mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
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>
199 lines
5.1 KiB
Markdown
199 lines
5.1 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.
|
|
|
|
-->
|
|
|
|
|
|
<div style="float: right;">
|
|
<div class="flex flex-wrap space-x-1">
|
|
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|
<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
|
|
</div>
|
|
</div>
|
|
|
|
# MobileBERT
|
|
|
|
[MobileBERT](https://huggingface.co/papers/2004.02984) is a lightweight and efficient variant of BERT, specifically designed for resource-limited devices such as mobile phones. It retains BERT's architecture but significantly reduces model size and inference latency while maintaining strong performance on NLP tasks. MobileBERT achieves this through a bottleneck structure and carefully balanced self-attention and feedforward networks. The model is trained by knowledge transfer from a large BERT model with an inverted bottleneck structure.
|
|
|
|
You can find the original MobileBERT checkpoint under the [Google](https://huggingface.co/google/mobilebert-uncased) organization.
|
|
> [!TIP]
|
|
> Click on the MobileBERT models in the right sidebar for more examples of how to apply MobileBERT to different language tasks.
|
|
|
|
The example below demonstrates how to predict the `[MASK]` token with [`Pipeline`], [`AutoModel`], and from the command line.
|
|
|
|
<hfoptions id="usage">
|
|
<hfoption id="Pipeline">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import pipeline
|
|
|
|
pipeline = pipeline(
|
|
task="fill-mask",
|
|
model="google/mobilebert-uncased",
|
|
torch_dtype=torch.float16,
|
|
device=0
|
|
)
|
|
pipeline("The capital of France is [MASK].")
|
|
```
|
|
</hfoption>
|
|
<hfoption id="AutoModel">
|
|
|
|
```py
|
|
import torch
|
|
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
"google/mobilebert-uncased",
|
|
)
|
|
model = AutoModelForMaskedLM.from_pretrained(
|
|
"google/mobilebert-uncased",
|
|
torch_dtype=torch.float16,
|
|
device_map="auto",
|
|
)
|
|
inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt").to("cuda")
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
predictions = outputs.logits
|
|
|
|
masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
|
|
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
|
|
predicted_token = tokenizer.decode(predicted_token_id)
|
|
|
|
print(f"The predicted token is: {predicted_token}")
|
|
```
|
|
|
|
</hfoption>
|
|
<hfoption id="transformers CLI">
|
|
|
|
```bash
|
|
echo -e "The capital of France is [MASK]." | transformers run --task fill-mask --model google/mobilebert-uncased --device 0
|
|
```
|
|
|
|
</hfoption>
|
|
</hfoptions>
|
|
|
|
|
|
## Notes
|
|
|
|
- Inputs should be padded on the right because BERT uses absolute position embeddings.
|
|
|
|
## MobileBertConfig
|
|
|
|
[[autodoc]] MobileBertConfig
|
|
|
|
## MobileBertTokenizer
|
|
|
|
[[autodoc]] MobileBertTokenizer
|
|
|
|
## MobileBertTokenizerFast
|
|
|
|
[[autodoc]] MobileBertTokenizerFast
|
|
|
|
## MobileBert specific outputs
|
|
|
|
[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
|
|
|
|
[[autodoc]] models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
|
|
|
|
<frameworkcontent>
|
|
<pt>
|
|
|
|
## MobileBertModel
|
|
|
|
[[autodoc]] MobileBertModel
|
|
- forward
|
|
|
|
## MobileBertForPreTraining
|
|
|
|
[[autodoc]] MobileBertForPreTraining
|
|
- forward
|
|
|
|
## MobileBertForMaskedLM
|
|
|
|
[[autodoc]] MobileBertForMaskedLM
|
|
- forward
|
|
|
|
## MobileBertForNextSentencePrediction
|
|
|
|
[[autodoc]] MobileBertForNextSentencePrediction
|
|
- forward
|
|
|
|
## MobileBertForSequenceClassification
|
|
|
|
[[autodoc]] MobileBertForSequenceClassification
|
|
- forward
|
|
|
|
## MobileBertForMultipleChoice
|
|
|
|
[[autodoc]] MobileBertForMultipleChoice
|
|
- forward
|
|
|
|
## MobileBertForTokenClassification
|
|
|
|
[[autodoc]] MobileBertForTokenClassification
|
|
- forward
|
|
|
|
## MobileBertForQuestionAnswering
|
|
|
|
[[autodoc]] MobileBertForQuestionAnswering
|
|
- forward
|
|
|
|
</pt>
|
|
<tf>
|
|
|
|
## TFMobileBertModel
|
|
|
|
[[autodoc]] TFMobileBertModel
|
|
- call
|
|
|
|
## TFMobileBertForPreTraining
|
|
|
|
[[autodoc]] TFMobileBertForPreTraining
|
|
- call
|
|
|
|
## TFMobileBertForMaskedLM
|
|
|
|
[[autodoc]] TFMobileBertForMaskedLM
|
|
- call
|
|
|
|
## TFMobileBertForNextSentencePrediction
|
|
|
|
[[autodoc]] TFMobileBertForNextSentencePrediction
|
|
- call
|
|
|
|
## TFMobileBertForSequenceClassification
|
|
|
|
[[autodoc]] TFMobileBertForSequenceClassification
|
|
- call
|
|
|
|
## TFMobileBertForMultipleChoice
|
|
|
|
[[autodoc]] TFMobileBertForMultipleChoice
|
|
- call
|
|
|
|
## TFMobileBertForTokenClassification
|
|
|
|
[[autodoc]] TFMobileBertForTokenClassification
|
|
- call
|
|
|
|
## TFMobileBertForQuestionAnswering
|
|
|
|
[[autodoc]] TFMobileBertForQuestionAnswering
|
|
- call
|
|
|
|
</tf>
|
|
</frameworkcontent>
|