket.py or any data file in the same directory. Couldn't find 'dangvinh77/data_ticket' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in dangvinh77/data_ticket. \\n\"]}" + "- {\"error\":\"Access to dataset vietgpt-archive/Cong-Thong-Tin-Dien-Tu-Thanh-Pho-Da-Nang is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1121,7 +569,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/tinago/gosiedan/gosiedan.py or any data file in the same directory. Couldn't find 'tinago/gosiedan' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in tinago/gosiedan. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/tinago/gosiedan/gosiedan.py or any data file in the same directory. Couldn't find 'tinago/gosiedan' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in tinago/gosiedan. \\n\"]}" + "- {\"error\":\"Access to dataset pufanyi/MIMICIT is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1133,7 +581,7 @@ { "data": { "text/markdown": [ - "- {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"FileNotFoundError\",\"cause_message\":\"Couldn't find a dataset script at /src/services/worker/zhangshuai507653/111/111.py or any data file in the same directory. Couldn't find 'zhangshuai507653/111' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in zhangshuai507653/111. \",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1508, in dataset_module_factory\\n raise FileNotFoundError(\\n\",\"FileNotFoundError: Couldn't find a dataset script at /src/services/worker/zhangshuai507653/111/111.py or any data file in the same directory. Couldn't find 'zhangshuai507653/111' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in zhangshuai507653/111. \\n\"]}" + "- {\"error\":\"Access to dataset vietgpt-archive/thuvienphapluat_qa_vi is restricted. You must be authenticated to access it.\"}" ], "text/plain": [ "" @@ -1185,211 +633,7 @@ { "data": { "text/markdown": [ - "- **[5081 similar errors]** [code 500]: {\"error\":\"Unexpected error.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[2649 similar errors]** [code 401]: {\"error\":\"The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[650 similar errors]** [code 501]: {\"error\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[337 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"DatasetWithScriptNotSupportedError\",\"cause_message\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 65, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", li" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[284 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"DatasetWithScriptNotSupportedError\",\"cause_message\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 65, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1511, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", li" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[74 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"DatasetWithScriptNotSupportedError\",\"cause_message\":\"The dataset viewer doesn't support this dataset because it runs arbitrary python code. Please open a discussion in the discussion tab if you think this is an error and tag @lhoestq and @severo.\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 64, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1511, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", li" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[39 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"ValueError\",\"cause_message\":\"Seek before start of file\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1054, in get_m" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[34 similar errors]** [code 501]: {\"error\":\"Job manager was killed while running this job (job exceeded maximum duration).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[17 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"ValueError\",\"cause_message\":\"Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('csv', {})}\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return H" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[16 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"AttributeError\",\"cause_message\":\"'NoneType' object has no attribute 'builder_configs'\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 361, in get_dataset_config_names\\n return list(builder_cls.builder_configs.keys()) or [dataset_module.builder_kwargs.get(\\\"config_name\\\", \\\"default\\\")]\\n\",\"AttributeError: 'NoneType' object has no attribute 'builder_configs'\\n\"]}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[15 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"BrokenPipeError\",\"cause_message\":\"[Errno 32] Broken pipe\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1031, in get" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[13 similar errors]** [code 501]: {\"error\":\"Job manager crashed while running this job (missing heartbeats).\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[12 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"BrokenPipeError\",\"cause_message\":\"[Errno 32] Broken pipe\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1031, in get" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[11 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"ValueError\",\"cause_message\":\"Invalid IPv6 URL\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1495, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1472, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1042, in get_module\\n " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[10 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"BadZipFile\",\"cause_message\":\"File is not a zip file\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1054, in get_modu" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[10 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"RuntimeError\",\"cause_message\":\"generator raised StopIteration\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1047, i" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[9 similar errors]** [code 500]: {\"error\":\"Authentication check on the Hugging Face Hub failed or timed out. Please try again later, it's a temporary internal issue.\"}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "- **[6 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"BadZipFile\",\"cause_message\":\"File is not a zip file\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 65, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1511, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1488, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScript(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1053, in get_modu" + "- **[37056 similar errors]** [code 400]: {\"error\":\"The croissant format is not available for this dataset.\"}" ], "text/plain": [ "" @@ -1401,7 +645,7 @@ { "data": { "text/markdown": [ - "- **[6 similar errors]** [code 500]: {\"error\":\"Cannot get the config names for the dataset.\",\"cause_exception\":\"ValueError\",\"cause_message\":\"Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): (None, {}), NamedSplit('test'): ('imagefolder', {})}\",\"cause_traceback\":[\"Traceback (most recent call last):\\n\",\" File \\\"/src/services/worker/src/worker/job_runners/dataset/config_names.py\\\", line 55, in compute_config_names_response\\n for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\\\", line 351, in get_dataset_config_names\\n dataset_module = dataset_module_factory(\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1512, in dataset_module_factory\\n raise e1 from None\\n\",\" File \\\"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\\\", line 1489, in dataset_module_factory\\n return HubDatasetModuleFactoryWithoutScrip" + "- **[89 similar errors]** [code 401]: {\"error\":\"Invalid username or password.\"}" ], "text/plain": [ ""