wehub-resource-sync
e06fe8e8c6
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Has been cancelled
New model PR merged notification / Notify new model (push) Has been cancelled
Update Transformers metadata / build_and_package (push) Has been cancelled
135 lines
4.1 KiB
Markdown
135 lines
4.1 KiB
Markdown
<!--Copyright 2020 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.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
*This model was published in HF papers on 2019-10-02 and contributed to Hugging Face Transformers on 2020-11-16.*
|
|
|
|
<div style="float: right;">
|
|
<div class="flex flex-wrap space-x-1">
|
|
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
|
|
</div>
|
|
</div>
|
|
|
|
# DistilBERT
|
|
|
|
[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.
|
|
|
|
You can find all the original DistilBERT checkpoints under the [DistilBERT](https://huggingface.co/distilbert) organization.
|
|
|
|
> [!TIP]
|
|
> Click on the DistilBERT models in the right sidebar for more examples of how to apply DistilBERT to different language tasks.
|
|
|
|
The example below demonstrates how to classify text with [`Pipeline`], [`AutoModel`], and from the command line.
|
|
|
|
<hfoptions id="usage">
|
|
|
|
<hfoption id="Pipeline">
|
|
|
|
```python
|
|
from transformers import pipeline
|
|
|
|
|
|
classifier = pipeline(
|
|
task="text-classification",
|
|
model="distilbert-base-uncased-finetuned-sst-2-english",
|
|
device=0
|
|
)
|
|
|
|
result = classifier("I love using Hugging Face Transformers!")
|
|
print(result)
|
|
# Output: [{'label': 'POSITIVE', 'score': 0.9998}]
|
|
```
|
|
|
|
</hfoption>
|
|
|
|
<hfoption id="AutoModel">
|
|
|
|
```python
|
|
import torch
|
|
|
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
|
|
)
|
|
model = AutoModelForSequenceClassification.from_pretrained(
|
|
"distilbert/distilbert-base-uncased-finetuned-sst-2-english",
|
|
device_map="auto",
|
|
attn_implementation="sdpa"
|
|
)
|
|
inputs = tokenizer("I love using Hugging Face Transformers!", return_tensors="pt").to(model.device)
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
|
|
predicted_label = model.config.id2label[predicted_class_id]
|
|
print(f"Predicted label: {predicted_label}")
|
|
```
|
|
|
|
</hfoption>
|
|
|
|
</hfoptions>
|
|
|
|
## Notes
|
|
|
|
- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs 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 us know if you need this option.
|
|
|
|
## DistilBertConfig
|
|
|
|
[[autodoc]] DistilBertConfig
|
|
|
|
## DistilBertTokenizer
|
|
|
|
[[autodoc]] DistilBertTokenizer
|
|
|
|
## DistilBertTokenizerFast
|
|
|
|
[[autodoc]] DistilBertTokenizerFast
|
|
|
|
## DistilBertModel
|
|
|
|
[[autodoc]] DistilBertModel
|
|
- forward
|
|
|
|
## DistilBertForMaskedLM
|
|
|
|
[[autodoc]] DistilBertForMaskedLM
|
|
- forward
|
|
|
|
## DistilBertForSequenceClassification
|
|
|
|
[[autodoc]] DistilBertForSequenceClassification
|
|
- forward
|
|
|
|
## DistilBertForMultipleChoice
|
|
|
|
[[autodoc]] DistilBertForMultipleChoice
|
|
- forward
|
|
|
|
## DistilBertForTokenClassification
|
|
|
|
[[autodoc]] DistilBertForTokenClassification
|
|
- forward
|
|
|
|
## DistilBertForQuestionAnswering
|
|
|
|
[[autodoc]] DistilBertForQuestionAnswering
|
|
- forward
|