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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- bookcorpus |
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- wikipedia |
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--- |
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# Model Card for DistilBERT base model (cased) |
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This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased). |
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It was introduced in [this paper](https://arxiv.org/abs/1910.01108). |
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The code for the distillation process can be found |
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[here](https://github.com/huggingface/transformers/tree/master/examples/distillation). |
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This model is cased: it does make a difference between english and English. |
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All the training details on the pre-training, the uses, limitations and potential biases (included below) are the same as for [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased). |
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We highly encourage to check it if you want to know more. |
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## Model description |
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DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a |
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self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, |
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with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic |
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process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained |
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with three objectives: |
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- Distillation loss: the model was trained to return the same probabilities as the BERT base model. |
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- Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a |
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sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the |
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model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that |
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usually see the words one after the other, or from autoregressive models like GPT which internally mask the future |
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tokens. It allows the model to learn a bidirectional representation of the sentence. |
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- Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base |
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model. |
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This way, the model learns the same inner representation of the English language than its teacher model, while being |
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faster for inference or downstream tasks. |
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## Intended uses & limitations |
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You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to |
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be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for |
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fine-tuned versions on a task that interests you. |
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Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) |
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to make decisions, such as sequence classification, token classification or question answering. For tasks such as text |
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generation you should look at model like GPT2. |
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### How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') |
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>>> unmasker("Hello I'm a [MASK] model.") |
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[{'sequence': "[CLS] hello i'm a role model. [SEP]", |
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'score': 0.05292855575680733, |
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'token': 2535, |
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'token_str': 'role'}, |
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{'sequence': "[CLS] hello i'm a fashion model. [SEP]", |
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'score': 0.03968575969338417, |
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'token': 4827, |
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'token_str': 'fashion'}, |
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{'sequence': "[CLS] hello i'm a business model. [SEP]", |
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'score': 0.034743521362543106, |
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'token': 2449, |
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'token_str': 'business'}, |
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{'sequence': "[CLS] hello i'm a model model. [SEP]", |
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'score': 0.03462274372577667, |
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'token': 2944, |
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'token_str': 'model'}, |
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{'sequence': "[CLS] hello i'm a modeling model. [SEP]", |
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'score': 0.018145186826586723, |
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'token': 11643, |
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'token_str': 'modeling'}] |
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``` |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import DistilBertTokenizer, DistilBertModel |
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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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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import DistilBertTokenizer, TFDistilBertModel |
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tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') |
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model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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### Limitations and bias |
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Even if the training data used for this model could be characterized as fairly neutral, this model can have biased |
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predictions. It also inherits some of |
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[the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') |
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>>> unmasker("The White man worked as a [MASK].") |
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[{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', |
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'score': 0.1235365942120552, |
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'token': 20987, |
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'token_str': 'blacksmith'}, |
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{'sequence': '[CLS] the white man worked as a carpenter. [SEP]', |
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'score': 0.10142576694488525, |
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'token': 10533, |
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'token_str': 'carpenter'}, |
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{'sequence': '[CLS] the white man worked as a farmer. [SEP]', |
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'score': 0.04985016956925392, |
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'token': 7500, |
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'token_str': 'farmer'}, |
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{'sequence': '[CLS] the white man worked as a miner. [SEP]', |
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'score': 0.03932540491223335, |
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'token': 18594, |
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'token_str': 'miner'}, |
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{'sequence': '[CLS] the white man worked as a butcher. [SEP]', |
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'score': 0.03351764753460884, |
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'token': 14998, |
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'token_str': 'butcher'}] |
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>>> unmasker("The Black woman worked as a [MASK].") |
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[{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', |
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'score': 0.13283951580524445, |
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'token': 13877, |
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'token_str': 'waitress'}, |
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{'sequence': '[CLS] the black woman worked as a nurse. [SEP]', |
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'score': 0.12586183845996857, |
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'token': 6821, |
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'token_str': 'nurse'}, |
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{'sequence': '[CLS] the black woman worked as a maid. [SEP]', |
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'score': 0.11708822101354599, |
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'token': 10850, |
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'token_str': 'maid'}, |
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{'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', |
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'score': 0.11499975621700287, |
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'token': 19215, |
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'token_str': 'prostitute'}, |
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{'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', |
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'score': 0.04722772538661957, |
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'token': 22583, |
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'token_str': 'housekeeper'}] |
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``` |
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This bias will also affect all fine-tuned versions of this model. |
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## Training data |
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DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset |
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consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) |
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(excluding lists, tables and headers). |
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## Training procedure |
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### Preprocessing |
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The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are |
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then of the form: |
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``` |
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[CLS] Sentence A [SEP] Sentence B [SEP] |
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``` |
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With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in |
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the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a |
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consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two |
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"sentences" has a combined length of less than 512 tokens. |
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The details of the masking procedure for each sentence are the following: |
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- 15% of the tokens are masked. |
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- In 80% of the cases, the masked tokens are replaced by `[MASK]`. |
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- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. |
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- In the 10% remaining cases, the masked tokens are left as is. |
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### Pretraining |
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The model was trained on 8 16 GB V100 for 90 hours. See the |
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[training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters |
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details. |
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## Evaluation results |
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When fine-tuned on downstream tasks, this model achieves the following results: |
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Glue test results: |
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| Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |
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|:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| |
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| | 81.5 | 87.8 | 88.2 | 90.4 | 47.2 | 85.5 | 85.6 | 60.6 | |
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### BibTeX entry and citation info |
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```bibtex |
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@article{Sanh2019DistilBERTAD, |
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title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, |
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author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, |
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journal={ArXiv}, |
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year={2019}, |
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volume={abs/1910.01108} |
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} |
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``` |
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<a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> |
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<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> |
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</a> |
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