Training completed with emotion labels, simplified implementation code!

README.md

@@ -14,48 +14,25 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# RoBERTa Emotion Classifier
-This model is a fine-tuned version of `distilroberta-base` for emotion classification, trained on the [DAIR-AI Emotion Dataset](https://huggingface.co/datasets/dair-ai/emotion).
-
-## Model Description
-The model is designed to classify text into six different emotions: sadness, joy, love, anger, fear, and surprise. It uses a DistilRoBERTa architecture fine-tuned on emotional text data to provide accurate emotion predictions.
-
-### Performance
-The model achieves the following results on the evaluation set:
-- Loss: 0.1517
-- Accuracy: 94.05%
-
-## Intended Uses & Limitations
-
-### Intended Uses
-- Emotion analysis in text
-- Sentiment classification
-- Social media content analysis
-- Customer feedback emotion detection
-- Conversational AI emotion understanding
-
-### Limitations
-- Limited to six basic emotions
-- Best suited for English text
-- May not perform well on very short texts or slang
-- Not suitable for clinical or diagnostic purposes
-
-## Training and Evaluation Data
-
-### Dataset
-The model was fine-tuned on the [DAIR-AI Emotion Dataset](https://huggingface.co/datasets/dair-ai/emotion), which contains:
-- Training set: 16,000 examples
-- Validation set: 2,000 examples
-- Test set: 2,000 examples
-
-### Data Distribution
-The dataset includes text labeled with six emotion categories:
-- Sadness
-- Joy
-- Love
-- Anger
-- Fear
-- Surprise
+# roberta-emotion
+
+This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1336
+- Accuracy: 0.941
+- Per Class: {'sadness': 0.9636363636363636, 'joy': 0.96875, 'love': 0.8370786516853933, 'anger': 0.9454545454545454, 'fear': 0.9009433962264151, 'surprise': 0.8641975308641975}
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
 
 ## Training procedure
 
@@ -75,11 +52,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | Per Class                                                                                                                                                                       |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| 0.7509        | 1.0   | 500  | 0.2261          | 0.922    | {'sadness': 0.9527272727272728, 'joy': 0.9176136363636364, 'love': 0.9550561797752809, 'anger': 0.8581818181818182, 'fear': 0.8962264150943396, 'surprise': 0.9629629629629629} |
-| 0.1966        | 2.0   | 1000 | 0.1587          | 0.9315   | {'sadness': 0.9545454545454546, 'joy': 0.984375, 'love': 0.7415730337078652, 'anger': 0.92, 'fear': 0.9009433962264151, 'surprise': 0.8518518518518519}                         |
-| 0.1254        | 3.0   | 1500 | 0.1371          | 0.9415   | {'sadness': 0.9672727272727273, 'joy': 0.9630681818181818, 'love': 0.8539325842696629, 'anger': 0.9527272727272728, 'fear': 0.8632075471698113, 'surprise': 0.9382716049382716} |
-| 0.0995        | 4.0   | 2000 | 0.1316          | 0.9425   | {'sadness': 0.98, 'joy': 0.9644886363636364, 'love': 0.8370786516853933, 'anger': 0.9454545454545454, 'fear': 0.8820754716981132, 'surprise': 0.8765432098765432}               |
-| 0.073         | 5.0   | 2500 | 0.1444          | 0.943    | {'sadness': 0.9745454545454545, 'joy': 0.953125, 'love': 0.8820224719101124, 'anger': 0.9563636363636364, 'fear': 0.8915094339622641, 'surprise': 0.8641975308641975}           |
+| 0.7921        | 1.0   | 500  | 0.2453          | 0.9205   | {'sadness': 0.9618181818181818, 'joy': 0.953125, 'love': 0.8202247191011236, 'anger': 0.8763636363636363, 'fear': 0.8254716981132075, 'surprise': 0.9753086419753086}           |
+| 0.2062        | 2.0   | 1000 | 0.1608          | 0.938    | {'sadness': 0.9563636363636364, 'joy': 0.9829545454545454, 'love': 0.7415730337078652, 'anger': 0.9345454545454546, 'fear': 0.9433962264150944, 'surprise': 0.8518518518518519} |
+| 0.1342        | 3.0   | 1500 | 0.1418          | 0.9335   | {'sadness': 0.9581818181818181, 'joy': 0.9758522727272727, 'love': 0.7808988764044944, 'anger': 0.9127272727272727, 'fear': 0.9056603773584906, 'surprise': 0.8765432098765432} |
+| 0.1001        | 4.0   | 2000 | 0.1336          | 0.941    | {'sadness': 0.9636363636363636, 'joy': 0.96875, 'love': 0.8370786516853933, 'anger': 0.9454545454545454, 'fear': 0.9009433962264151, 'surprise': 0.8641975308641975}            |
+| 0.0751        | 5.0   | 2500 | 0.1445          | 0.942    | {'sadness': 0.9763636363636363, 'joy': 0.9573863636363636, 'love': 0.8595505617977528, 'anger': 0.9418181818181818, 'fear': 0.910377358490566, 'surprise': 0.8395061728395061}  |
 
 
 ### Framework versions

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