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docs/source/en/model_doc/blenderbot.md
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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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.*
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# Blenderbot
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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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,
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Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020.
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The abstract of the paper is the following:
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*Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that
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scaling neural models in the number of parameters and the size of the data they are trained on gives improved results,
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we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of
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skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to
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their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent
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persona. We show that large scale models can learn these skills when given appropriate training data and choice of
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generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models
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and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn
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dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing
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failure cases of our models.*
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This model was contributed by [sshleifer](https://huggingface.co/sshleifer). The authors' code can be found [here](https://github.com/facebookresearch/ParlAI) .
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## Usage tips and example
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Blenderbot is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
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rather than the left.
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An example:
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```python
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from transformers import BlenderbotForConditionalGeneration, BlenderbotTokenizer
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mname = "facebook/blenderbot-400M-distill"
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model = BlenderbotForConditionalGeneration.from_pretrained(mname, device_map="auto")
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tokenizer = BlenderbotTokenizer.from_pretrained(mname)
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UTTERANCE = "My friends are cool but they eat too many carbs."
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inputs = tokenizer([UTTERANCE], return_tensors="pt").to(model.device)
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reply_ids = model.generate(**inputs)
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print(tokenizer.batch_decode(reply_ids))
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["<s> That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?</s>"]
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```
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## Implementation Notes
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- Blenderbot uses a standard [seq2seq model transformer](https://huggingface.co/papers/1706.03762) based architecture.
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- Available checkpoints can be found in the [model hub](https://huggingface.co/models?search=blenderbot).
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- This is the *default* Blenderbot model class. However, some smaller checkpoints, such as
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`facebook/blenderbot_small_90M`, have a different architecture and consequently should be used with
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[BlenderbotSmall](blenderbot-small).
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## Resources
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- [Causal language modeling task guide](../tasks/language_modeling)
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- [Translation task guide](../tasks/translation)
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- [Summarization task guide](../tasks/summarization)
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## BlenderbotConfig
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[[autodoc]] BlenderbotConfig
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## BlenderbotTokenizer
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[[autodoc]] BlenderbotTokenizer
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## BlenderbotTokenizerFast
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[[autodoc]] BlenderbotTokenizerFast
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## BlenderbotModel
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See [`~transformers.BartModel`] for arguments to *forward* and *generate*
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[[autodoc]] BlenderbotModel
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- forward
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## BlenderbotForConditionalGeneration
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See [`~transformers.BartForConditionalGeneration`] for arguments to *forward* and *generate*
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[[autodoc]] BlenderbotForConditionalGeneration
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- forward
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## BlenderbotForCausalLM
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[[autodoc]] BlenderbotForCausalLM
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- forward
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