Update README.md

README.md

@@ -3,65 +3,135 @@ library_name: transformers
 license: apache-2.0
 base_model: bert-base-uncased
 tags:
+- text-classification
+- sentiment-analysis
+- emotion-classification
 - generated_from_trainer
 metrics:
+- f1_macro
 - accuracy
 model-index:
 - name: SentimentAnalysis-bert-base-uncased-finetuned-emotion
-  results: []
+  results:
+  - task:
+      type: text-classification
+      name: Emotion Classification
+    dataset:
+      name: Emotion Dataset
+      type: text
+    metrics:
+    - type: f1_macro
+      value: 0.8801
+      name: F1 Macro
+    - type: accuracy
+      value: 0.9215
+      name: Accuracy
 ---
 
-<!-- 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. -->
-
 # SentimentAnalysis-bert-base-uncased-finetuned-emotion
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2913
-- F1 Macro: 0.8801
-- Accuracy: 0.9215
+This model is a **fine-tuned version of `bert-base-uncased`** for **emotion classification** of short English texts such as tweets and social media posts.
+
+The model predicts one of **six emotion classes**:
+
+- sadness  
+- joy  
+- love  
+- anger  
+- fear  
+- surprise  
+
+The model was trained using the 🤗 **Transformers Trainer API**.
 
-## Model description
+---
+
+## Model performance
 
-More information needed
+Evaluation results on the test set:
 
-## Intended uses & limitations
+- **Loss:** 0.2913  
+- **F1 Macro:** 0.8801  
+- **Accuracy:** 0.9215  
 
-More information needed
+**Macro F1** is reported as the primary metric because the dataset is imbalanced and this metric better reflects performance across all emotion classes.
+
+---
 
-## Training and evaluation data
+## Intended uses
 
-More information needed
+This model is suitable for:
+
+- Emotion classification of tweets and short social media texts  
+- NLP research and academic projects  
+- Emotion-aware chatbots  
+- Sentiment and emotion analytics dashboards  
+
+---
+
+## Limitations
+
+- Optimized for **short texts**; performance may degrade on long documents  
+- **English-only**  
+- May reflect biases present in the training data  
+- Not intended for **high-stakes or sensitive decision-making**
+
+---
+
+## Training data
+
+The model was trained on an **emotion-labeled dataset of short texts** with six emotion categories.
+
+Preprocessing steps included:
+
+- Train / validation / test split  
+- Tokenization using the BERT tokenizer  
+- Padding and truncation to a fixed maximum sequence length  
+- Label encoding using Hugging Face `ClassLabel`
+
+---
 
 ## Training procedure
 
-### Training hyperparameters
+### Hyperparameters
+
+- **Base model:** bert-base-uncased  
+- **Learning rate:** 1e-5  
+- **Train batch size:** 16  
+- **Eval batch size:** 16  
+- **Epochs:** 6  
+- **Optimizer:** AdamW (Torch fused)  
+  - betas = (0.9, 0.999)  
+  - epsilon = 1e-8  
+- **Learning rate scheduler:** Linear  
+- **Seed:** 42  
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 6
+---
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
-| 0.7334        | 1.0   | 1000 | 0.2508          | 0.8941   | 0.917    |
-| 0.2016        | 2.0   | 2000 | 0.1881          | 0.9096   | 0.933    |
-| 0.145         | 3.0   | 3000 | 0.1981          | 0.9119   | 0.9355   |
-| 0.1178        | 4.0   | 4000 | 0.2229          | 0.9158   | 0.939    |
-| 0.0903        | 5.0   | 5000 | 0.2469          | 0.9161   | 0.9385   |
-| 0.0808        | 6.0   | 6000 | 0.2489          | 0.9100   | 0.9355   |
+| 0.7334 | 1.0 | 1000 | 0.2508 | 0.8941 | 0.9170 |
+| 0.2016 | 2.0 | 2000 | 0.1881 | 0.9096 | 0.9330 |
+| 0.1450 | 3.0 | 3000 | 0.1981 | 0.9119 | 0.9355 |
+| 0.1178 | 4.0 | 4000 | 0.2229 | 0.9158 | 0.9390 |
+| 0.0903 | 5.0 | 5000 | 0.2469 | 0.9161 | 0.9385 |
+| 0.0808 | 6.0 | 6000 | 0.2489 | 0.9100 | 0.9355 |
+
+The best model checkpoint was selected based on **macro F1 score**.
+
+---
 
+## Framework versions
+
+- **Transformers:** 4.57.1  
+- **PyTorch:** 2.8.0+cu126  
+- **Datasets:** 4.4.1  
+- **Tokenizers:** 0.22.1  
+
+---
 
-### Framework versions
+## Source code
 
-- Transformers 4.57.1
-- Pytorch 2.8.0+cu126
-- Datasets 4.4.1
-- Tokenizers 0.22.1
+Training and evaluation code is available on GitHub:  
+https://github.com/Abdelrahmanemam01/Sentiment-Analysis