Update README.md

README.md

@@ -1,65 +1,126 @@
 ---
-library_name: transformers
+language: en
 license: apache-2.0
 base_model: bert-base-uncased
 tags:
 - generated_from_trainer
+- paraphrase-identification
+- bert
+- glue
+- mrpc
 metrics:
 - accuracy
 - f1
+datasets:
+- glue
 model-index:
-- name: results
-  results: []
+- name: bert-base-uncased-finetuned-mrpc
+  results:
+  - task: 
+      type: text-classification
+      name: Paraphrase Identification
+    dataset:
+      name: GLUE MRPC
+      type: glue
+      args: mrpc
+    metrics:
+      - name: Accuracy
+        type: accuracy
+        value: 0.8652
+      - name: F1
+        type: f1
+        value: 0.9057
 ---
 
-<!-- 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. -->
+# BERT Fine-tuned on MRPC
 
-# results
-
-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: 0.5471
-- Accuracy: 0.8652
-- F1: 0.9057
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the MRPC (Microsoft Research Paraphrase Corpus) dataset from the GLUE benchmark. It is designed to determine whether two given sentences are semantically equivalent.
 
 ## Model description
 
-More information needed
+The model uses the BERT base architecture (12 layers, 768 hidden dimensions, 12 attention heads) and has been fine-tuned specifically for the paraphrase identification task. The output layer predicts whether the input sentence pair expresses the same meaning.
+
+Key specifications:
+- Base model: bert-base-uncased
+- Task type: Binary classification (paraphrase/not paraphrase)
+- Training method: Fine-tuning all layers
+- Language: English
 
 ## Intended uses & limitations
 
-More information needed
+### Intended uses
+- Paraphrase detection
+- Semantic similarity assessment
+- Question duplicate detection
+- Content matching
+- Automated text comparison
+
+### Limitations
+- Only works with English text
+- Performance may degrade on out-of-domain text
+- May struggle with complex or nuanced semantic relationships
+- Limited to comparing pairs of sentences (not longer texts)
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on the Microsoft Research Paraphrase Corpus (MRPC) from the GLUE benchmark:
+- Training set: 3,667 sentence pairs
+- Validation set: 408 sentence pairs
+- Each pair is labeled as either paraphrase (1) or non-paraphrase (0)
+- Class distribution: approximately 67.4% positive (paraphrase) and 32.6% negative (non-paraphrase)
 
 ## Training procedure
 
 ### Training hyperparameters
-
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use 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: 3
+- Learning rate: 3e-05
+- Batch size: 8 (train and eval)
+- Optimizer: AdamW (betas=(0.9,0.999), epsilon=1e-08)
+- LR scheduler: Linear decay
+- Number of epochs: 3
+- Max sequence length: 512
+- Weight decay: 0.01
 
 ### Training results
-
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| No log        | 1.0   | 459  | 0.3905          | 0.8382   | 0.8878 |
-| 0.5385        | 2.0   | 918  | 0.4275          | 0.8505   | 0.8961 |
-| 0.3054        | 3.0   | 1377 | 0.5471          | 0.8652   | 0.9057 |
-
+| No log        | 1.0   | 459  | 0.3905         | 0.8382   | 0.8878 |
+| 0.5385        | 2.0   | 918  | 0.4275         | 0.8505   | 0.8961 |
+| 0.3054        | 3.0   | 1377 | 0.5471         | 0.8652   | 0.9057 |
 
 ### Framework versions
-
 - Transformers 4.46.2
-- Pytorch 2.5.1+cu121
+- PyTorch 2.5.1+cu121
 - Datasets 3.1.0
 - Tokenizers 0.20.3
+
+## Performance analysis
+
+The model achieves strong performance on the MRPC validation set:
+- Accuracy: 86.52%
+- F1 Score: 90.57%
+
+These metrics indicate that the model is effective at identifying paraphrases while maintaining a good balance between precision and recall.
+
+## Example usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("real-jiakai/bert-base-uncased-finetuned-mrpc")
+model = AutoModelForSequenceClassification.from_pretrained("real-jiakai/bert-base-uncased-finetuned-mrpc")
+
+# Example function
+def check_paraphrase(sentence1, sentence2):
+    inputs = tokenizer(sentence1, sentence2, return_tensors="pt", padding=True, truncation=True)
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax().item()
+    return "Paraphrase" if prediction == 1 else "Not paraphrase"
+
+# Example usage
+sentence1 = "The cat sat on the mat."
+sentence2 = "A cat was sitting on the mat."
+result = check_paraphrase(sentence1, sentence2)
+print(f"Result: {result}")
+```