transformers/docs/source/en/model_doc/blenderbot.md

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*This model was published in HF papers on 2020-04-28 and contributed to Hugging Face Transformers on 2020-11-16.*

# Blenderbot

<div class="flex flex-wrap space-x-1">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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## Overview

The Blender chatbot model was proposed in [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu,
Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.

The abstract of the paper is the following:

*Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
persona. We show that large scale models can learn these skills when given appropriate training data and choice of
generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
failure cases of our models.*

This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The authors' code can be found [here](https://github.com/facebookresearch/ParlAI) .

## Usage tips and example

Blenderbot is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
rather than the left.

An example:

```python
from transformers import BlenderbotForConditionalGeneration, BlenderbotTokenizer


mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname, device_map="auto")
tokenizer = BlenderbotTokenizer.from_pretrained(mname)
UTTERANCE = "My friends are cool but they eat too many carbs."
inputs = tokenizer([UTTERANCE], return_tensors="pt").to(model.device)
reply_ids = model.generate(**inputs)
print(tokenizer.batch_decode(reply_ids))
["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]
```

## Implementation Notes

- Blenderbot uses a standard [seq2seq model transformer](https://huggingface.co/papers/1706.03762) based architecture.
- Available checkpoints can be found in the [model hub](https://huggingface.co/models?search=blenderbot).
- This is the *default* Blenderbot model class. However, some smaller checkpoints, such as
  `facebook/blenderbot_small_90M`, have a different architecture and consequently should be used with
  [BlenderbotSmall](blenderbot-small).

## Resources

- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)

## BlenderbotConfig

[[autodoc]] BlenderbotConfig

## BlenderbotTokenizer

[[autodoc]] BlenderbotTokenizer

## BlenderbotTokenizerFast

[[autodoc]] BlenderbotTokenizerFast

## BlenderbotModel

See [`~transformers.BartModel`] for arguments to *forward* and *generate*

[[autodoc]] BlenderbotModel
    - forward

## BlenderbotForConditionalGeneration

See [`~transformers.BartForConditionalGeneration`] for arguments to *forward* and *generate*

[[autodoc]] BlenderbotForConditionalGeneration
    - forward

## BlenderbotForCausalLM

[[autodoc]] BlenderbotForCausalLM
    - forward