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dilbert -> distilbert
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thomwolf
parent
c9bce1811c
commit
912a377e90
@ -13,7 +13,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
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5. **[XLNet](https://github.com/zihangdai/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 Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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6. **[XLM](https://github.com/facebookresearch/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.
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7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/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 Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
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8. **[DilBERT](https://github.com/huggingface/pytorch-transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [Smaller, faster, cheaper, lighter: Introducing DilBERT, a distilled version of BERT](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-dilbert-a-distilled-version-of-bert-8cf3380435b5
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8. **[DistilBERT](https://github.com/huggingface/pytorch-transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
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) by Victor Sanh, Lysandre Debut and Thomas Wolf.
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These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/pytorch-transformers/examples.html).
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@ -1,33 +1,33 @@
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# DilBERT
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# DistilBERT
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This folder contains the original code used to train DilBERT as well as examples showcasing how to use DilBERT.
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This folder contains the original code used to train DistilBERT as well as examples showcasing how to use DistilBERT.
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## What is DilBERT
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## What is DistilBERT
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DilBERT stands for Distillated-BERT. DilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on the GLUE language understanding benchmark. DilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
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DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
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For more information on DilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-dilbert-a-distilled-version-of-bert-8cf3380435b5
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For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
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).
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## How to use DilBERT
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## How to use DistilBERT
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PyTorch-Transformers includes two pre-trained DilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DilBERT):
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PyTorch-Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
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- `dilbert-base-uncased`: DilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
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- `dilbert-base-uncased-distilled-squad`: A finetuned version of `dilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.2 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
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- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
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- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.2 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
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Using DilBERT is very similar to using BERT. DilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DilBertTokenizer` name to have a consistent naming between the library models.
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Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
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```python
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tokenizer = DilBertTokenizer.from_pretrained('dilbert-base-uncased')
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model = DilBertModel.from_pretrained('dilbert-base-uncased')
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tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
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model = DistilBertModel.from_pretrained('distilbert-base-uncased')
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input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)
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outputs = model(input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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```
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## How to train DilBERT
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## How to train DistilBERT
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In the following, we will explain how you can train your own compressed model.
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@ -68,7 +68,7 @@ python train.py \
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By default, this will launch a training on a single GPU (even if more are available on the cluster). Other parameters are available in the command line, please look in `train.py` or run `python train.py --help` to list them.
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We highly encourage you to distributed training for training DilBert as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
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We highly encourage you to distributed training for training DistilBert as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
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```bash
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export NODE_RANK=0
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Dataloaders to train DilBERT.
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Dataloaders to train DistilBERT.
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"""
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from typing import List
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import math
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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The distiller to distil DilBERT.
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The distiller to distil DistilBERT.
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"""
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import os
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import math
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocessing script before training DilBERT.
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Preprocessing script before training DistilBERT.
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"""
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import argparse
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import pickle
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocessing script before training DilBERT.
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Preprocessing script before training DistilBERT.
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"""
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from pytorch_transformers import BertForPreTraining
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import torch
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@ -33,32 +33,32 @@ if __name__ == '__main__':
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compressed_sd = {}
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for w in ['word_embeddings', 'position_embeddings']:
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compressed_sd[f'dilbert.embeddings.{w}.weight'] = \
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compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
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state_dict[f'bert.embeddings.{w}.weight']
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for w in ['weight', 'bias']:
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compressed_sd[f'dilbert.embeddings.LayerNorm.{w}'] = \
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compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
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state_dict[f'bert.embeddings.LayerNorm.{w}']
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std_idx = 0
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for teacher_idx in [0, 2, 4, 7, 9, 11]:
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for w in ['weight', 'bias']:
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.query.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.key.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.value.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.output.dense.{w}']
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compressed_sd[f'dilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
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compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
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state_dict[f'bert.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
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std_idx += 1
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Preprocessing script before training DilBERT.
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Preprocessing script before training DistilBERT.
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"""
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from collections import Counter
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import argparse
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Training DilBERT.
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Training DistilBERT.
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"""
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import os
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import argparse
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@ -24,7 +24,7 @@ import numpy as np
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import torch
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from pytorch_transformers import BertTokenizer, BertForMaskedLM
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from pytorch_transformers import DilBertForMaskedLM, DilBertConfig
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from pytorch_transformers import DistilBertForMaskedLM, DistilBertConfig
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from distiller import Distiller
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from utils import git_log, logger, init_gpu_params, set_seed
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@ -201,13 +201,13 @@ def main():
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assert os.path.isfile(os.path.join(args.from_pretrained_config))
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logger.info(f'Loading pretrained weights from {args.from_pretrained_weights}')
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logger.info(f'Loading pretrained config from {args.from_pretrained_config}')
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stu_architecture_config = DilBertConfig.from_json_file(args.from_pretrained_config)
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student = DilBertForMaskedLM.from_pretrained(args.from_pretrained_weights,
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stu_architecture_config = DistilBertConfig.from_json_file(args.from_pretrained_config)
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student = DistilBertForMaskedLM.from_pretrained(args.from_pretrained_weights,
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config=stu_architecture_config)
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else:
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args.vocab_size_or_config_json_file = args.vocab_size
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stu_architecture_config = DilBertConfig(**vars(args))
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student = DilBertForMaskedLM(stu_architecture_config)
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stu_architecture_config = DistilBertConfig(**vars(args))
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student = DistilBertForMaskedLM(stu_architecture_config)
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if args.n_gpu > 0:
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Utils to train DilBERT.
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Utils to train DistilBERT.
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"""
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import git
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import json
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@ -7,7 +7,7 @@ from .tokenization_gpt2 import GPT2Tokenizer
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from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
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from .tokenization_xlm import XLMTokenizer
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from .tokenization_roberta import RobertaTokenizer
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from .tokenization_dilbert import DilBertTokenizer
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from .tokenization_distilbert import DistilBertTokenizer
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from .tokenization_utils import (PreTrainedTokenizer)
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@ -41,9 +41,9 @@ from .modeling_xlm import (XLMConfig, XLMPreTrainedModel , XLMModel,
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XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_roberta import (RobertaConfig, RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_dilbert import (DilBertConfig, DilBertForMaskedLM, DilBertModel,
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DilBertForSequenceClassification, DilBertForQuestionAnswering,
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DILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_distilbert import (DistilBertConfig, DistilBertForMaskedLM, DistilBertModel,
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DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
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from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
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PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
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@ -30,7 +30,7 @@ from .modeling_transfo_xl import TransfoXLConfig, TransfoXLModel
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from .modeling_xlnet import XLNetConfig, XLNetModel
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from .modeling_xlm import XLMConfig, XLMModel
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from .modeling_roberta import RobertaConfig, RobertaModel
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from .modeling_dilbert import DilBertConfig, DilBertModel
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from .modeling_distilbert import DistilBertConfig, DistilBertModel
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from .modeling_utils import PreTrainedModel, SequenceSummary
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@ -111,8 +111,8 @@ class AutoConfig(object):
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assert unused_kwargs == {'foo': False}
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"""
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if 'dilbert' in pretrained_model_name_or_path:
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return DilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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if 'distilbert' in pretrained_model_name_or_path:
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return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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@ -228,8 +228,8 @@ class AutoModel(object):
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model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
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"""
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if 'dilbert' in pretrained_model_name_or_path:
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return DilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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if 'distilbert' in pretrained_model_name_or_path:
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return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'roberta' in pretrained_model_name_or_path:
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return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
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elif 'bert' in pretrained_model_name_or_path:
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@ -13,7 +13,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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PyTorch DilBERT model.
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PyTorch DistilBERT model.
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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@ -36,19 +36,19 @@ import logging
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logger = logging.getLogger(__name__)
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DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'dilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/dilbert-base-uncased-pytorch_model.bin",
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'dilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/dilbert-base-uncased-distilled-squad-pytorch_model.bin"
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
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'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin",
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'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin"
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}
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DILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'dilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/dilbert-base-uncased-config.json",
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'dilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/dilbert-base-uncased-distilled-squad-config.json"
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
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'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
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}
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class DilBertConfig(PretrainedConfig):
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pretrained_config_archive_map = DILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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class DistilBertConfig(PretrainedConfig):
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pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=30522,
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@ -66,7 +66,7 @@ class DilBertConfig(PretrainedConfig):
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qa_dropout=0.1,
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seq_classif_dropout=0.2,
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**kwargs):
|
||||
super(DilBertConfig, self).__init__(**kwargs)
|
||||
super(DistilBertConfig, self).__init__(**kwargs)
|
||||
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
@ -398,17 +398,17 @@ class Transformer(nn.Module):
|
||||
|
||||
|
||||
### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
|
||||
class DilBertPreTrainedModel(PreTrainedModel):
|
||||
class DistilBertPreTrainedModel(PreTrainedModel):
|
||||
""" An abstract class to handle weights initialization and
|
||||
a simple interface for downloading and loading pretrained models.
|
||||
"""
|
||||
config_class = DilBertConfig
|
||||
pretrained_model_archive_map = DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
config_class = DistilBertConfig
|
||||
pretrained_model_archive_map = DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
load_tf_weights = None
|
||||
base_model_prefix = "dilbert"
|
||||
base_model_prefix = "distilbert"
|
||||
|
||||
def __init__(self, *inputs, **kwargs):
|
||||
super(DilBertPreTrainedModel, self).__init__(*inputs, **kwargs)
|
||||
super(DistilBertPreTrainedModel, self).__init__(*inputs, **kwargs)
|
||||
|
||||
def init_weights(self, module):
|
||||
""" Initialize the weights.
|
||||
@ -425,36 +425,36 @@ class DilBertPreTrainedModel(PreTrainedModel):
|
||||
module.bias.data.zero_()
|
||||
|
||||
|
||||
DILBERT_START_DOCSTRING = r"""
|
||||
DilBERT is a small, fast, cheap and light Transformer model
|
||||
DISTILBERT_START_DOCSTRING = r"""
|
||||
DistilBERT is a small, fast, cheap and light Transformer model
|
||||
trained by distilling Bert base. It has 40% less parameters than
|
||||
`bert-base-uncased`, runs 60% faster while preserving over 95% of
|
||||
Bert's performances as measured on the GLUE language understanding benchmark.
|
||||
|
||||
Here are the differences between the interface of Bert and DilBert:
|
||||
Here are the differences between the interface of Bert and DistilBert:
|
||||
|
||||
- DilBert doesn't have `token_type_ids`, you don't need to indicate which token belong to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
|
||||
- DilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
|
||||
- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belong to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
|
||||
- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
|
||||
|
||||
For more information on DilBERT, please refer to our
|
||||
For more information on DistilBERT, please refer to our
|
||||
`detailed blog post`_
|
||||
|
||||
.. _`detailed blog post`:
|
||||
https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-dilbert-a-distilled-version-of-bert-8cf3380435b5
|
||||
https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
|
||||
|
||||
Parameters:
|
||||
config (:class:`~pytorch_transformers.DilBertConfig`): Model configuration class with all the parameters of the model.
|
||||
config (:class:`~pytorch_transformers.DistilBertConfig`): Model configuration class with all the parameters of the model.
|
||||
Initializing with a config file does not load the weights associated with the model, only the configuration.
|
||||
Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
|
||||
"""
|
||||
|
||||
DILBERT_INPUTS_DOCSTRING = r"""
|
||||
DISTILBERT_INPUTS_DOCSTRING = r"""
|
||||
Inputs:
|
||||
**input_ids**L ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Indices oof input sequence tokens in the vocabulary.
|
||||
The input sequences should start with `[CLS]` and `[SEP]` tokens.
|
||||
|
||||
For now, ONLY BertTokenizer(`bert-base-uncased`) is supported and you should use this tokenizer when using DilBERT.
|
||||
For now, ONLY BertTokenizer(`bert-base-uncased`) is supported and you should use this tokenizer when using DistilBERT.
|
||||
**attention_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Mask to avoid performing attention on padding token indices.
|
||||
Mask values selected in ``[0, 1]``:
|
||||
@ -465,9 +465,9 @@ DILBERT_INPUTS_DOCSTRING = r"""
|
||||
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
|
||||
"""
|
||||
|
||||
@add_start_docstrings("The bare DilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
|
||||
DILBERT_START_DOCSTRING, DILBERT_INPUTS_DOCSTRING)
|
||||
class DilBertModel(DilBertPreTrainedModel):
|
||||
@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
|
||||
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
|
||||
class DistilBertModel(DistilBertPreTrainedModel):
|
||||
r"""
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
|
||||
@ -482,15 +482,15 @@ class DilBertModel(DilBertPreTrainedModel):
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = DilBertTokenizer.from_pretrained('dilbert-base-uncased')
|
||||
model = DilBertModel.from_pretrained('dilbert-base-uncased')
|
||||
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
|
||||
model = DistilBertModel.from_pretrained('distilbert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
|
||||
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(DilBertModel, self).__init__(config)
|
||||
super(DistilBertModel, self).__init__(config)
|
||||
|
||||
self.embeddings = Embeddings(config) # Embeddings
|
||||
self.transformer = Transformer(config) # Encoder
|
||||
@ -543,9 +543,9 @@ class DilBertModel(DilBertPreTrainedModel):
|
||||
return output # last-layer hidden-state, (all hidden_states), (all attentions)
|
||||
|
||||
|
||||
@add_start_docstrings("""DilBert Model with a `masked language modeling` head on top. """,
|
||||
DILBERT_START_DOCSTRING, DILBERT_INPUTS_DOCSTRING)
|
||||
class DilBertForMaskedLM(DilBertPreTrainedModel):
|
||||
@add_start_docstrings("""DistilBert Model with a `masked language modeling` head on top. """,
|
||||
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
|
||||
class DistilBertForMaskedLM(DistilBertPreTrainedModel):
|
||||
r"""
|
||||
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Labels for computing the masked language modeling loss.
|
||||
@ -568,19 +568,19 @@ class DilBertForMaskedLM(DilBertPreTrainedModel):
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = DilBertTokenizer.from_pretrained('dilbert-base-uncased')
|
||||
model = DilBertForMaskedLM.from_pretrained('dilbert-base-uncased')
|
||||
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
|
||||
model = DistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, masked_lm_labels=input_ids)
|
||||
loss, prediction_scores = outputs[:2]
|
||||
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(DilBertForMaskedLM, self).__init__(config)
|
||||
super(DistilBertForMaskedLM, self).__init__(config)
|
||||
self.output_attentions = config.output_attentions
|
||||
self.output_hidden_states = config.output_hidden_states
|
||||
|
||||
self.dilbert = DilBertModel(config)
|
||||
self.distilbert = DistilBertModel(config)
|
||||
self.vocab_transform = nn.Linear(config.dim, config.dim)
|
||||
self.vocab_layer_norm = nn.LayerNorm(config.dim, eps=1e-12)
|
||||
self.vocab_projector = nn.Linear(config.dim, config.vocab_size)
|
||||
@ -595,14 +595,14 @@ class DilBertForMaskedLM(DilBertPreTrainedModel):
|
||||
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
|
||||
"""
|
||||
self._tie_or_clone_weights(self.vocab_projector,
|
||||
self.dilbert.embeddings.word_embeddings)
|
||||
self.distilbert.embeddings.word_embeddings)
|
||||
|
||||
def forward(self,
|
||||
input_ids: torch.tensor,
|
||||
attention_mask: torch.tensor = None,
|
||||
masked_lm_labels: torch.tensor = None,
|
||||
head_mask: torch.tensor = None):
|
||||
dlbrt_output = self.dilbert(input_ids=input_ids,
|
||||
dlbrt_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask)
|
||||
hidden_states = dlbrt_output[0] # (bs, seq_length, dim)
|
||||
@ -620,10 +620,10 @@ class DilBertForMaskedLM(DilBertPreTrainedModel):
|
||||
return outputs # (mlm_loss), prediction_logits, (all hidden_states), (all attentions)
|
||||
|
||||
|
||||
@add_start_docstrings("""DilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
|
||||
@add_start_docstrings("""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
|
||||
the pooled output) e.g. for GLUE tasks. """,
|
||||
DILBERT_START_DOCSTRING, DILBERT_INPUTS_DOCSTRING)
|
||||
class DilBertForSequenceClassification(DilBertPreTrainedModel):
|
||||
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
|
||||
class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
|
||||
r"""
|
||||
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
||||
Labels for computing the sequence classification/regression loss.
|
||||
@ -646,8 +646,8 @@ class DilBertForSequenceClassification(DilBertPreTrainedModel):
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = DilBertTokenizer.from_pretrained('dilbert-base-uncased')
|
||||
model = DilBertForSequenceClassification.from_pretrained('dilbert-base-uncased')
|
||||
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
|
||||
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
@ -655,10 +655,10 @@ class DilBertForSequenceClassification(DilBertPreTrainedModel):
|
||||
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(DilBertForSequenceClassification, self).__init__(config)
|
||||
super(DistilBertForSequenceClassification, self).__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.dilbert = DilBertModel(config)
|
||||
self.distilbert = DistilBertModel(config)
|
||||
self.pre_classifier = nn.Linear(config.dim, config.dim)
|
||||
self.classifier = nn.Linear(config.dim, config.num_labels)
|
||||
self.dropout = nn.Dropout(config.seq_classif_dropout)
|
||||
@ -670,17 +670,17 @@ class DilBertForSequenceClassification(DilBertPreTrainedModel):
|
||||
attention_mask: torch.tensor = None,
|
||||
labels: torch.tensor = None,
|
||||
head_mask: torch.tensor = None):
|
||||
dilbert_output = self.dilbert(input_ids=input_ids,
|
||||
distilbert_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask)
|
||||
hidden_state = dilbert_output[0] # (bs, seq_len, dim)
|
||||
hidden_state = distilbert_output[0] # (bs, seq_len, dim)
|
||||
pooled_output = hidden_state[:, 0] # (bs, dim)
|
||||
pooled_output = self.pre_classifier(pooled_output) # (bs, dim)
|
||||
pooled_output = nn.ReLU()(pooled_output) # (bs, dim)
|
||||
pooled_output = self.dropout(pooled_output) # (bs, dim)
|
||||
logits = self.classifier(pooled_output) # (bs, dim)
|
||||
|
||||
outputs = (logits,) + dilbert_output[1:]
|
||||
outputs = (logits,) + distilbert_output[1:]
|
||||
if labels is not None:
|
||||
if self.num_labels == 1:
|
||||
loss_fct = nn.MSELoss()
|
||||
@ -693,10 +693,10 @@ class DilBertForSequenceClassification(DilBertPreTrainedModel):
|
||||
return outputs # (loss), logits, (hidden_states), (attentions)
|
||||
|
||||
|
||||
@add_start_docstrings("""DilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
|
||||
@add_start_docstrings("""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
|
||||
the hidden-states output to compute `span start logits` and `span end logits`). """,
|
||||
DILBERT_START_DOCSTRING, DILBERT_INPUTS_DOCSTRING)
|
||||
class DilBertForQuestionAnswering(DilBertPreTrainedModel):
|
||||
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
|
||||
class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
|
||||
r"""
|
||||
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
||||
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
||||
@ -724,8 +724,8 @@ class DilBertForQuestionAnswering(DilBertPreTrainedModel):
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = DilBertTokenizer.from_pretrained('dilbert-base-uncased')
|
||||
model = DilBertForQuestionAnswering.from_pretrained('dilbert-base-uncased')
|
||||
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
|
||||
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
start_positions = torch.tensor([1])
|
||||
end_positions = torch.tensor([3])
|
||||
@ -734,9 +734,9 @@ class DilBertForQuestionAnswering(DilBertPreTrainedModel):
|
||||
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(DilBertForQuestionAnswering, self).__init__(config)
|
||||
super(DistilBertForQuestionAnswering, self).__init__(config)
|
||||
|
||||
self.dilbert = DilBertModel(config)
|
||||
self.distilbert = DistilBertModel(config)
|
||||
self.qa_outputs = nn.Linear(config.dim, config.num_labels)
|
||||
assert config.num_labels == 2
|
||||
self.dropout = nn.Dropout(config.qa_dropout)
|
||||
@ -749,10 +749,10 @@ class DilBertForQuestionAnswering(DilBertPreTrainedModel):
|
||||
start_positions: torch.tensor = None,
|
||||
end_positions: torch.tensor = None,
|
||||
head_mask: torch.tensor = None):
|
||||
dilbert_output = self.dilbert(input_ids=input_ids,
|
||||
distilbert_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask)
|
||||
hidden_states = dilbert_output[0] # (bs, max_query_len, dim)
|
||||
hidden_states = distilbert_output[0] # (bs, max_query_len, dim)
|
||||
|
||||
hidden_states = self.dropout(hidden_states) # (bs, max_query_len, dim)
|
||||
logits = self.qa_outputs(hidden_states) # (bs, max_query_len, 2)
|
||||
@ -760,7 +760,7 @@ class DilBertForQuestionAnswering(DilBertPreTrainedModel):
|
||||
start_logits = start_logits.squeeze(-1) # (bs, max_query_len)
|
||||
end_logits = end_logits.squeeze(-1) # (bs, max_query_len)
|
||||
|
||||
outputs = (start_logits, end_logits,) + dilbert_output[1:]
|
||||
outputs = (start_logits, end_logits,) + distilbert_output[1:]
|
||||
if start_positions is not None and end_positions is not None:
|
||||
# If we are on multi-GPU, split add a dimension
|
||||
if len(start_positions.size()) > 1:
|
||||
@ -20,23 +20,23 @@ import unittest
|
||||
import shutil
|
||||
import pytest
|
||||
|
||||
from pytorch_transformers import (DilBertConfig, DilBertModel, DilBertForMaskedLM,
|
||||
DilBertForQuestionAnswering, DilBertForSequenceClassification)
|
||||
from pytorch_transformers.modeling_dilbert import DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
from pytorch_transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
|
||||
DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
|
||||
from pytorch_transformers.modeling_distilbert import DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
|
||||
from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
|
||||
|
||||
|
||||
class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
class DistilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
|
||||
all_model_classes = (DilBertModel, DilBertForMaskedLM, DilBertForQuestionAnswering,
|
||||
DilBertForSequenceClassification)
|
||||
all_model_classes = (DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering,
|
||||
DistilBertForSequenceClassification)
|
||||
test_pruning = True
|
||||
test_torchscript = True
|
||||
test_resize_embeddings = True
|
||||
test_head_masking = True
|
||||
|
||||
class DilBertModelTester(object):
|
||||
class DistilBertModelTester(object):
|
||||
|
||||
def __init__(self,
|
||||
parent,
|
||||
@ -100,7 +100,7 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
|
||||
choice_labels = ids_tensor([self.batch_size], self.num_choices)
|
||||
|
||||
config = DilBertConfig(
|
||||
config = DistilBertConfig(
|
||||
vocab_size_or_config_json_file=self.vocab_size,
|
||||
dim=self.hidden_size,
|
||||
n_layers=self.num_hidden_layers,
|
||||
@ -119,8 +119,8 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
list(result["loss"].size()),
|
||||
[])
|
||||
|
||||
def create_and_check_dilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = DilBertModel(config=config)
|
||||
def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = DistilBertModel(config=config)
|
||||
model.eval()
|
||||
(sequence_output,) = model(input_ids, input_mask)
|
||||
(sequence_output,) = model(input_ids)
|
||||
@ -132,8 +132,8 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
list(result["sequence_output"].size()),
|
||||
[self.batch_size, self.seq_length, self.hidden_size])
|
||||
|
||||
def create_and_check_dilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = DilBertForMaskedLM(config=config)
|
||||
def create_and_check_distilbert_for_masked_lm(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = DistilBertForMaskedLM(config=config)
|
||||
model.eval()
|
||||
loss, prediction_scores = model(input_ids, attention_mask=input_mask, masked_lm_labels=token_labels)
|
||||
result = {
|
||||
@ -145,8 +145,8 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
[self.batch_size, self.seq_length, self.vocab_size])
|
||||
self.check_loss_output(result)
|
||||
|
||||
def create_and_check_dilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
||||
model = DilBertForQuestionAnswering(config=config)
|
||||
def create_and_check_distilbert_for_question_answering(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
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model = DistilBertForQuestionAnswering(config=config)
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model.eval()
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loss, start_logits, end_logits = model(input_ids, input_mask, sequence_labels, sequence_labels)
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result = {
|
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@ -162,9 +162,9 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
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[self.batch_size, self.seq_length])
|
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self.check_loss_output(result)
|
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|
||||
def create_and_check_dilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
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def create_and_check_distilbert_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
|
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config.num_labels = self.num_labels
|
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model = DilBertForSequenceClassification(config)
|
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model = DistilBertForSequenceClassification(config)
|
||||
model.eval()
|
||||
loss, logits = model(input_ids, input_mask, sequence_labels)
|
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result = {
|
||||
@ -183,33 +183,33 @@ class DilBertModelTest(CommonTestCases.CommonModelTester):
|
||||
return config, inputs_dict
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = DilBertModelTest.DilBertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=DilBertConfig, dim=37)
|
||||
self.model_tester = DistilBertModelTest.DistilBertModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_dilbert_model(self):
|
||||
def test_distilbert_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_dilbert_model(*config_and_inputs)
|
||||
self.model_tester.create_and_check_distilbert_model(*config_and_inputs)
|
||||
|
||||
def test_for_masked_lm(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_dilbert_for_masked_lm(*config_and_inputs)
|
||||
self.model_tester.create_and_check_distilbert_for_masked_lm(*config_and_inputs)
|
||||
|
||||
def test_for_question_answering(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_dilbert_for_question_answering(*config_and_inputs)
|
||||
self.model_tester.create_and_check_distilbert_for_question_answering(*config_and_inputs)
|
||||
|
||||
def test_for_sequence_classification(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_dilbert_for_sequence_classification(*config_and_inputs)
|
||||
self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
|
||||
|
||||
# @pytest.mark.slow
|
||||
# def test_model_from_pretrained(self):
|
||||
# cache_dir = "/tmp/pytorch_transformers_test/"
|
||||
# for model_name in list(DILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
# model = DilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
|
||||
# for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
|
||||
# model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
|
||||
# shutil.rmtree(cache_dir)
|
||||
# self.assertIsNotNone(model)
|
||||
|
||||
|
||||
@ -18,20 +18,20 @@ import os
|
||||
import unittest
|
||||
from io import open
|
||||
|
||||
from pytorch_transformers.tokenization_dilbert import (DilBertTokenizer)
|
||||
from pytorch_transformers.tokenization_distilbert import (DistilBertTokenizer)
|
||||
|
||||
from .tokenization_tests_commons import CommonTestCases
|
||||
from .tokenization_bert_test import BertTokenizationTest
|
||||
|
||||
class DilBertTokenizationTest(BertTokenizationTest):
|
||||
class DistilBertTokenizationTest(BertTokenizationTest):
|
||||
|
||||
tokenizer_class = DilBertTokenizer
|
||||
tokenizer_class = DistilBertTokenizer
|
||||
|
||||
def get_tokenizer(self):
|
||||
return DilBertTokenizer.from_pretrained(self.tmpdirname)
|
||||
return DistilBertTokenizer.from_pretrained(self.tmpdirname)
|
||||
|
||||
def test_sequence_builders(self):
|
||||
tokenizer = DilBertTokenizer.from_pretrained("dilbert-base-uncased")
|
||||
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
text = tokenizer.encode("sequence builders")
|
||||
text_2 = tokenizer.encode("multi-sequence build")
|
||||
|
||||
@ -12,7 +12,7 @@
|
||||
# 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 DilBERT."""
|
||||
"""Tokenization classes for DistilBERT."""
|
||||
|
||||
from __future__ import absolute_import, division, print_function, unicode_literals
|
||||
|
||||
@ -31,21 +31,21 @@ VOCAB_FILES_NAMES = {'vocab_file': 'vocab.txt'}
|
||||
PRETRAINED_VOCAB_FILES_MAP = {
|
||||
'vocab_file':
|
||||
{
|
||||
'dilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
|
||||
'dilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
|
||||
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
|
||||
}
|
||||
}
|
||||
|
||||
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
||||
'dilbert-base-uncased': 512,
|
||||
'dilbert-base-uncased-distilled-squad': 512,
|
||||
'distilbert-base-uncased': 512,
|
||||
'distilbert-base-uncased-distilled-squad': 512,
|
||||
}
|
||||
|
||||
|
||||
class DilBertTokenizer(BertTokenizer):
|
||||
class DistilBertTokenizer(BertTokenizer):
|
||||
r"""
|
||||
Constructs a DilBertTokenizer.
|
||||
:class:`~pytorch_transformers.DilBertTokenizer` is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece
|
||||
Constructs a DistilBertTokenizer.
|
||||
:class:`~pytorch_transformers.DistilBertTokenizer` is identical to BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece
|
||||
|
||||
Args:
|
||||
vocab_file: Path to a one-wordpiece-per-line vocabulary file
|
||||
Loading…
Reference in New Issue
Block a user