Create README.md

README.md

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+---
+language: en
+tags:
+- sentiment-analysis
+- transformers
+- pytorch
+license: apache-2.0
+datasets: 
+- custom-dataset
+metrics:
+- accuracy
+model_name: distilbert-base-uncased-finetuned-sentiment
+---
+
+# DistilBERT Base Uncased Fine-tuned for Sentiment Analysis
+
+## Model Description
+
+This model is a fine-tuned version of `distilbert-base-uncased` on a sentiment analysis dataset. It is trained to classify text into positive and negative sentiment categories.
+
+## Training Details
+
+The model was fine-tuned on a sentiment analysis dataset using the Hugging Face `transformers` library. The training parameters are as follows:
+
+- **Learning Rate**: 2e-5
+- **Batch Size**: 32
+- **Number of Epochs**: 4
+- **Optimizer**: AdamW
+- **Scheduler**: Linear with warmup
+- **Device**: Nvidia T4 GPU
+
+## Training and Validation Metrics
+
+| Step | Training Loss | Validation Loss | Accuracy |
+|------|---------------|-----------------|----------|
+| 400  | 0.389300      | 0.181316        | 93.25%   |
+| 800  | 0.161900      | 0.166204        | 94.13%   |
+| 1200 | 0.114600      | 0.200135        | 94.30%   |
+| 1600 | 0.076300      | 0.211609        | 94.40%   |
+| 2000 | 0.041600      | 0.225439        | 94.45%   |
+
+Final training metrics:
+
+- **Global Step**: 2000
+- **Training Loss**: 0.156715
+- **Training Runtime**: 1257.5696 seconds
+- **Training Samples per Second**: 50.892
+- **Training Steps per Second**: 1.59
+- **Total FLOPS**: 8477913513984000.0
+- **Epochs**: 4.0
+
+## Model Performance
+
+The model achieves an accuracy of approximately 94.45% on the validation set.
+
+## Usage
+
+To use this model for sentiment analysis, you can load it using the `transformers` library:
+
+```python
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+
+model_name = 'luluw/distilbert-base-uncased-finetuned-sentiment'
+tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+
+# Example usage
+text = "I love this product!"
+inputs = tokenizer(text, return_tensors='pt')
+outputs = model(**inputs)
+predictions = torch.argmax(outputs.logits, dim=-1)
+```