Darshan03/AI-Hackathon

1---2library_name: transformers3tags: []4---5 6# Model Card for Fine-Tuned BERT for Classification7 8This model is a fine-tuned version of BERT for binary text classification tasks. It was trained on a specific dataset for classification purposes and is intended for use in text classification applications.9 10## Model Details11 12### Model Description13 14This BERT model has been fine-tuned for binary text classification. It is based on the `bert-base-uncased` model and has been trained to classify text into two categories: Class 0 and Class 1.15 16- **Developed by:** Your Name or Organization17- **Funded by [optional]:** [Add funding information if applicable]18- **Shared by [optional]:** [Add sharing information if applicable]19- **Model type:** Text Classification20- **Language(s) (NLP):** English21- **License:** Apache-2.022- **Finetuned from model [optional]:** BERT `bert-base-uncased`23 24### Model Sources25 26- **Repository:** [Link to your GitHub repository if available]27- **Paper [optional]:** [Link to related paper if available]28- **Demo [optional]:** [Link to a live demo if available]29 30## Uses31 32### Direct Use33 34This model is intended for binary text classification tasks. It can be used to classify text data into two categories.35 36### Downstream Use37 38The model can be fine-tuned further for other specific binary text classification tasks by using appropriate datasets and training procedures.39 40### Out-of-Scope Use41 42The model is not intended for use in tasks other than binary text classification. Misuse includes any application that requires multi-class classification or tasks beyond the scope of text classification.43 44## Bias, Risks, and Limitations45 46This model inherits biases present in the pre-trained BERT model and the fine-tuning dataset. Users should be cautious of potential biases related to language, context, and dataset-specific characteristics.47 48### Recommendations49 50Users should evaluate the model on their specific tasks and datasets to ensure it performs as expected. It is recommended to perform bias and fairness checks before deploying the model in production.51 52## How to Get Started with the Model53 54```python55import torch56from transformers import BertTokenizer, BertForSequenceClassification57 58# Load the model and tokenizer59model = BertForSequenceClassification.from_pretrained('Darshan03/AI-Hackathon')60tokenizer = BertTokenizer.from_pretrained('Darshan03/AI-Hackathon')61 62# Tokenize the input text63inputs = tokenizer("Your text here", return_tensors='pt', padding=True, truncation=True, max_length=128)64 65# Perform inference66outputs = model(**inputs)67logits = outputs.logits68predicted_class = torch.argmax(logits, dim=1).item()69 70print(f"Predicted class: {predicted_class}")71