Feat/emotion/jais13b

assets/ar/sentiment_emotion_others/emotion/Emotion_Jais13b_FewShot.py

@@ -0,0 +1,92 @@
+from llmebench.datasets import EmotionDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import EmotionTask
+
+
+def metadata():
+    return {
+        "author": "Arabic Language Technologies, QCRI, HBKU",
+        "model": "Jais-13b-chat",
+        "description": "Locally hosted Jais-13b-chat model using FastChat. 3-shot results.",
+        "scores": {"Jaccard similarity": "0.1001005"},
+    }
+
+
+def config():
+    return {
+        "dataset": EmotionDataset,
+        "task": EmotionTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": [
+                "anger",
+                "disgust",
+                "fear",
+                "joy",
+                "love",
+                "optimism",
+                "pessimism",
+                "sadness",
+                "surprise",
+                "trust",
+            ],
+            "max_tries": 30,
+        },
+    }
+
+
+def prompt(input_sample, examples):
+    base_prompt = f"Predict all the possible emotions in the following Arabic sentence without explanation and put them in a Python list. List of emotions is: anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust.\n "
+
+    return [
+        {
+            "role": "user",
+            "content": few_shot_prompt(input_sample, base_prompt, examples),
+        },
+    ]
+
+
+def few_shot_prompt(input_sample, base_prompt, examples):
+    out_prompt = base_prompt + "\n"
+    for example in examples:
+        # Found chatgpt confused when using 0 and 1 in the prompt
+        label_list = ", ".join(map(str, example["label"]))
+        out_prompt = (
+            out_prompt + "Sentence: " + example["input"] + "\n" + label_list + "\n\n"
+        )
+
+    # Append the sentence we want the model to predict for but leave the Label blank
+    out_prompt = out_prompt + "Sentence: " + input_sample + "\nlabel: \n"
+
+    return out_prompt
+
+
+emotions_positions = {
+    "anger": 0,
+    "anticipation": 1,
+    "disgust": 2,
+    "fear": 3,
+    "joy": 4,
+    "love": 5,
+    "optimism": 6,
+    "pessimism": 7,
+    "sadness": 8,
+    "surprise": 9,
+    "trust": 10,
+}
+
+
+def emotions_array(labels):
+    labels_arr = []
+    for x, y in emotions_positions.items():
+        v = 0
+        if x.lower() in labels:
+            v = 1
+        labels_arr.append(v)
+    return labels_arr
+
+
+def post_process(response):
+    out = emotions_array(response["choices"][0]["message"]["content"])
+
+    return out

assets/ar/sentiment_emotion_others/emotion/Emotion_Jais13b_ZeroShot.py

@@ -0,0 +1,80 @@
+from llmebench.datasets import EmotionDataset
+from llmebench.models import FastChatModel
+from llmebench.tasks import EmotionTask
+
+
+def metadata():
+    return {
+        "author": "Arabic Language Technologies, QCRI, HBKU",
+        "model": "Jais-13b-chat",
+        "description": "Locally hosted Jais-13b-chat model using FastChat.",
+        "scores": {"Jaccard similarity": "0.16779"},
+    }
+
+
+def config():
+    return {
+        "dataset": EmotionDataset,
+        "task": EmotionTask,
+        "model": FastChatModel,
+        "model_args": {
+            "class_labels": [
+                "anger",
+                "disgust",
+                "fear",
+                "joy",
+                "love",
+                "optimism",
+                "pessimism",
+                "sadness",
+                "surprise",
+                "trust",
+            ],
+            "max_tries": 30,
+        },
+    }
+
+
+def prompt(input_sample):
+    base_prompt = (
+        f"Predict all the possible emotions in the following Arabic sentence without explanation and put them in a Python list. List of emotions is: anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, and trust.\n "
+        f"Sentence: {input_sample}\n"
+        f"label: \n"
+    )
+    return [
+        {
+            "role": "user",
+            "content": base_prompt,
+        },
+    ]
+
+
+emotions_positions = {
+    "anger": 0,
+    "anticipation": 1,
+    "disgust": 2,
+    "fear": 3,
+    "joy": 4,
+    "love": 5,
+    "optimism": 6,
+    "pessimism": 7,
+    "sadness": 8,
+    "surprise": 9,
+    "trust": 10,
+}
+
+
+def emotions_array(labels):
+    labels_arr = []
+    for x, y in emotions_positions.items():
+        v = 0
+        if x.lower() in labels:
+            v = 1
+        labels_arr.append(v)
+    return labels_arr
+
+
+def post_process(response):
+    out = emotions_array(response["choices"][0]["message"]["content"])
+
+    return out