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Unverified Commit 206f06f2 authored by yujun's avatar yujun Committed by GitHub
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Add new model RoFormer (use rotary position embedding ) (#11684) 



* add roformer

* Update docs/source/model_doc/roformer.rst
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>

* Update docs/source/model_doc/roformer.rst
Co-authored-by: default avatarSuraj Patil <surajp815@gmail.com>

* update

* add TFRoFormerSinusoidalPositionalEmbedding and fix TFMarianSinusoidalPositionalEmbedding

* update docs

* make style and make quality

* roback

* unchanged

* rm copies from , this is a error in TFMarianSinusoidalPositionalEmbedding

* update Copyright year

* move # Add modeling imports here to the correct position

* max_position_embeddings can be set to 1536

* # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer

* # Copied from transformers.models.bert.modeling_bert.BertLayer.__init__ with Bert->RoFormer

* update tokenization_roformer

* make style

* add staticmethod apply_rotary_position_embeddings

* add TF staticmethod apply_rotary_position_em...
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README.md
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...@@ -243,6 +243,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. ...@@ -243,6 +243,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou. 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya. 1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. 1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. 1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu. 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
......
docs/source/index.rst
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...@@ -231,41 +231,44 @@ Supported models ...@@ -231,41 +231,44 @@ Supported models
45. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT 45. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
46. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper 46. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
47. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
`fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
47. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP 48. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
Krishna, and Kurt W. Keutzer. Krishna, and Kurt W. Keutzer.
48. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a 49. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu. Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
49. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via 50. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
Francesco Piccinno and Julian Martin Eisenschlos. Francesco Piccinno and Julian Martin Eisenschlos.
50. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL: 51. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*, Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
51. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16 52. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy, Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
52. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for 53. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
Zhou, Abdelrahman Mohamed, Michael Auli. Zhou, Abdelrahman Mohamed, Michael Auli.
53. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model 54. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau. Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
54. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet: 55. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou. Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
55. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised 56. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
Zettlemoyer and Veselin Stoyanov. Zettlemoyer and Veselin Stoyanov.
56. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive 57. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le. Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
57. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised 58. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli. Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
...@@ -369,6 +372,8 @@ Flax), PyTorch, and/or TensorFlow. ...@@ -369,6 +372,8 @@ Flax), PyTorch, and/or TensorFlow.
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+ +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ | | RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+ +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| RoFormer | ✅ | ✅ | ✅ | ✅ | ❌ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| Speech2Text | ✅ | ❌ | ✅ | ❌ | ❌ | | Speech2Text | ✅ | ❌ | ✅ | ❌ | ❌ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+ +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ | | SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
...@@ -520,6 +525,7 @@ Flax), PyTorch, and/or TensorFlow. ...@@ -520,6 +525,7 @@ Flax), PyTorch, and/or TensorFlow.
model_doc/reformer model_doc/reformer
model_doc/retribert model_doc/retribert
model_doc/roberta model_doc/roberta
model_doc/roformer
model_doc/speech_to_text model_doc/speech_to_text
model_doc/squeezebert model_doc/squeezebert
model_doc/t5 model_doc/t5
......
docs/source/model_doc/roformer.rst 0 → 100644
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..
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
RoFormer
-----------------------------------------------------------------------------------------------------------------------
Overview
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The RoFormer model was proposed in `RoFormer: Enhanced Transformer with Rotary Position Embedding
<https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
The abstract from the paper is the following:
*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
different positions in the sequence. We investigate various methods to encode positional information in
transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
experiment for English benchmark will soon be updated.*
Tips:
- RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
improved performance on classification tasks with long texts.
This model was contributed by `junnyu <https://huggingface.co/junnyu>`__. The original code can be found `here
<https://github.com/ZhuiyiTechnology/roformer>`__.
RoFormerConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerConfig
:members:
RoFormerTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerTokenizer
:members: build_inputs_with_special_tokens, get_special_tokens_mask,
create_token_type_ids_from_sequences, save_vocabulary
RobertaTokenizerFast
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerTokenizerFast
:members: build_inputs_with_special_tokens
RoFormerModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerModel
:members: forward
RoFormerForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForCausalLM
:members: forward
RoFormerForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForMaskedLM
:members: forward
RoFormerForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForSequenceClassification
:members: forward
RoFormerForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForMultipleChoice
:members: forward
RoFormerForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForTokenClassification
:members: forward
RoFormerForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RoFormerForQuestionAnswering
:members: forward
TFRoFormerModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerModel
:members: call
TFRoFormerForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForMaskedLM
:members: call
TFRoFormerForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForCausalLM
:members: call
TFRoFormerForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForSequenceClassification
:members: call
TFRoFormerForMultipleChoice
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForMultipleChoice
:members: call
TFRoFormerForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForTokenClassification
:members: call
TFRoFormerForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFRoFormerForQuestionAnswering
:members: call
src/transformers/__init__.py
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...@@ -218,6 +218,7 @@ _import_structure = { ...@@ -218,6 +218,7 @@ _import_structure = {
"models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"], "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"],
"models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"], "models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"],
"models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"], "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"],
"models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"],
"models.speech_to_text": [ "models.speech_to_text": [
"SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Speech2TextConfig", "Speech2TextConfig",
...@@ -322,6 +323,7 @@ else: ...@@ -322,6 +323,7 @@ else:
# tokenizers-backed objects # tokenizers-backed objects
if is_tokenizers_available(): if is_tokenizers_available():
# Fast tokenizers # Fast tokenizers
_import_structure["models.roformer"].append("RoFormerTokenizerFast")
_import_structure["models.clip"].append("CLIPTokenizerFast") _import_structure["models.clip"].append("CLIPTokenizerFast")
_import_structure["models.convbert"].append("ConvBertTokenizerFast") _import_structure["models.convbert"].append("ConvBertTokenizerFast")
_import_structure["models.albert"].append("AlbertTokenizerFast") _import_structure["models.albert"].append("AlbertTokenizerFast")
...@@ -927,6 +929,21 @@ if is_torch_available(): ...@@ -927,6 +929,21 @@ if is_torch_available():
"RobertaModel", "RobertaModel",
] ]
) )
_import_structure["models.roformer"].extend(
[
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
)
_import_structure["models.speech_to_text"].extend( _import_structure["models.speech_to_text"].extend(
[ [
"SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
...@@ -1339,6 +1356,20 @@ if is_tf_available(): ...@@ -1339,6 +1356,20 @@ if is_tf_available():
"TFRobertaPreTrainedModel", "TFRobertaPreTrainedModel",
] ]
) )
_import_structure["models.roformer"].extend(
[
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
)
_import_structure["models.t5"].extend( _import_structure["models.t5"].extend(
[ [
"TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
...@@ -1641,6 +1672,7 @@ if TYPE_CHECKING: ...@@ -1641,6 +1672,7 @@ if TYPE_CHECKING:
from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer
from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer
from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer
from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
...@@ -1767,6 +1799,7 @@ if TYPE_CHECKING: ...@@ -1767,6 +1799,7 @@ if TYPE_CHECKING:
from .models.reformer import ReformerTokenizerFast from .models.reformer import ReformerTokenizerFast
from .models.retribert import RetriBertTokenizerFast from .models.retribert import RetriBertTokenizerFast
from .models.roberta import RobertaTokenizerFast from .models.roberta import RobertaTokenizerFast
from .models.roformer import RoFormerTokenizerFast
from .models.squeezebert import SqueezeBertTokenizerFast from .models.squeezebert import SqueezeBertTokenizerFast
from .models.t5 import T5TokenizerFast from .models.t5 import T5TokenizerFast
from .models.xlm_roberta import XLMRobertaTokenizerFast from .models.xlm_roberta import XLMRobertaTokenizerFast
...@@ -2232,6 +2265,19 @@ if TYPE_CHECKING: ...@@ -2232,6 +2265,19 @@ if TYPE_CHECKING:
RobertaForTokenClassification, RobertaForTokenClassification,
RobertaModel, RobertaModel,
) )
from .models.roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
from .models.speech_to_text import ( from .models.speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
Speech2TextForConditionalGeneration, Speech2TextForConditionalGeneration,
...@@ -2575,6 +2621,18 @@ if TYPE_CHECKING: ...@@ -2575,6 +2621,18 @@ if TYPE_CHECKING:
TFRobertaModel, TFRobertaModel,
TFRobertaPreTrainedModel, TFRobertaPreTrainedModel,
) )
from .models.roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
from .models.t5 import ( from .models.t5 import (
TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
TFT5EncoderModel, TFT5EncoderModel,
......
src/transformers/convert_slow_tokenizer.py
View file @ 206f06f2
...@@ -25,6 +25,7 @@ from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, ...@@ -25,6 +25,7 @@ from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers,
from tokenizers.models import BPE, Unigram, WordPiece from tokenizers.models import BPE, Unigram, WordPiece
from .file_utils import requires_backends from .file_utils import requires_backends
from .models.roformer.tokenization_utils import JiebaPreTokenizer
class SentencePieceExtractor: class SentencePieceExtractor:
...@@ -296,6 +297,43 @@ class RobertaConverter(Converter): ...@@ -296,6 +297,43 @@ class RobertaConverter(Converter):
return tokenizer return tokenizer
class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=False,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab))
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class DebertaConverter(Converter): class DebertaConverter(Converter):
def converted(self) -> Tokenizer: def converted(self) -> Tokenizer:
ot = self.original_tokenizer ot = self.original_tokenizer
...@@ -755,6 +793,7 @@ SLOW_TO_FAST_CONVERTERS = { ...@@ -755,6 +793,7 @@ SLOW_TO_FAST_CONVERTERS = {
"ReformerTokenizer": ReformerConverter, "ReformerTokenizer": ReformerConverter,
"RetriBertTokenizer": BertConverter, "RetriBertTokenizer": BertConverter,
"RobertaTokenizer": RobertaConverter, "RobertaTokenizer": RobertaConverter,
"RoFormerTokenizer": RoFormerConverter,
"SqueezeBertTokenizer": BertConverter, "SqueezeBertTokenizer": BertConverter,
"T5Tokenizer": T5Converter, "T5Tokenizer": T5Converter,
"XLMRobertaTokenizer": XLMRobertaConverter, "XLMRobertaTokenizer": XLMRobertaConverter,
......
src/transformers/models/__init__.py
View file @ 206f06f2
...@@ -68,6 +68,7 @@ from . import ( ...@@ -68,6 +68,7 @@ from . import (
reformer, reformer,
retribert, retribert,
roberta, roberta,
roformer,
speech_to_text, speech_to_text,
squeezebert, squeezebert,
t5, t5,
......
src/transformers/models/auto/configuration_auto.py
View file @ 206f06f2
...@@ -68,6 +68,7 @@ from ..rag.configuration_rag import RagConfig ...@@ -68,6 +68,7 @@ from ..rag.configuration_rag import RagConfig
from ..reformer.configuration_reformer import ReformerConfig from ..reformer.configuration_reformer import ReformerConfig
from ..retribert.configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig from ..retribert.configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
from ..roberta.configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig from ..roberta.configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
from ..roformer.configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig
from ..speech_to_text.configuration_speech_to_text import ( from ..speech_to_text.configuration_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
Speech2TextConfig, Speech2TextConfig,
...@@ -91,6 +92,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict( ...@@ -91,6 +92,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
(key, value) (key, value)
for pretrained_map in [ for pretrained_map in [
# Add archive maps here # Add archive maps here
ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
...@@ -146,6 +148,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict( ...@@ -146,6 +148,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
CONFIG_MAPPING = OrderedDict( CONFIG_MAPPING = OrderedDict(
[ [
# Add configs here # Add configs here
("roformer", RoFormerConfig),
("clip", CLIPConfig), ("clip", CLIPConfig),
("bigbird_pegasus", BigBirdPegasusConfig), ("bigbird_pegasus", BigBirdPegasusConfig),
("deit", DeiTConfig), ("deit", DeiTConfig),
...@@ -207,6 +210,7 @@ CONFIG_MAPPING = OrderedDict( ...@@ -207,6 +210,7 @@ CONFIG_MAPPING = OrderedDict(
MODEL_NAMES_MAPPING = OrderedDict( MODEL_NAMES_MAPPING = OrderedDict(
[ [
# Add full (and cased) model names here # Add full (and cased) model names here
("roformer", "RoFormer"),
("clip", "CLIP"), ("clip", "CLIP"),
("bigbird_pegasus", "BigBirdPegasus"), ("bigbird_pegasus", "BigBirdPegasus"),
("deit", "DeiT"), ("deit", "DeiT"),
......
src/transformers/models/auto/modeling_auto.py
View file @ 206f06f2
...@@ -240,6 +240,15 @@ from ..roberta.modeling_roberta import ( ...@@ -240,6 +240,15 @@ from ..roberta.modeling_roberta import (
RobertaForTokenClassification, RobertaForTokenClassification,
RobertaModel, RobertaModel,
) )
from ..roformer.modeling_roformer import (
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerModel,
)
from ..speech_to_text.modeling_speech_to_text import Speech2TextForConditionalGeneration, Speech2TextModel from ..speech_to_text.modeling_speech_to_text import Speech2TextForConditionalGeneration, Speech2TextModel
from ..squeezebert.modeling_squeezebert import ( from ..squeezebert.modeling_squeezebert import (
SqueezeBertForMaskedLM, SqueezeBertForMaskedLM,
...@@ -334,6 +343,7 @@ from .configuration_auto import ( ...@@ -334,6 +343,7 @@ from .configuration_auto import (
ReformerConfig, ReformerConfig,
RetriBertConfig, RetriBertConfig,
RobertaConfig, RobertaConfig,
RoFormerConfig,
Speech2TextConfig, Speech2TextConfig,
SqueezeBertConfig, SqueezeBertConfig,
T5Config, T5Config,
...@@ -354,6 +364,7 @@ logger = logging.get_logger(__name__) ...@@ -354,6 +364,7 @@ logger = logging.get_logger(__name__)
MODEL_MAPPING = OrderedDict( MODEL_MAPPING = OrderedDict(
[ [
# Base model mapping # Base model mapping
(RoFormerConfig, RoFormerModel),
(CLIPConfig, CLIPModel), (CLIPConfig, CLIPModel),
(BigBirdPegasusConfig, BigBirdPegasusModel), (BigBirdPegasusConfig, BigBirdPegasusModel),
(DeiTConfig, DeiTModel), (DeiTConfig, DeiTModel),
...@@ -451,6 +462,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict( ...@@ -451,6 +462,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
MODEL_WITH_LM_HEAD_MAPPING = OrderedDict( MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[ [
# Model with LM heads mapping # Model with LM heads mapping
(RoFormerConfig, RoFormerForMaskedLM),
(BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration), (BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration),
(GPTNeoConfig, GPTNeoForCausalLM), (GPTNeoConfig, GPTNeoForCausalLM),
(BigBirdConfig, BigBirdForMaskedLM), (BigBirdConfig, BigBirdForMaskedLM),
...@@ -498,6 +510,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict( ...@@ -498,6 +510,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict( MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
[ [
# Model for Causal LM mapping # Model for Causal LM mapping
(RoFormerConfig, RoFormerForCausalLM),
(BigBirdPegasusConfig, BigBirdPegasusForCausalLM), (BigBirdPegasusConfig, BigBirdPegasusForCausalLM),
(GPTNeoConfig, GPTNeoForCausalLM), (GPTNeoConfig, GPTNeoForCausalLM),
(BigBirdConfig, BigBirdForCausalLM), (BigBirdConfig, BigBirdForCausalLM),
...@@ -539,6 +552,7 @@ MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = OrderedDict( ...@@ -539,6 +552,7 @@ MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = OrderedDict(
MODEL_FOR_MASKED_LM_MAPPING = OrderedDict( MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
[ [
# Model for Masked LM mapping # Model for Masked LM mapping
(RoFormerConfig, RoFormerForMaskedLM),
(BigBirdConfig, BigBirdForMaskedLM), (BigBirdConfig, BigBirdForMaskedLM),
(Wav2Vec2Config, Wav2Vec2ForMaskedLM), (Wav2Vec2Config, Wav2Vec2ForMaskedLM),
(ConvBertConfig, ConvBertForMaskedLM), (ConvBertConfig, ConvBertForMaskedLM),
...@@ -592,6 +606,7 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict( ...@@ -592,6 +606,7 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict( MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
[ [
# Model for Sequence Classification mapping # Model for Sequence Classification mapping
(RoFormerConfig, RoFormerForSequenceClassification),
(BigBirdPegasusConfig, BigBirdPegasusForSequenceClassification), (BigBirdPegasusConfig, BigBirdPegasusForSequenceClassification),
(BigBirdConfig, BigBirdForSequenceClassification), (BigBirdConfig, BigBirdForSequenceClassification),
(ConvBertConfig, ConvBertForSequenceClassification), (ConvBertConfig, ConvBertForSequenceClassification),
...@@ -630,6 +645,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict( ...@@ -630,6 +645,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict( MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
[ [
# Model for Question Answering mapping # Model for Question Answering mapping
(RoFormerConfig, RoFormerForQuestionAnswering),
(BigBirdPegasusConfig, BigBirdPegasusForQuestionAnswering), (BigBirdPegasusConfig, BigBirdPegasusForQuestionAnswering),
(BigBirdConfig, BigBirdForQuestionAnswering), (BigBirdConfig, BigBirdForQuestionAnswering),
(ConvBertConfig, ConvBertForQuestionAnswering), (ConvBertConfig, ConvBertForQuestionAnswering),
...@@ -670,6 +686,7 @@ MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict( ...@@ -670,6 +686,7 @@ MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict(
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict( MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
[ [
# Model for Token Classification mapping # Model for Token Classification mapping
(RoFormerConfig, RoFormerForTokenClassification),
(BigBirdConfig, BigBirdForTokenClassification), (BigBirdConfig, BigBirdForTokenClassification),
(ConvBertConfig, ConvBertForTokenClassification), (ConvBertConfig, ConvBertForTokenClassification),
(LayoutLMConfig, LayoutLMForTokenClassification), (LayoutLMConfig, LayoutLMForTokenClassification),
...@@ -699,6 +716,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict( ...@@ -699,6 +716,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict( MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
[ [
# Model for Multiple Choice mapping # Model for Multiple Choice mapping
(RoFormerConfig, RoFormerForMultipleChoice),
(BigBirdConfig, BigBirdForMultipleChoice), (BigBirdConfig, BigBirdForMultipleChoice),
(ConvBertConfig, ConvBertForMultipleChoice), (ConvBertConfig, ConvBertForMultipleChoice),
(CamembertConfig, CamembertForMultipleChoice), (CamembertConfig, CamembertForMultipleChoice),
......
src/transformers/models/auto/modeling_tf_auto.py
View file @ 206f06f2
...@@ -148,6 +148,15 @@ from ..roberta.modeling_tf_roberta import ( ...@@ -148,6 +148,15 @@ from ..roberta.modeling_tf_roberta import (
TFRobertaForTokenClassification, TFRobertaForTokenClassification,
TFRobertaModel, TFRobertaModel,
) )
from ..roformer.modeling_tf_roformer import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from ..t5.modeling_tf_t5 import TFT5ForConditionalGeneration, TFT5Model from ..t5.modeling_tf_t5 import TFT5ForConditionalGeneration, TFT5Model
from ..transfo_xl.modeling_tf_transfo_xl import ( from ..transfo_xl.modeling_tf_transfo_xl import (
TFTransfoXLForSequenceClassification, TFTransfoXLForSequenceClassification,
...@@ -206,6 +215,7 @@ from .configuration_auto import ( ...@@ -206,6 +215,7 @@ from .configuration_auto import (
OpenAIGPTConfig, OpenAIGPTConfig,
PegasusConfig, PegasusConfig,
RobertaConfig, RobertaConfig,
RoFormerConfig,
T5Config, T5Config,
TransfoXLConfig, TransfoXLConfig,
XLMConfig, XLMConfig,
...@@ -220,6 +230,7 @@ logger = logging.get_logger(__name__) ...@@ -220,6 +230,7 @@ logger = logging.get_logger(__name__)
TF_MODEL_MAPPING = OrderedDict( TF_MODEL_MAPPING = OrderedDict(
[ [
# Base model mapping # Base model mapping
(RoFormerConfig, TFRoFormerModel),
(ConvBertConfig, TFConvBertModel), (ConvBertConfig, TFConvBertModel),
(LEDConfig, TFLEDModel), (LEDConfig, TFLEDModel),
(LxmertConfig, TFLxmertModel), (LxmertConfig, TFLxmertModel),
...@@ -285,6 +296,7 @@ TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict( ...@@ -285,6 +296,7 @@ TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict( TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
[ [
# Model with LM heads mapping # Model with LM heads mapping
(RoFormerConfig, TFRoFormerForMaskedLM),
(ConvBertConfig, TFConvBertForMaskedLM), (ConvBertConfig, TFConvBertForMaskedLM),
(LEDConfig, TFLEDForConditionalGeneration), (LEDConfig, TFLEDForConditionalGeneration),
(T5Config, TFT5ForConditionalGeneration), (T5Config, TFT5ForConditionalGeneration),
...@@ -315,6 +327,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict( ...@@ -315,6 +327,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict( TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
[ [
# Model for Causal LM mapping # Model for Causal LM mapping
(RoFormerConfig, TFRoFormerForCausalLM),
(BertConfig, TFBertLMHeadModel), (BertConfig, TFBertLMHeadModel),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel), (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel), (GPT2Config, TFGPT2LMHeadModel),
...@@ -331,6 +344,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict( ...@@ -331,6 +344,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict( TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
[ [
# Model for Masked LM mapping # Model for Masked LM mapping
(RoFormerConfig, TFRoFormerForMaskedLM),
(ConvBertConfig, TFConvBertForMaskedLM), (ConvBertConfig, TFConvBertForMaskedLM),
(DistilBertConfig, TFDistilBertForMaskedLM), (DistilBertConfig, TFDistilBertForMaskedLM),
(AlbertConfig, TFAlbertForMaskedLM), (AlbertConfig, TFAlbertForMaskedLM),
...@@ -368,6 +382,7 @@ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict( ...@@ -368,6 +382,7 @@ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict( TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
[ [
# Model for Sequence Classification mapping # Model for Sequence Classification mapping
(RoFormerConfig, TFRoFormerForSequenceClassification),
(ConvBertConfig, TFConvBertForSequenceClassification), (ConvBertConfig, TFConvBertForSequenceClassification),
(DistilBertConfig, TFDistilBertForSequenceClassification), (DistilBertConfig, TFDistilBertForSequenceClassification),
(AlbertConfig, TFAlbertForSequenceClassification), (AlbertConfig, TFAlbertForSequenceClassification),
...@@ -394,6 +409,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict( ...@@ -394,6 +409,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict( TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
[ [
# Model for Question Answering mapping # Model for Question Answering mapping
(RoFormerConfig, TFRoFormerForQuestionAnswering),
(ConvBertConfig, TFConvBertForQuestionAnswering), (ConvBertConfig, TFConvBertForQuestionAnswering),
(DistilBertConfig, TFDistilBertForQuestionAnswering), (DistilBertConfig, TFDistilBertForQuestionAnswering),
(AlbertConfig, TFAlbertForQuestionAnswering), (AlbertConfig, TFAlbertForQuestionAnswering),
...@@ -415,6 +431,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict( ...@@ -415,6 +431,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict( TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
[ [
# Model for Token Classification mapping # Model for Token Classification mapping
(RoFormerConfig, TFRoFormerForTokenClassification),
(ConvBertConfig, TFConvBertForTokenClassification), (ConvBertConfig, TFConvBertForTokenClassification),
(DistilBertConfig, TFDistilBertForTokenClassification), (DistilBertConfig, TFDistilBertForTokenClassification),
(AlbertConfig, TFAlbertForTokenClassification), (AlbertConfig, TFAlbertForTokenClassification),
...@@ -437,6 +454,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict( ...@@ -437,6 +454,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict( TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
[ [
# Model for Multiple Choice mapping # Model for Multiple Choice mapping
(RoFormerConfig, TFRoFormerForMultipleChoice),
(ConvBertConfig, TFConvBertForMultipleChoice), (ConvBertConfig, TFConvBertForMultipleChoice),
(CamembertConfig, TFCamembertForMultipleChoice), (CamembertConfig, TFCamembertForMultipleChoice),
(XLMConfig, TFXLMForMultipleChoice), (XLMConfig, TFXLMForMultipleChoice),
......
src/transformers/models/auto/tokenization_auto.py
View file @ 206f06f2
...@@ -51,6 +51,7 @@ from ..prophetnet.tokenization_prophetnet import ProphetNetTokenizer ...@@ -51,6 +51,7 @@ from ..prophetnet.tokenization_prophetnet import ProphetNetTokenizer
from ..rag.tokenization_rag import RagTokenizer from ..rag.tokenization_rag import RagTokenizer
from ..retribert.tokenization_retribert import RetriBertTokenizer from ..retribert.tokenization_retribert import RetriBertTokenizer
from ..roberta.tokenization_roberta import RobertaTokenizer from ..roberta.tokenization_roberta import RobertaTokenizer
from ..roformer.tokenization_roformer import RoFormerTokenizer
from ..squeezebert.tokenization_squeezebert import SqueezeBertTokenizer from ..squeezebert.tokenization_squeezebert import SqueezeBertTokenizer
from ..tapas.tokenization_tapas import TapasTokenizer from ..tapas.tokenization_tapas import TapasTokenizer
from ..transfo_xl.tokenization_transfo_xl import TransfoXLTokenizer from ..transfo_xl.tokenization_transfo_xl import TransfoXLTokenizer
...@@ -98,6 +99,7 @@ from .configuration_auto import ( ...@@ -98,6 +99,7 @@ from .configuration_auto import (
ReformerConfig, ReformerConfig,
RetriBertConfig, RetriBertConfig,
RobertaConfig, RobertaConfig,
RoFormerConfig,
Speech2TextConfig, Speech2TextConfig,
SqueezeBertConfig, SqueezeBertConfig,
T5Config, T5Config,
...@@ -228,6 +230,7 @@ logger = logging.get_logger(__name__) ...@@ -228,6 +230,7 @@ logger = logging.get_logger(__name__)
TOKENIZER_MAPPING = OrderedDict( TOKENIZER_MAPPING = OrderedDict(
[ [
(RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)), (RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
(RoFormerConfig, (RoFormerTokenizer, None)),
(T5Config, (T5Tokenizer, T5TokenizerFast)), (T5Config, (T5Tokenizer, T5TokenizerFast)),
(MT5Config, (MT5Tokenizer, MT5TokenizerFast)), (MT5Config, (MT5Tokenizer, MT5TokenizerFast)),
(MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)), (MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),
......
src/transformers/models/roformer/__init__.py 0 → 100644
View file @ 206f06f2
# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
_import_structure = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig"],
"tokenization_roformer": ["RoFormerTokenizer"],
}
if is_tokenizers_available():
_import_structure["tokenization_roformer_fast"] = ["RoFormerTokenizerFast"]
if is_torch_available():
_import_structure["modeling_roformer"] = [
"ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoFormerForCausalLM",
"RoFormerForMaskedLM",
"RoFormerForMultipleChoice",
"RoFormerForQuestionAnswering",
"RoFormerForSequenceClassification",
"RoFormerForTokenClassification",
"RoFormerLayer",
"RoFormerModel",
"RoFormerPreTrainedModel",
"load_tf_weights_in_roformer",
]
if is_tf_available():
_import_structure["modeling_tf_roformer"] = [
"TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRoFormerForCausalLM",
"TFRoFormerForMaskedLM",
"TFRoFormerForMultipleChoice",
"TFRoFormerForQuestionAnswering",
"TFRoFormerForSequenceClassification",
"TFRoFormerForTokenClassification",
"TFRoFormerLayer",
"TFRoFormerModel",
"TFRoFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig
from .tokenization_roformer import RoFormerTokenizer
if is_tokenizers_available():
from .tokenization_roformer_fast import RoFormerTokenizerFast
if is_torch_available():
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
if is_tf_available():
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
else:
import importlib
import os
import sys
class _LazyModule(_BaseLazyModule):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
__file__ = globals()["__file__"]
__path__ = [os.path.dirname(__file__)]
def _get_module(self, module_name: str):
return importlib.import_module("." + module_name, self.__name__)
sys.modules[__name__] = _LazyModule(__name__, _import_structure)
src/transformers/models/roformer/configuration_roformer.py 0 → 100644
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" RoFormer model configuration """
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json"
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class RoFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a :class:`~transformers.RoFormerModel`. It is used to
instantiate an RoFormer model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defaults will yield a similar configuration to that of the RoFormer
`junnyu/roformer_chinese_base <https://huggingface.co/junnyu/roformer_chinese_base>`__ architecture.
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
Args:
vocab_size (:obj:`int`, `optional`, defaults to 50000):
Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by
the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel` or
:class:`~transformers.TFRoFormerModel`.
embedding_size (:obj:`int`, `optional`, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
hidden_size (:obj:`int`, `optional`, defaults to 768):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string,
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (:obj:`int`, `optional`, defaults to 1536):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 1536).
type_vocab_size (:obj:`int`, `optional`, defaults to 2):
The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.RoFormerModel`
or :class:`~transformers.TFRoFormerModel`.
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
The epsilon used by the layer normalization layers.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if ``config.is_decoder=True``.
rotary_value (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not apply rotary position embeddings on value layer.
gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
Example::
>>> from transformers import RoFormerModel, RoFormerConfig
>>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration
>>> configuration = RoFormerConfig()
>>> # Initializing a model from the junnyu/roformer_chinese_base style configuration
>>> model = RoFormerModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
"""
model_type = "roformer"
def __init__(
self,
vocab_size=50000,
embedding_size=768,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=1536,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
pad_token_id=0,
gradient_checkpointing=False,
rotary_value=False,
use_cache=True,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.gradient_checkpointing = gradient_checkpointing
self.rotary_value = rotary_value
self.use_cache = use_cache
src/transformers/models/roformer/convert_roformer_original_tf_checkpoint_to_pytorch.py 0 → 100755
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert RoFormer checkpoint."""
import argparse
import torch
from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = RoFormerConfig.from_json_file(bert_config_file)
print(f"Building PyTorch model from configuration: {config}")
model = RoFormerForMaskedLM(config)
# Load weights from tf checkpoint
load_tf_weights_in_roformer(model, config, tf_checkpoint_path)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict(), pytorch_dump_path, _use_new_zipfile_serialization=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--bert_config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
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src/transformers/models/roformer/tokenization_roformer.py 0 → 100644
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for RoFormer."""
import collections
import os
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..bert.tokenization_bert import BasicTokenizer, WordpieceTokenizer, load_vocab
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
PRETRAINED_INIT_CONFIGURATION = {
"junnyu/roformer_chinese_small": {"do_lower_case": True},
"junnyu/roformer_chinese_base": {"do_lower_case": True},
}
class RoFormerTokenizer(PreTrainedTokenizer):
r"""
Construct a RoFormer tokenizer. Based on `Rust Jieba <https://pypi.org/project/rjieba/>`.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (:obj:`str`):
File containing the vocabulary.
do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to do basic tokenization before WordPiece.
never_split (:obj:`Iterable`, `optional`):
Collection of tokens which will never be split during tokenization. Only has an effect when
:obj:`do_basic_tokenize=True`
unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`):
The token used for padding, for example when batching sequences of different lengths.
cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
Whether or not to tokenize Chinese characters.
This should likely be deactivated for Japanese (see this `issue
<https://github.com/huggingface/transformers/issues/328>`__).
strip_accents: (:obj:`bool`, `optional`):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for :obj:`lowercase` (as in the original BERT).
Example::
>>> from transformers import RoFormerTokenizer
>>> tokenizer = RoFormerTokenizer.from_pretrained('junnyu/roformer_chinese_base')
>>> tokenizer.tokenize("今天天气非常好。")
# ['今', '天', '天', '气', '非常', '好', '。']
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
"model use `tokenizer = RoFormerTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.vocab = load_vocab(vocab_file)
self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
self.do_basic_tokenize = do_basic_tokenize
if do_basic_tokenize:
self.basic_tokenizer = BasicTokenizer(
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
try:
import rjieba
except ImportError:
raise ImportError(
"You need to install rjieba to use RoFormerTokenizer."
"See https://pypi.org/project/rjieba/ for installation."
)
self.jieba = rjieba
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def __getstate__(self):
state = self.__dict__.copy()
state["jieba"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
try:
import rjieba
except ImportError:
raise ImportError(
"You need to install rjieba to use RoFormerTokenizer."
"See https://pypi.org/project/rjieba/ for installation."
)
self.jieba = rjieba
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text, use_jieba=True):
split_tokens = []
if use_jieba:
for wholword in self.jieba.cut(text, False):
if wholword in self.vocab:
split_tokens.append(wholword)
else:
# use bert tokenizer to _tokenize
char_list = self._tokenize(wholword, use_jieba=False)
split_tokens.extend(char_list)
else:
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
# If the token is part of the never_split set
if token in self.basic_tokenizer.never_split:
split_tokens.append(token)
else:
split_tokens += self.wordpiece_tokenizer.tokenize(token)
else:
split_tokens = self.wordpiece_tokenizer.tokenize(text)
return split_tokens
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
else:
vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(token + "\n")
index += 1
return (vocab_file,)
src/transformers/models/roformer/tokenization_roformer_fast.py 0 → 100644
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for RoFormer."""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
"junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
PRETRAINED_INIT_CONFIGURATION = {
"junnyu/roformer_chinese_small": {"do_lower_case": True},
"junnyu/roformer_chinese_base": {"do_lower_case": True},
}
class RoFormerTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" RoFormer tokenizer (backed by HuggingFace's `tokenizers` library).
:class:`~transformers.RoFormerTokenizerFast` is almost identical to :class:`~transformers.BertTokenizerFast` and
runs end-to-end tokenization: punctuation splitting and wordpiece. There are some difference between them when
tokenizing Chinese.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
methods. Users should refer to this superclass for more information regarding those methods.
Example::
>>> from transformers import RoFormerTokenizerFast
>>> tokenizer = RoFormerTokenizerFast.from_pretrained('junnyu/roformer_chinese_base')
>>> tokenizer.tokenize("今天天气非常好。")
# ['今', '天', '天', '气', '非常', '好', '。']
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
slow_tokenizer_class = RoFormerTokenizer
def __init__(
self,
vocab_file,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
super().__init__(
vocab_file,
tokenizer_file=tokenizer_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
):
pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
pre_tok_state["lowercase"] = do_lower_case
pre_tok_state["strip_accents"] = strip_accents
self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
self.do_lower_case = do_lower_case
def __getstate__(self):
state = self.__dict__.copy()
state["_tokenizer"].pre_tokenizer = BertPreTokenizer()
return state
def __setstate__(self, d):
self.__dict__ = d
vocab = self.__dict__["_tokenizer"].get_vocab()
self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab))
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
if token_ids_1:
output += token_ids_1 + [self.sep_token_id]
return output
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files)
def save_pretrained(
self,
save_directory,
legacy_format=None,
filename_prefix=None,
push_to_hub=False,
**kwargs,
):
self.backend_tokenizer.pre_tokenizer = BertPreTokenizer()
return super().save_pretrained(save_directory, legacy_format, filename_prefix, push_to_hub, **kwargs)
src/transformers/models/roformer/tokenization_utils.py 0 → 100644
View file @ 206f06f2
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization utils for RoFormer."""
from typing import List
from tokenizers import NormalizedString, PreTokenizedString, normalizers
class JiebaPreTokenizer:
def __init__(self, vocab) -> None:
self.vocab = vocab
self.normalizers = normalizers.BertNormalizer(
clean_text=False,
handle_chinese_chars=True,
strip_accents=False,
lowercase=False,
)
try:
import rjieba
except ImportError:
raise ImportError(
"You need to install rjieba to use RoFormerTokenizer."
"See https://pypi.org/project/rjieba/ for installation."
)
self.jieba = rjieba
def jieba_split(self, i: int, normalized_string: NormalizedString) -> List[NormalizedString]:
splits = []
# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
# for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
# if token in self.vocab:
# splits.append(normalized_string.slice((start, end)))
# else:
# token_list = self.normalizers.normalize_str(token).split()
# for token in token_list:
# if token:
# end = start + len(token)
# splits.append(normalized_string.slice((start, end)))
# start = end
# this code test_alignement_methods can't pass but fast (300ms)
for token in self.jieba.cut(str(normalized_string), False):
if token in self.vocab:
splits.append(NormalizedString(token))
else:
token_list = self.normalizers.normalize_str(token).split()
for token in token_list:
if token:
splits.append(NormalizedString(token))
return splits
def pre_tokenize(self, pretok: PreTokenizedString):
pretok.split(self.jieba_split)
src/transformers/utils/dummy_pt_objects.py
View file @ 206f06f2
...@@ -2553,6 +2553,86 @@ class RobertaModel: ...@@ -2553,6 +2553,86 @@ class RobertaModel:
requires_backends(self, ["torch"]) requires_backends(self, ["torch"])
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
class RoFormerForCausalLM:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerForMaskedLM:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerForMultipleChoice:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerForQuestionAnswering:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerForSequenceClassification:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerForTokenClassification:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerLayer:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerModel:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
class RoFormerPreTrainedModel:
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["torch"])
def load_tf_weights_in_roformer(*args, **kwargs):
requires_backends(load_tf_weights_in_roformer, ["torch"])
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None
......
src/transformers/utils/dummy_tf_objects.py
View file @ 206f06f2
...@@ -1479,6 +1479,82 @@ class TFRobertaPreTrainedModel: ...@@ -1479,6 +1479,82 @@ class TFRobertaPreTrainedModel:
requires_backends(self, ["tf"]) requires_backends(self, ["tf"])
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
class TFRoFormerForCausalLM:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerForMaskedLM:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerForMultipleChoice:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerForQuestionAnswering:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerForSequenceClassification:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerForTokenClassification:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerLayer:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerModel:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
class TFRoFormerPreTrainedModel:
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_backends(self, ["tf"])
TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = None TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = None
......
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