| # My Custom BERT Model | |
| This is a fine-tuned version of `bert-base-uncased` for binary sentiment analysis. | |
| ## Model Overview | |
| - Model: BERT for sequence classification | |
| - Number of Labels: 2 | |
| - Dataset: Custom sentiment dataset | |
| ## How to Use | |
| ```python | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| # Load model and tokenizer | |
| model = AutoModelForSequenceClassification.from_pretrained("your_username/my_model") | |
| tokenizer = AutoTokenizer.from_pretrained("your_username/my_model") | |
| # Tokenize text | |
| inputs = tokenizer("I love this!", return_tensors="pt") | |
| outputs = model(**inputs) | |