ReadMe

README.md

@@ -1,65 +1,53 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: bert-base-uncased
-tags:
-- generated_from_trainer
+language: en
+datasets:
+- glue
 metrics:
 - accuracy
-model-index:
-- name: train-test
-  results: []
+model-name: bert-base-uncased-finetuned-sst2
+tags:
+- text-classification
+- sentiment-analysis
 ---
 
-<!-- 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. -->
-
-# train-test
-
-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: 0.2701
-- Accuracy: 0.9312
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
+# BERT Base (uncased) fine-tuned on SST-2
 
-More information needed
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the **GLUE SST-2** dataset for sentiment classification (positive vs. negative).  
 
-## Training procedure
+## Model Details
+- **Model type**: BERT (base, uncased)  
+- **Fine-tuned on**: SST-2 (Stanford Sentiment Treebank)  
+- **Labels**:  
+  - 0 → Negative  
+  - 1 → Positive  
+- **Training framework**: [🤗 Transformers](https://github.com/huggingface/transformers)  
 
-### Training hyperparameters
+## Training
+- Epochs: 2  
+- Batch size: 4 (with gradient accumulation steps = 4)  
+- Learning rate: 3e-5  
+- Mixed precision: fp16  
+- Optimizer & Scheduler: Default Hugging Face Trainer  
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 16
-- 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: 2
-- mixed_precision_training: Native AMP
+## Evaluation Results
+On the SST-2 validation set:  
 
-### Training results
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|-------|---------------|-----------------|----------|
+| 1     | 0.1761        | 0.2282          | 93.0%    |
+| 2     | 0.1127        | 0.2701          | 93.1%    |
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.1761        | 1.0   | 4210 | 0.2282          | 0.9300   |
-| 0.1127        | 2.0   | 8420 | 0.2701          | 0.9312   |
+Final averaged training loss: **0.1663**  
 
+## How to Use
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-### Framework versions
+model_name = "your-username/bert-base-uncased-finetuned-sst2"
+tok = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-- Transformers 4.56.1
-- Pytorch 2.8.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.0
+inputs = tok("I love Hugging Face!", return_tensors="pt")
+outputs = model(**inputs)
+pred = outputs.logits.argmax(dim=-1).item()
+print("Label:", pred)  # 1 = Positive