Description: A high-quality, multi-turn dialogue dataset covering everyday topics.
Features: Includes dialogues, communication acts, and emotion annotations.

Citation:

@InProceedings{li2017dailydialog,
    author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
    title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
    booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)},
    year = {2017}
}

Training Configuration

Learning Rate: 2e-5
Batch Size: 8
Number of Epochs: 3
Weight Decay: 0.01

Ethical Considerations

Inherited from the GPT-2 base model and the DailyDialog dataset, this model may reflect biases or limitations present in its training data. Caution is advised when using it in sensitive contexts, as it could produce biased or inappropriate responses.

How to Use

Using the Model Directly

python

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("harpertoken/harpertokenConvFT")
tokenizer = AutoTokenizer.from_pretrained("harpertoken/harpertokenConvFT")

# Prepare input
input_text = "Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Using the Terminal

Run the provided script to generate responses:

python3 generate_response.py --input "Hello, how are you?"

Using the API

Check API Status:

curl http://localhost:8000/status

Generate a Response:

curl -X POST http://localhost:8000/chat -H "Content-Type: application/json" -d '{"input_text": "Hello, how are you?"}'

Using FastAPI Documentation

Interact with the API via the browser at: http://localhost:8000/docs#/default/generate_response_chat_post

Related Models

harpertokenConvAI: https://huggingface.co/harpertoken/harpertokenConvAI - DistilBERT-based model for question answering. Note: This is not the base model for harpertokenConvFT due to incompatible architectures (DistilBERT vs GPT-2).
Base Model: This model is fine-tuned from GPT-2 (openai/gpt2).

Model Differences

harpertokenConvFT is a GPT-2 model for conversational AI, while harpertokenConvAI is a DistilBERT model for question answering. They have different architectures, tokenizers, and parameters, making fine-tuning between them impossible. For truthful info, refer to the config.json files.

bniladridas/conversational-ai-fine-tuned

HarpertokenConvFT

Model Details

Intended Use

Training Data

Dataset Characteristics