mobilebert model card update

docs/source/en/model_doc/mobilebert.md

@@ -14,52 +14,81 @@ rendered properly in your Markdown viewer.
 
 -->
 
-# MobileBERT
 
-<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 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>
 
-## Overview
+# 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")
 
-The MobileBERT model was proposed in [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny
-Zhou. It's a bidirectional transformer based on the BERT model, which is compressed and accelerated using several
-approaches.
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = outputs.logits
 
-The abstract from the paper is the following:
+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)
 
-*Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds
-of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot
-be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating
-the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to
-various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while
-equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks.
-To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE
-model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is
-4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the
-natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms
-latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of
-90.0/79.2 (1.5/2.1 higher than BERT_BASE).*
+print(f"The predicted token is: {predicted_token}")
+```
 
-This model was contributed by [vshampor](https://huggingface.co/vshampor). The original code can be found [here](https://github.com/google-research/google-research/tree/master/mobilebert).
+</hfoption>
+<hfoption id="transformers-cli">
 
-## Usage tips
+```bash
+echo -e "The capital of France is [MASK]." | transformers-cli run --task fill-mask --model google/mobilebert-uncased --device 0
+```
 
-- MobileBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather
-  than the left.
-- MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
-  efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
-  with a causal language modeling (CLM) objective are better in that regard.
+</hfoption>
+</hfoptions>
 
 
-## Resources
+## Notes
 
-- [Text classification task guide](../tasks/sequence_classification)
-- [Token classification task guide](../tasks/token_classification)
-- [Question answering task guide](../tasks/question_answering)
-- [Masked language modeling task guide](../tasks/masked_language_modeling)
-- [Multiple choice task guide](../tasks/multiple_choice)
+- Inputs should be padded on the right because BERT uses absolute position embeddings.
 
 ## MobileBertConfig