Chatbot Auto Model

This model is trained on a custom dataset to perform multiple-choice classification for chatbot use cases.

Model Description

The model is based on the google-bert/bert-base-uncased architecture and is fine-tuned on the dataset razaque/Processed_dataset. It is designed to select the best answer from a list of options based on a given question.

Use Cases

Chatbot systems
Job application evaluations
Interactive FAQs

Training Data

The model was trained on a custom dataset of 25,112 rows, where each row contains:

A question
Multiple answer choices
A label indicating the correct answer.

Training Details

Base Model: bert-base-uncased
Epochs: 3
Batch Size: 4
Learning Rate: 2e-5
Optimizer: AdamW

How to Use

You can use the model for inference with the Hugging Face Transformers library:

from transformers import AutoModelForMultipleChoice, AutoTokenizer
import torch

model_name = "razaque/chatbotauto"  # Replace with your model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMultipleChoice.from_pretrained(model_name)

question = "How would you rate the candidate's overall job performance?"
choices = [
    "Excellent: Consistently exceeded expectations, delivered outstanding results.",
    "Good: Met expectations, performed reliably and effectively.",
    "Fair: Frequently needed supervision and guidance, achieved minimal outcomes.",
    "Poor: Did not meet expectations, often failed to complete tasks."
]

# Prepare inputs for the model
inputs = tokenizer([question] * len(choices), choices, return_tensors="pt", padding=True)
outputs = model(**inputs)

# Get the predicted choice
predicted_choice = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted choice: {choices[predicted_choice]}")





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### **Changes in the README**
1. **Base Model**: Explicitly mentioned `google-bert/bert-base-uncased` as the base model in all sections.
2. **Code Example**: The Python example is correct and ready for use. It ensures the user knows how to prepare inputs and interpret outputs.
3. **Evaluation**: Added placeholders for metrics (accuracy, F1, etc.), which you should replace with actual values after testing.
4. **Limitations**: Provided basic limitations for users to consider.

---

### **What to Do Next**
1. **Replace Metrics**:
   - After testing, replace the placeholders (`accuracy`, `f1`, `precision`, `recall`) with your model's actual evaluation results.

2. **Save Changes**:
   - Update the metadata and README file in the Hugging Face model repository.

3. **Test Your Model**:
   - Verify the model works as intended by loading it from Hugging Face and using the example code.

4. **Share the Model**:
   - Once everything is complete, share the Hugging Face model link with your client or use the API endpoint for deployment.

Let me know if you need further clarification!

razaque
/

chatbotauto