transformers safetensors bart text2text-generation summarization seq2seq huggingface dataset:samsum autotrain_compatible endpoints_compatible region:us
---
datasets:
- samsum
library_name: transformers
tags:
- summarization
- bart
- seq2seq
- huggingface
---

# Model Card for Fine-tuned BART on SAMSum for Dialogue Summarization

## Model Details

### Model Description

This model is a fine-tuned version of [BART](https://huggingface.co/facebook/bart-large-cnn) for dialogue summarization using the [SAMSum dataset](https://huggingface.co/datasets/samsum). It generates summaries for dialogues in the SAMSum dataset, which contains conversations between two participants. The model is designed to condense the conversation into a shorter, more concise summary.

- **Developed by:** [shogun-the-great](https://huggingface.co/shogun-the-great)
- **Model type:** Seq2Seq (Sequence-to-Sequence) for Summarization
- **Language(s):** English
- **License:** Apache-2.0 (or specify your license)
- **Finetuned from model:** `facebook/bart-large-cnn`

### Model Sources

- **Dataset:** [SAMSum Dataset](https://huggingface.co/datasets/samsum)

## Uses

### Direct Use

This model can be directly used for automatic dialogue summarization. It can generate summaries of conversations between two individuals, ideal for applications such as:

- Meeting notes summarization.
- Chat summarization for customer support or virtual assistants.
- Condensing lengthy conversations into digestible formats.

### Downstream Use

This model can be further fine-tuned on specific dialogue datasets for applications requiring more context-specific summarization or domain-specific conversations.

### Out-of-Scope Use

This model may not perform well on:
- Non-English dialogues.
- Conversations with heavy slang or informal language not represented in the SAMSum dataset.

## Bias, Risks, and Limitations

### Bias

The model may inherit biases present in the SAMSum dataset, including but not limited to gender, tone, and conversational context.

### Risks

- Summarization might omit important details if they are not deemed essential by the model.
- Inaccuracies in the generated summaries could lead to misinterpretation in sensitive contexts.

### Recommendations

- Regularly evaluate and fine-tune the model with more diverse datasets for improved generalization.
- Monitor the generated summaries for quality and relevance, especially in customer-facing or high-stakes applications.

## How to Get Started with the Model

You can load and use the fine-tuned model directly from the Hugging Face Hub:

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the tokenizer and model from Hugging Face Hub
model_name = "shogun-the-great/finetuned-bart-samsum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Example usage for summarizing a dialogue
dialogue = "Hi, how are you today? I'm good, thanks! What about you?"

inputs = tokenizer(dialogue, return_tensors="pt", truncation=True, max_length=1024)
summary_ids = model.generate(inputs['input_ids'], max_length=50, num_beams=4, early_stopping=True)

# Decode the generated summary
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("Summary:", summary)
