Arc Of Conversation

🚀 Arc of the Conversation Model

The Conversation Arc Predictor model is designed to predict the arc of a conversation based on its text, offering valuable insights for various applications in understanding conversation flow.

🚀 Quick Start

The Conversation Arc Predictor model can predict the arc of a conversation from its text. It's based on the google/t5-small model, fine - tuned on a custom conversation dataset. This model can categorize conversation texts into predefined arcs, useful for customer service, chatbots, and conversational analysis.

✨ Features

• Fine - tuned Model: Built on google/t5-small, fine - tuned to understand and predict conversation arcs.
• Versatile Application: Can be applied in multiple fields where understanding conversation flow is crucial, such as customer service and chatbots.

📦 Installation

To use this model, you need to have the transformers library installed. You can install it using pip:

pip install transformers

If you plan to run the model on GPU, you also need to install the accelerate library:

pip install accelerate

💻 Usage Examples

Basic Usage

# Use a pipeline as a high - level helper
from transformers import pipeline

convo1 = 'Your conversation text here.'
pipe = pipeline("summarization", model="Falconsai/arc_of_conversation")
res1 = pipe(convo1, max_length=1024, min_length=512, do_sample=False)
print(res1)

Advanced Usage - Running on CPU

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Falconsai/arc_of_conversation")
model = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/arc_of_conversation")

input_text = "Your conversation Here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Advanced Usage - Running on GPU

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Falconsai/arc_of_conversation")
model = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/arc_of_conversation", device_map="auto")

input_text = "Your conversation Here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

📚 Documentation

Model Details

Model Architecture

The base model architecture is T5 (Text - To - Text Transfer Transformer), which treats every NLP problem as a text - to - text problem. The specific version used here is google/t5 - small, fine - tuned to understand and predict conversation arcs.

Fine - Tuning Data

The model was fine - tuned on a dataset of conversation texts and their corresponding arcs. The dataset should be formatted in a CSV file with two columns: conversation and arc.

Intended Use

The model is for categorizing the arc of conversation texts. It's useful for applications in customer service, chatbots, conversational analysis, and other areas where understanding the flow of a conversation is important.

Training

The training process involves the following steps:

1. Load and Explore Data: Load the dataset and perform initial exploration to understand the data distribution.
2. Preprocess Data: Tokenize the conversations and prepare them for the T5 model.
3. Fine - Tune Model: Fine - tune the google/t5 - small model using the preprocessed data.
4. Evaluate Model: Evaluate the model's performance on a validation set to ensure it's learning correctly.
5. Save Model: Save the fine - tuned model for future use.

Evaluation

The model's performance should be evaluated on a separate validation set to ensure it accurately predicts the conversation arcs. Metrics such as accuracy, precision, recall, and F1 score can be used to assess its performance.

Limitations

• Data Dependency: The model's performance is highly dependent on the quality and representativeness of the training data.
• Generalization: The model may not generalize well to conversation texts that are significantly different from the training data.

Ethical Considerations

When deploying the model, be mindful of the ethical implications, including but not limited to:

• Privacy: Ensure that conversation data used for training and inference does not contain sensitive or personally identifiable information.
• Bias: Be aware of potential biases in the training data that could affect the model's predictions.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🔧 Technical Details

The model uses the T5 architecture, which is a powerful text - to - text framework in NLP. Fine - tuning on a custom conversation dataset allows it to capture the patterns and structures of different conversation arcs. The training process follows standard NLP fine - tuning procedures, including data preprocessing, model training, and evaluation.

📚 Citation

If you use this model in your research, please cite it as follows:

@misc{conversation_arc_predictor,
  author = {Michael Stattelman},
  title = {Arc of the Conversation Generator},
  year = {2024},
  publisher = {Falcons.ai},
}