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https://github.com/huggingface/transformers.git
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d538293f62
* transformers-cli -> transformers * Chat command works with positional argument * update doc references to transformers-cli * doc headers * deepspeed --------- Co-authored-by: Joao Gante <joao@huggingface.co>
216 lines
8.7 KiB
Markdown
216 lines
8.7 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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</div>
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</div>
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# DistilBERT
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[DistilBERT](https://huggingface.co/papers/1910.01108) is pretrained by knowledge distillation to create a smaller model with faster inference and requires less compute to train. Through a triple loss objective during pretraining, language modeling loss, distillation loss, cosine-distance loss, DistilBERT demonstrates similar performance to a larger transformer language model.
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You can find all the original DistilBERT checkpoints under the [DistilBERT](https://huggingface.co/distilbert) organization.
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> [!TIP]
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> Click on the DistilBERT models in the right sidebar for more examples of how to apply DistilBERT to different language tasks.
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The example below demonstrates how to classify text with [`Pipeline`], [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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from transformers import pipeline
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classifier = pipeline(
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task="text-classification",
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model="distilbert-base-uncased-finetuned-sst-2-english",
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torch_dtype=torch.float16,
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device=0
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)
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result = classifier("I love using Hugging Face Transformers!")
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print(result)
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# Output: [{'label': 'POSITIVE', 'score': 0.9998}]
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
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)
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model = AutoModelForSequenceClassification.from_pretrained(
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"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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inputs = tokenizer("I love using Hugging Face Transformers!", return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model(**inputs)
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predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
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predicted_label = model.config.id2label[predicted_class_id]
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print(f"Predicted label: {predicted_label}")
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "I love using Hugging Face Transformers!" | transformers run --task text-classification --model distilbert-base-uncased-finetuned-sst-2-english
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```
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</hfoption>
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</hfoptions>
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## Notes
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- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
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separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
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- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
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necessary though, just let us know if you need this option.
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## DistilBertConfig
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[[autodoc]] DistilBertConfig
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## DistilBertTokenizer
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[[autodoc]] DistilBertTokenizer
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## DistilBertTokenizerFast
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[[autodoc]] DistilBertTokenizerFast
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<frameworkcontent>
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<pt>
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## DistilBertModel
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[[autodoc]] DistilBertModel
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- forward
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## DistilBertForMaskedLM
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[[autodoc]] DistilBertForMaskedLM
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- forward
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## DistilBertForSequenceClassification
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[[autodoc]] DistilBertForSequenceClassification
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- forward
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## DistilBertForMultipleChoice
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[[autodoc]] DistilBertForMultipleChoice
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- forward
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## DistilBertForTokenClassification
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[[autodoc]] DistilBertForTokenClassification
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- forward
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## DistilBertForQuestionAnswering
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[[autodoc]] DistilBertForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFDistilBertModel
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[[autodoc]] TFDistilBertModel
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- call
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## TFDistilBertForMaskedLM
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[[autodoc]] TFDistilBertForMaskedLM
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- call
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## TFDistilBertForSequenceClassification
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[[autodoc]] TFDistilBertForSequenceClassification
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- call
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## TFDistilBertForMultipleChoice
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[[autodoc]] TFDistilBertForMultipleChoice
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- call
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## TFDistilBertForTokenClassification
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[[autodoc]] TFDistilBertForTokenClassification
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- call
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## TFDistilBertForQuestionAnswering
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[[autodoc]] TFDistilBertForQuestionAnswering
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- call
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</tf>
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<jax>
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## FlaxDistilBertModel
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[[autodoc]] FlaxDistilBertModel
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- __call__
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## FlaxDistilBertForMaskedLM
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[[autodoc]] FlaxDistilBertForMaskedLM
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- __call__
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## FlaxDistilBertForSequenceClassification
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[[autodoc]] FlaxDistilBertForSequenceClassification
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- __call__
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## FlaxDistilBertForMultipleChoice
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[[autodoc]] FlaxDistilBertForMultipleChoice
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- __call__
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## FlaxDistilBertForTokenClassification
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[[autodoc]] FlaxDistilBertForTokenClassification
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- __call__
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## FlaxDistilBertForQuestionAnswering
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[[autodoc]] FlaxDistilBertForQuestionAnswering
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- __call__
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</jax>
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</frameworkcontent>
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