Applied review suggestions for CamemBERT: restored API refs, added examples, badges, and attribution

Author: MShaheerMalik77

docs/source/en/model_doc/camembert.md

@@ -1,59 +1,143 @@
-# CamemBERT Base
+<!--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
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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
+rendered properly in your Markdown viewer.
+
+-->
+# CamemBERT
 <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">
+    <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
 	</div>
 </div>
 
-[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert) is a language model based on RoBERTa, but it was trained specifically on French text from the OSCAR dataset.
+[CamemBERT](https://huggingface.co/papers/1911.03894) is a language model based on [RoBERTa](./roberta), but trained specifically on French text from the OSCAR dataset, making it more effective for French language tasks.
 
+## CamembertConfig
 What sets CamemBERT apart is that it learned from a huge, high quality collection of French data, as opposed to mixing lots of languages. This helps it really understand French better than many multilingual models.
 
 Common applications of CamemBERT include masked language modeling (Fill-mask prediction), text classification (sentiment analysis), token classification (entity recognition) and sentence pair classification (entailment tasks).
 
-You can find all the original CamemBERT checkpoints under the [CamemBERT](https://huggingface.co/models?search=camembert) collection.
+[[autodoc]] CamembertConfig
+## CamembertTokenizer
+You can find all the original CamemBERT checkpoints under the [ALMAnaCH](https://huggingface.co/almanach/models?search=camembert) organization.
 
 > [!TIP]
-> This model was contributed by the [Facebook AI](https://huggingface.co/facebook) team.
+> This model was contributed by the [ALMAnaCH (Inria)](https://huggingface.co/almanach) team.
 >
 > Click on the CamemBERT models in the right sidebar for more examples of how to apply CamemBERT to different NLP tasks.
+[[autodoc]] CamembertTokenizer
+## CamembertTokenizerFast
 
-The examples below demonstrate how to perform masked language modeling with `pipeline` or the `AutoModel` class.
+The examples below demonstrate how to predict the `<mask>` token with [`Pipeline`], [`AutoModel`], and from the command line.
 
 <hfoptions id="usage">
 
 <hfoption id="Pipeline">
 
 ```python
+import torch
 from transformers import pipeline
 
-fill_mask = pipeline("fill-mask", model="camembert-base")
-result = fill_mask("Le camembert est un délicieux fromage <mask>.")
-print(result)
+pipeline = pipeline("fill-mask", model="camembert-base", torch_dtype=torch.float16, device=0)
+pipeline("Le camembert est un délicieux fromage <mask>.")
 ```
 
 </hfoption>
 
 <hfoption id="AutoModel">
 
 ```python
+import torch
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 
 tokenizer = AutoTokenizer.from_pretrained("camembert-base")
-model = AutoModelForMaskedLM.from_pretrained("camembert-base")
+model = AutoModelForMaskedLM.from_pretrained("camembert-base", torch_dtype="auto", device_map="auto", attn_implementation="sdpa")
+inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", return_tensors="pt").to("cuda")
 
-inputs = tokenizer("Le camembert est un délicieux fromage <mask>.", return_tensors="pt")
-outputs = model(**inputs)
+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>
 
 </hfoptions>
+[[autodoc]] CamembertTokenizerFast
+
+## CamembertModel
+[[autodoc]] CamembertModel
+
+## CamembertForMaskedLM
+[[autodoc]] CamembertForMaskedLM
+
+## CamembertForSequenceClassification
 
-Quantization reduces the memory burden of large models by representing weights in lower precision. Refer to the [Quantization](https://huggingface.co/docs/transformers/main/en/quantization) overview for available options.
-The example below uses [BitsAndBytes](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#load-in-8bit-or-4bit-using-bitsandbytes) quantization to load the model in 8-bit precision.
+[[autodoc]] CamembertForSequenceClassification
+
+## CamembertForMultipleChoice
+
+You can also use CamemBERT for multiple-choice tasks via the command line:
+
+```bash
+transformers-cli env
+python -m transformers.cli run camembert-base \
+    --task multiple-choice \
+    --context "The president of France is" \
+    --choices "Emmanuel Macron" "Napoleon Bonaparte" "Marie Curie" \
+    --framework pt
+```
+
+[[autodoc]] CamembertForMultipleChoice
+  
+## CamembertForTokenClassification
+
+You can use the `camembert/camembert-large` model for token classification tasks like Named Entity Recognition (NER).
 
+```python
+from transformers import AutoTokenizer, CamembertForTokenClassification
+import torch
+
+model_id = "camembert/camembert-large"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = CamembertForTokenClassification.from_pretrained(model_id)
+
+text = "Emmanuel Macron est le président de la République française."
+tokens = tokenizer(text, return_tensors="pt", truncation=True)
+
+with torch.no_grad():
+    outputs = model(**tokens)
+
+predictions = torch.argmax(outputs.logits, dim=2)
+
+for token, pred_id in zip(tokenizer.convert_ids_to_tokens(tokens["input_ids"][0]), predictions[0]):
+    label = model.config.id2label.get(pred_id.item(), "O")
+    print(f"{token}: {label}")
+```
+
+[[autodoc]] CamembertForTokenClassification
+  
+Quantization reduces the memory burden of large models by representing weights in lower precision. Refer to the [Quantization](../quantization/overview) overview for available options.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) quantization to quantize the weights to 8-bits.
+  
 ```python
 from transformers import AutoTokenizer, AutoModelForMaskedLM, BitsAndBytesConfig
 
@@ -65,15 +149,39 @@ model = AutoModelForMaskedLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained("camembert-base")
 ```
-	
-## Notes
 
-- CamemBERT uses RoBERTa pretraining objectives.
-- It makes use of a SentencePiece tokenizer.
-- It does not support token type IDs (segment embeddings).
-- Special pre-processing/post-processing is not needed.
+[[autodoc]] CamembertForQuestionAnswering
+
+</pt>
+<tf>
+
+## TFCamembertModel
+
+[[autodoc]] TFCamembertModel
+
+## TFCamembertForCausalLM
+
+[[autodoc]] TFCamembertForCausalLM
+
+## TFCamembertForMaskedLM
+
+[[autodoc]] TFCamembertForMaskedLM
+
+## TFCamembertForSequenceClassification
+
+[[autodoc]] TFCamembertForSequenceClassification
+
+## TFCamembertForMultipleChoice
+
+[[autodoc]] TFCamembertForMultipleChoice
+
+## TFCamembertForTokenClassification
+
+[[autodoc]] TFCamembertForTokenClassification
+
+## TFCamembertForQuestionAnswering
 
-## Resources
+[[autodoc]] TFCamembertForQuestionAnswering
 
-- [Original Paper](https://arxiv.org/abs/1911.03894)
-- [Hugging Face Model Card](https://huggingface.co/camembert-base)
+</tf>
+</frameworkcontent>