---
library_name: transformers
license: mit
language:
- en
pipeline_tag: summarization
---

# BART Base Text Summarization Modeli

<!-- Provide a quick summary of what the model is/does. -->
This model is based on the Facebook BART (Bidirectional and Auto-Regressive Transformers) architecture.  BART is particularly effective when fine-tuned for text generation tasks like summarization but also works well for comprehension tasks.
BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, 
and (2) learning a model to reconstruct the original text.
 
## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Architecture:** [BART Base]
- **Pre-trained model:** [facebook/bart-base]
- **Fine-tuned for:** [Summarization]
- **License:** [MIT]
- **Finetuned from model:** [facebook/bart-base]


## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
- **Installation:**
  pip install transformers

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Here is a simple snippet oon how to use the model directly.

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ChijoTheDatascientist/summarization-model")
model = AutoModelForSeq2SeqLM.from_pretrained("ChijoTheDatascientist/summarization-model")