Update README.md

README.md

@@ -1,64 +1,59 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: results
-  results: []
----
-
-<!-- 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. -->
-
-# results
-
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1382
-- Accuracy: 0.94
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 4
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.511         | 1.0   | 500  | 0.1797          | 0.933    |
-| 0.1495        | 2.0   | 1000 | 0.1449          | 0.938    |
-| 0.1002        | 3.0   | 1500 | 0.1350          | 0.9415   |
-| 0.0707        | 4.0   | 2000 | 0.1382          | 0.94     |
-
-
-### Framework versions
-
-- Transformers 4.46.3
-- Pytorch 2.5.1+cu118
-- Datasets 3.1.0
-- Tokenizers 0.20.3
+---
+language: en
+tags:
+- distilbert
+- emotion-classification
+- text-classification
+datasets:
+- dair-ai/emotion
+metrics:
+- accuracy
+---
+
+# Emotion Classification Model
+
+## Model Description
+This model fine-tunes DistilBERT for multi-class emotion classification on the `dair-ai/emotion` dataset. 
+The model is designed to classify text into one of six emotions: sadness, joy, love, anger, fear, or surprise. 
+It can be used in applications requiring emotional analysis in English text.
+
+## Training and Evaluation
+- **Training Dataset**: `dair-ai/emotion` (16,000 examples)
+- **Training Time**: 8 minutes and 51 seconds
+- **Training Hyperparameters**:
+  - Learning Rate: `3e-5`
+  - Batch Size: `32`
+  - Epochs: `4`
+  - Weight Decay: `0.01`
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.511         | 1.0   | 500  | 0.1797          | 0.933    |
+| 0.1495        | 2.0   | 1000 | 0.1449          | 0.938    |
+| 0.1002        | 3.0   | 1500 | 0.1350          | 0.9415   |
+| 0.0707        | 4.0   | 2000 | 0.1382          | 0.94     |
+
+- **Validation Accuracy**: 
+  - Epoch 1: 0.9275
+  - Epoch 2: 0.9345
+  - Epoch 3: 0.940
+  - Epoch 4: 0.940
+- **Test Accuracy**: 100% accuracy on the provided 10 test examples.
+
+
+## Usage
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="Zoopa/emotion-classification-model")
+
+text = "I am so happy today!"
+result = classifier(text)
+print(result)
+‘‘‘
+
+## Limitations
+  - The model only supports English.
+  - The training dataset may contain biases, affecting model predictions on test data.
+  - Edge Cases like mixed emotions might reduce accuracy.