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transformers/docs/source/en/model_doc/mobilebert.md

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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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+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
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+
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+*This model was released on 2020-04-06 and added to Hugging Face Transformers on 2020-11-16.*
+
+<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">
+    </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",
+    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",
+    dtype=torch.float16,
+    device_map="auto",
+)
+inputs = tokenizer("The capital of France is [MASK].", return_tensors="pt").to(model.device)
+
+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
+
+## 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