đ 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.
đ 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.
đĻ 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
đģ Usage Examples
Basic Usage
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
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
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]))
đ Documentation
Model Details
Property |
Details |
Model Name |
arc_of_conversation |
Model Type |
Fine - tuned google/t5 - small |
Language |
English |
License |
MIT |
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:
- 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.
đ License
This project is licensed under the MIT License. See the LICENSE file for details.
đ§ 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.
đ 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},
}