arc_of_conversation

Falconsai/arc_of_conversation

arc_of_conversation is a summarization model from Falconsai. Use it when you need a shorter version of a longer text. It is set up for transformers. The card lists the license as apache-2.0.

- Model Name: arcofconversation - Model Type: Fine-tuned google/t5-small - Language: English - License: MIT

Downloads · 30 days

3% of all-time downloads

All-time downloads

1.6K

Public

Parameters

60.5M

1.2 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors242 MB · 99%

At a glance

Task: Summarization
Library: transformers
License: apache-2.0
Model type: t5
Access: Public
Created: Jun 9, 2024
Updated: Jul 1, 2024
SHA: 468fca35

Try a prompt

Model card

From the Hugging Face model README

Arc of the Conversation Model

Model Details

Model Name: arc_of_conversation
Model Type: Fine-tuned google/t5-small
Language: English
License: MIT

Overview

The Conversation Arc Predictor model is designed to predict the arc of a conversation given its text. It is based on the google/t5-small model, fine-tuned on a custom dataset of conversations and their corresponding arcs. This model can be used to analyze and categorize conversation texts into predefined arcs.

Model Description

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, which has been fine-tuned to understand and predict conversation arcs.

Fine-Tuning Data

The model was fine-tuned on a dataset consisting 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 intended for categorizing the arc of conversation texts. It can be useful for applications in customer service, chatbots, conversational analysis, and other areas where understanding the flow of a conversation is important.

How to Use

Inference

To use this model for inference, you need to load the fine-tuned model and tokenizer. Here is an example of how to do this using the transformers library:

Running Pipeline

# 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)

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]))

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]))

Training

The training process involves the following steps:

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

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},
}

Task: Summarization
Library: transformers
Type: t5
License: apache-2.0
Languages: en
Created: Jun 9, 2024
Updated: Jul 1, 2024