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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>
199 lines
5.1 KiB
Markdown
199 lines
5.1 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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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="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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</div>
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</div>
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# MobileBERT
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[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.
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You can find the original MobileBERT checkpoint under the [Google](https://huggingface.co/google/mobilebert-uncased) organization.
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> [!TIP]
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> Click on the MobileBERT models in the right sidebar for more examples of how to apply MobileBERT to different language tasks.
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The example below demonstrates how to predict the `[MASK]` token 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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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="fill-mask",
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model="google/mobilebert-uncased",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("The capital of France is [MASK].")
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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 AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google/mobilebert-uncased",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"google/mobilebert-uncased",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = outputs.logits
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masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {predicted_token}")
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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 "The capital of France is [MASK]." | transformers run --task fill-mask --model google/mobilebert-uncased --device 0
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Inputs should be padded on the right because BERT uses absolute position embeddings.
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## MobileBertConfig
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[[autodoc]] MobileBertConfig
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## MobileBertTokenizer
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[[autodoc]] MobileBertTokenizer
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## MobileBertTokenizerFast
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[[autodoc]] MobileBertTokenizerFast
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## MobileBert specific outputs
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[[autodoc]] models.mobilebert.modeling_mobilebert.MobileBertForPreTrainingOutput
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[[autodoc]] models.mobilebert.modeling_tf_mobilebert.TFMobileBertForPreTrainingOutput
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<frameworkcontent>
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<pt>
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## MobileBertModel
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[[autodoc]] MobileBertModel
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- forward
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## MobileBertForPreTraining
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[[autodoc]] MobileBertForPreTraining
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- forward
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## MobileBertForMaskedLM
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[[autodoc]] MobileBertForMaskedLM
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- forward
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## MobileBertForNextSentencePrediction
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[[autodoc]] MobileBertForNextSentencePrediction
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- forward
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## MobileBertForSequenceClassification
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[[autodoc]] MobileBertForSequenceClassification
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- forward
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## MobileBertForMultipleChoice
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[[autodoc]] MobileBertForMultipleChoice
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- forward
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## MobileBertForTokenClassification
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[[autodoc]] MobileBertForTokenClassification
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- forward
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## MobileBertForQuestionAnswering
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[[autodoc]] MobileBertForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFMobileBertModel
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[[autodoc]] TFMobileBertModel
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- call
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## TFMobileBertForPreTraining
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[[autodoc]] TFMobileBertForPreTraining
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- call
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## TFMobileBertForMaskedLM
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[[autodoc]] TFMobileBertForMaskedLM
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- call
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## TFMobileBertForNextSentencePrediction
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[[autodoc]] TFMobileBertForNextSentencePrediction
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- call
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## TFMobileBertForSequenceClassification
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[[autodoc]] TFMobileBertForSequenceClassification
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- call
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## TFMobileBertForMultipleChoice
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[[autodoc]] TFMobileBertForMultipleChoice
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- call
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## TFMobileBertForTokenClassification
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[[autodoc]] TFMobileBertForTokenClassification
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- call
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## TFMobileBertForQuestionAnswering
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[[autodoc]] TFMobileBertForQuestionAnswering
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- call
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</tf>
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</frameworkcontent>
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