Initial commit of BertModel and tokenizer

README.md

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+# Custom BERT Model for Text Classification
+
+## Model Description
+
+This is a custom BERT model fine-tuned for text classification. The model was trained using a subset of a publicly available dataset and is capable of classifying text into 3 classes.
+
+## Training Details
+
+- **Architecture**: BERT Base Multilingual Cased
+- **Training data**: Custom dataset
+- **Preprocessing**: Tokenized using BERT's tokenizer, with a max sequence length of 80.
+- **Fine-tuning**: The model was trained for 1 epoch with a learning rate of 2e-5, using AdamW optimizer and Cross-Entropy Loss.
+- **Evaluation Metrics**: Accuracy on a held-out validation set.
+  
+## How to Use
+
+### Dependencies
+- Transformers 4.x
+- Torch 1.x
+
+### Code Snippet
+
+For classification:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("billfass/my_bert_model")
+model = AutoModelForSequenceClassification.from_pretrained("billfass/my_bert_model")
+
+text = "Your example text here."
+
+inputs = tokenizer(text, padding=True, truncation=True, max_length=80, return_tensors="pt")
+labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
+
+outputs = model(**inputs, labels=labels)
+loss = outputs.loss
+logits = outputs.logits
+
+# To get probabilities:
+probs = torch.softmax(logits, dim=-1)
+```
+
+## Limitations and Bias
+
+- Trained on a specific dataset, so may not generalize well to other kinds of text.
+- Uses multilingual cased BERT, so it's not optimized for any specific language.
+
+## Authors
+
+- **Fassinou Bile**
+- **billfass2010@gmail.com**
+  
+## Acknowledgments
+
+Special thanks to Hugging Face for providing the Transformers library that made this project possible.
+
+---