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ReStructuredText
119 lines
5.8 KiB
ReStructuredText
..
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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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Blenderbot
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-----------------------------------------------------------------------------------------------------------------------
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ .
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The Blender chatbot model was proposed in `Recipes for building an open-domain chatbot
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<https://arxiv.org/pdf/2004.13637.pdf>`__ 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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The authors' code can be found `here <https://github.com/facebookresearch/ParlAI>`__ .
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Implementation Notes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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- Blenderbot uses a standard `seq2seq model transformer <https://arxiv.org/pdf/1706.03762.pdf>`__ based architecture.
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- It inherits completely from :class:`~transformers.BartForConditionalGeneration`
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- Even though blenderbot is one model, it uses two tokenizers :class:`~transformers.BlenderbotSmallTokenizer` for 90M
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checkpoint and :class:`~transformers.BlenderbotTokenizer` for all other checkpoints.
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- :class:`~transformers.BlenderbotSmallTokenizer` will always return :class:`~transformers.BlenderbotSmallTokenizer`,
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regardless of checkpoint. To use the 3B parameter checkpoint, you must call
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:class:`~transformers.BlenderbotTokenizer` directly.
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- Available checkpoints can be found in the `model hub <https://huggingface.co/models?search=blenderbot>`__.
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Usage
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Here is an example of model usage:
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.. code-block::
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>>> from transformers import BlenderbotSmallTokenizer, BlenderbotForConditionalGeneration
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>>> mname = 'facebook/blenderbot-90M'
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>>> model = BlenderbotForConditionalGeneration.from_pretrained(mname)
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>>> tokenizer = BlenderbotSmallTokenizer.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')
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>>> reply_ids = model.generate(**inputs)
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>>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in reply_ids])
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Here is how you can check out config values:
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.. code-block::
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>>> from transformers import BlenderbotConfig
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>>> config_90 = BlenderbotConfig.from_pretrained("facebook/blenderbot-90M")
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>>> config_90.to_diff_dict() # show interesting Values.
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>>> configuration_3B = BlenderbotConfig("facebook/blenderbot-3B")
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>>> configuration_3B.to_diff_dict()
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BlenderbotConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BlenderbotConfig
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:members:
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BlenderbotTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BlenderbotTokenizer
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:members: build_inputs_with_special_tokens
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BlenderbotSmallTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.BlenderbotSmallTokenizer
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:members:
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BlenderbotForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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See :obj:`transformers.BartForConditionalGeneration` for arguments to `forward` and `generate`
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.. autoclass:: transformers.BlenderbotForConditionalGeneration
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:members:
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TFBlenderbotForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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See :obj:`transformers.TFBartForConditionalGeneration` for arguments to `forward` and `generate`
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.. autoclass:: transformers.TFBlenderbotForConditionalGeneration
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:members:
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