Automated compatible models list for task guides (#21338)

* initial commit. added tip placeholders and a script

* removed unused imports, fixed paths

* fixed generated links

* make style

* split language modeling doc into two: causal language modeling and masked language modeling

* added check_task_guides.py to make fix-copies

* review feedback addressed

.circleci/config.yml

@@ -178,6 +178,7 @@ jobs:
             - run: make deps_table_check_updated
             - run: python utils/tests_fetcher.py --sanity_check
             - run: python utils/update_metadata.py --check-only
+            - run: python utils/check_task_guides.py
 
 workflows:
     version: 2

Makefile

@@ -43,6 +43,7 @@ repo-consistency:
 	python utils/check_doctest_list.py
 	python utils/tests_fetcher.py --sanity_check
 	python utils/update_metadata.py --check-only
+	python utils/check_task_guides.py
 
 # this target runs checks on all files
 
@@ -81,6 +82,7 @@ fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
 	python utils/check_table.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
+	python utils/check_task_guides.py --fix_and_overwrite
 
 # Run tests for the library
 

docs/source/en/_toctree.yml

@@ -54,7 +54,9 @@
       - local: tasks/question_answering
         title: Question answering
       - local: tasks/language_modeling
-        title: Language modeling
+        title: Causal language modeling
+      - local: tasks/masked_language_modeling
+        title: Masked language modeling
       - local: tasks/translation
         title: Translation
       - local: tasks/summarization

docs/source/en/tasks/asr.mdx

-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+<!--Copyright 2023 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
@@ -22,8 +22,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the automatic speech recognition [task page](https://huggingface.co/tasks/automatic-speech-recognition) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/audio_classification.mdx

@@ -22,8 +22,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the audio classification [task page](https://huggingface.co/tasks/audio-classification) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[Audio Spectrogram Transformer](../model_doc/audio-spectrogram-transformer), [Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/image_classification.mdx

@@ -22,12 +22,16 @@ after a natural disaster, monitoring crop health, or helping screen medical imag
 
 This guide illustrates how to:
 
-1. Fine-tune [ViT](https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.
+1. Fine-tune [ViT](model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.
 2. Use your fine-tuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the image classification [task page](https://huggingface.co/tasks/image-classification) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/masked_language_modeling.mdx

+<!--Copyright 2023 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.
+-->
+
+# Masked language modeling
+
+[[open-in-colab]]
+
+<Youtube id="mqElG5QJWUg"/>
+
+Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This
+means the model has full access to the tokens on the left and right. Masked language modeling is great for tasks that
+require a good contextual understanding of an entire sequence. BERT is an example of a masked language model.
+
+This guide will show you how to:
+
+1. Finetune [DistilRoBERTa](https://huggingface.co/distilroberta-base) on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
+2. Use your finetuned model for inference.
+
+<Tip>
+You can finetune other architectures for masked language modeling following the same steps in this guide.
+Choose one of the following architectures:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [mBART](../model_doc/mbart), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [Perceiver](../model_doc/perceiver), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Wav2Vec2](../model_doc/wav2vec2), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [YOSO](../model_doc/yoso)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```bash
+pip install transformers datasets evaluate
+```
+
+We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+## Load ELI5 dataset
+
+Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This'll
+give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
+
+```py
+>>> from datasets import load_dataset
+
+>>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
+```
+
+Split the dataset's `train_asks` split into a train and test set with the [`~datasets.Dataset.train_test_split`] method:
+
+```py
+>>> eli5 = eli5.train_test_split(test_size=0.2)
+```
+
+Then take a look at an example:
+
+```py
+>>> eli5["train"][0]
+{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
+  'score': [6, 3],
+  'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
+   "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]},
+ 'answers_urls': {'url': []},
+ 'document': '',
+ 'q_id': 'nyxfp',
+ 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
+ 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
+ 'subreddit': 'askscience',
+ 'title': 'Few questions about this space walk photograph.',
+ 'title_urls': {'url': []}}
+```
+
+While this may look like a lot, you're only really interested in the `text` field. What's cool about language modeling tasks is you don't need labels (also known as an unsupervised task) because the next word *is* the label.
+
+## Preprocess
+
+<Youtube id="8PmhEIXhBvI"/>
+
+For masked language modeling, the next step is to load a DistilRoBERTa tokenizer to process the `text` subfield:
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
+```
+
+You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to e
+xtract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:
+
+```py
+>>> eli5 = eli5.flatten()
+>>> eli5["train"][0]
+{'answers.a_id': ['c3d1aib', 'c3d4lya'],
+ 'answers.score': [6, 3],
+ 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
+  "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"],
+ 'answers_urls.url': [],
+ 'document': '',
+ 'q_id': 'nyxfp',
+ 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
+ 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
+ 'subreddit': 'askscience',
+ 'title': 'Few questions about this space walk photograph.',
+ 'title_urls.url': []}
+```
+
+Each subfield is now a separate column as indicated by the `answers` prefix, and the `text` field is a list now. Instead
+of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.
+
+Here is how you can create a preprocessing function to convert the list to a string, and truncate sequences to be no longer than DistilRoBERTa's maximum input length:
+
+```py
+>>> def preprocess_function(examples):
+...     return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
+```
+
+To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.with_transform`] method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once, and increasing the number of processes with `num_proc`. Remove any columns you don't need:
+
+```py
+>>> tokenized_eli5 = eli5.map(
+...     preprocess_function,
+...     batched=True,
+...     num_proc=4,
+...     remove_columns=eli5["train"].column_names,
+... )
+```
+
+Now you'll need a second preprocessing function to capture text truncated from the lengthier examples to avoid losing any information. This preprocessing function should:
+
+- Concatenate all the text.
+- Split the concatenated text into smaller chunks defined by `block_size`.
+
+```py
+>>> block_size = 128
+
+
+>>> def group_texts(examples):
+...     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+...     total_length = len(concatenated_examples[list(examples.keys())[0]])
+...     total_length = (total_length // block_size) * block_size
+...     result = {
+...         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+...         for k, t in concatenated_examples.items()
+...     }
+...     result["labels"] = result["input_ids"].copy()
+...     return result
+```
+
+Apply the `group_texts` function over the entire dataset:
+
+```py
+>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
+```
+
+Now create a batch of examples using [`DataCollatorForLanguageModeling`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximium length.
+
+<frameworkcontent>
+<pt>
+
+Use the end-of-sequence token as the padding token and specify `mlm_probability` to randomly mask tokens each time you iterate over the data:
+
+```py
+>>> from transformers import DataCollatorForLanguageModeling
+
+>>> tokenizer.pad_token = tokenizer.eos_token
+>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
+```
+</pt>
+<tf>
+
+Use the end-of-sequence token as the padding token and specify `mlm_probability` to randomly mask tokens each time you iterate over the data:
+
+```py
+>>> from transformers import DataCollatorForLanguageModeling
+
+>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors="tf")
+```
+</tf>
+</frameworkcontent>
+
+## Train
+
+<frameworkcontent>
+<pt>
+<Tip>
+
+If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
+
+</Tip>
+You're ready to start training your model now! Load DistilRoBERTa with [`AutoModelForMaskedLM`]:
+
+```py
+>>> from transformers import AutoModelForMaskedLM
+
+>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
+```
+
+At this point, only three steps remain:
+
+1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model).
+2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
+3. Call [`~Trainer.train`] to finetune your model.
+
+```py
+>>> training_args = TrainingArguments(
+...     output_dir="my_awesome_eli5_mlm_model",
+...     evaluation_strategy="epoch",
+...     learning_rate=2e-5,
+...     num_train_epochs=3,
+...     weight_decay=0.01,
+...     push_to_hub=True,
+... )
+
+>>> trainer = Trainer(
+...     model=model,
+...     args=training_args,
+...     train_dataset=lm_dataset["train"],
+...     eval_dataset=lm_dataset["test"],
+...     data_collator=data_collator,
+... )
+
+>>> trainer.train()
+```
+
+Once training is completed, use the [`~transformers.Trainer.evaluate`] method to evaluate your model and get its perplexity:
+
+```py
+>>> import math
+
+>>> eval_results = trainer.evaluate()
+>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
+Perplexity: 8.76
+```
+
+Then share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
+
+```py
+>>> trainer.push_to_hub()
+```
+</pt>
+<tf>
+<Tip>
+
+If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
+
+</Tip>
+To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
+
+```py
+>>> from transformers import create_optimizer, AdamWeightDecay
+
+>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
+```
+
+Then you can load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:
+
+```py
+>>> from transformers import TFAutoModelForMaskedLM
+
+>>> model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base")
+```
+
+Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
+
+```py
+>>> tf_train_set = model.prepare_tf_dataset(
+...     lm_dataset["train"],
+...     shuffle=True,
+...     batch_size=16,
+...     collate_fn=data_collator,
+... )
+
+>>> tf_test_set = model.prepare_tf_dataset(
+...     lm_dataset["test"],
+...     shuffle=False,
+...     batch_size=16,
+...     collate_fn=data_collator,
+... )
+```
+
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+
+```py
+>>> import tensorflow as tf
+
+>>> model.compile(optimizer=optimizer)
+```
+
+This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
+
+```py
+>>> from transformers.keras_callbacks import PushToHubCallback
+
+>>> callback = PushToHubCallback(
+...     output_dir="my_awesome_eli5_mlm_model",
+...     tokenizer=tokenizer,
+... )
+```
+
+Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callback to finetune the model:
+
+```py
+>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])
+```
+
+Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
+</tf>
+</frameworkcontent>
+
+<Tip>
+
+For a more in-depth example of how to finetune a model for masked language modeling, take a look at the corresponding
+[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
+or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
+
+</Tip>
+
+## Inference
+
+Great, now that you've finetuned a model, you can use it for inference!
+
+Come up with some text you'd like the model to fill in the blank with, and use the special `<mask>` token to indicate the blank:
+
+```py
+>>> text = "The Milky Way is a <mask> galaxy."
+```
+
+The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for fill-mask with your model, and pass your text to it. If you like, you can use the `top_k` parameter to specify how many predictions to return:
+
+```py
+>>> from transformers import pipeline
+
+>>> mask_filler = pipeline("fill-mask", "stevhliu/my_awesome_eli5_mlm_model")
+>>> mask_filler(text, top_k=3)
+[{'score': 0.5150994658470154,
+  'token': 21300,
+  'token_str': ' spiral',
+  'sequence': 'The Milky Way is a spiral galaxy.'},
+ {'score': 0.07087188959121704,
+  'token': 2232,
+  'token_str': ' massive',
+  'sequence': 'The Milky Way is a massive galaxy.'},
+ {'score': 0.06434620916843414,
+  'token': 650,
+  'token_str': ' small',
+  'sequence': 'The Milky Way is a small galaxy.'}]
+```
+
+<frameworkcontent>
+<pt>
+Tokenize the text and return the `input_ids` as PyTorch tensors. You'll also need to specify the position of the `<mask>` token:
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
+>>> inputs = tokenizer(text, return_tensors="pt")
+>>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+```
+
+Pass your inputs to the model and return the `logits` of the masked token:
+
+```py
+>>> from transformers import AutoModelForMaskedLM
+
+>>> model = AutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
+>>> logits = model(**inputs).logits
+>>> mask_token_logits = logits[0, mask_token_index, :]
+```
+
+Then return the three masked tokens with the highest probability and print them out:
+
+```py
+>>> top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist()
+
+>>> for token in top_3_tokens:
+...     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
+The Milky Way is a spiral galaxy.
+The Milky Way is a massive galaxy.
+The Milky Way is a small galaxy.
+```
+</pt>
+<tf>
+Tokenize the text and return the `input_ids` as TensorFlow tensors. You'll also need to specify the position of the `<mask>` token:
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
+>>> inputs = tokenizer(text, return_tensors="tf")
+>>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
+```
+
+Pass your inputs to the model and return the `logits` of the masked token:
+
+```py
+>>> from transformers import TFAutoModelForMaskedLM
+
+>>> model = TFAutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
+>>> logits = model(**inputs).logits
+>>> mask_token_logits = logits[0, mask_token_index, :]
+```
+
+Then return the three masked tokens with the highest probability and print them out:
+
+```py
+>>> top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy()
+
+>>> for token in top_3_tokens:
+...     print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
+The Milky Way is a spiral galaxy.
+The Milky Way is a massive galaxy.
+The Milky Way is a small galaxy.
+```
+</tf>
+</frameworkcontent>
\ No newline at end of file

docs/source/en/tasks/multiple_choice.mdx

@@ -19,6 +19,17 @@ This guide will show you how to:
 1. Finetune [BERT](https://huggingface.co/bert-base-uncased) on the `regular` configuration of the [SWAG](https://huggingface.co/datasets/swag) dataset to select the best answer given multiple options and some context.
 2. Use your finetuned model for inference.
 
+<Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [I-BERT](../model_doc/ibert), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [YOSO](../model_doc/yoso)
+
+<!--End of the generated tip-->
+
+</Tip>
+
 Before you begin, make sure you have all the necessary libraries installed:
 
 ```bash

docs/source/en/tasks/object_detection.mdx

@@ -21,11 +21,6 @@ be present in different parts of an image (e.g. the image can have several cars)
 This task is commonly used in autonomous driving for detecting things like pedestrians, road signs, and traffic lights.
 Other applications include counting objects in images, image search, and more.
 
- <Tip>
-Check out the <a href="https://huggingface.co/tasks/object-detection">object detection</a> task page to learn about use cases,
-models, metrics, and datasets associated with this task.
- </Tip>
-
 In this guide, you will learn how to:
 
  1. Finetune [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a model that combines a convolutional
@@ -33,6 +28,17 @@ In this guide, you will learn how to:
  dataset.
  2. Use your finetuned model for inference.
 
+<Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
+
+<!--End of the generated tip-->
+
+</Tip>
+
 Before you begin, make sure you have all the necessary libraries installed:
 
 ```bash

docs/source/en/tasks/question_answering.mdx

@@ -26,9 +26,16 @@ This guide will show you how to:
 1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [SQuAD](https://huggingface.co/datasets/squad) dataset for extractive question answering.
 2. Use your finetuned model for inference.
 
+[ALBERT](../model_doc/albert)
+
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [LXMERT](../model_doc/lxmert), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OPT](../model_doc/opt), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [Splinter](../model_doc/splinter), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [YOSO](../model_doc/yoso)
 
-See the question answering [task page](https://huggingface.co/tasks/question-answering) for more information about other forms of question answering and their associated models, datasets, and metrics.
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/semantic_segmentation.mdx

@@ -24,8 +24,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the image segmentation [task page](https://huggingface.co/tasks/image-segmentation) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/sequence_classification.mdx

@@ -24,8 +24,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the text classification [task page](https://huggingface.co/tasks/text-classification) for more information about other forms of text classification and their associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[ALBERT](../model_doc/albert), [BART](../model_doc/bart), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPT Neo](../model_doc/gpt_neo), [GPT-J](../model_doc/gptj), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LED](../model_doc/led), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [mBART](../model_doc/mbart), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [MVP](../model_doc/mvp), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Perceiver](../model_doc/perceiver), [PLBart](../model_doc/plbart), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [TAPAS](../model_doc/tapas), [Transformer-XL](../model_doc/transfo-xl), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [YOSO](../model_doc/yoso)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/summarization.mdx

@@ -25,8 +25,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the summarization [task page](https://huggingface.co/tasks/summarization) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/token_classification.mdx

@@ -24,8 +24,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the token classification [task page](https://huggingface.co/tasks/token-classification) for more information about other forms of token classification and their associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[ALBERT](../model_doc/albert), [BERT](../model_doc/bert), [BigBird](../model_doc/big_bird), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CANINE](../model_doc/canine), [ConvBERT](../model_doc/convbert), [Data2VecText](../model_doc/data2vec-text), [DeBERTa](../model_doc/deberta), [DeBERTa-v2](../model_doc/deberta-v2), [DistilBERT](../model_doc/distilbert), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [ESM](../model_doc/esm), [FlauBERT](../model_doc/flaubert), [FNet](../model_doc/fnet), [Funnel Transformer](../model_doc/funnel), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [I-BERT](../model_doc/ibert), [LayoutLM](../model_doc/layoutlm), [LayoutLMv2](../model_doc/layoutlmv2), [LayoutLMv3](../model_doc/layoutlmv3), [LiLT](../model_doc/lilt), [Longformer](../model_doc/longformer), [LUKE](../model_doc/luke), [MarkupLM](../model_doc/markuplm), [Megatron-BERT](../model_doc/megatron-bert), [MobileBERT](../model_doc/mobilebert), [MPNet](../model_doc/mpnet), [Nezha](../model_doc/nezha), [Nyströmformer](../model_doc/nystromformer), [QDQBert](../model_doc/qdqbert), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [SqueezeBERT](../model_doc/squeezebert), [XLM](../model_doc/xlm), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [YOSO](../model_doc/yoso)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/translation.mdx

@@ -22,8 +22,13 @@ This guide will show you how to:
 2. Use your finetuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the translation [task page](https://huggingface.co/tasks/translation) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[BART](../model_doc/bart), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [Encoder decoder](../model_doc/encoder-decoder), [FairSeq Machine-Translation](../model_doc/fsmt), [LED](../model_doc/led), [LongT5](../model_doc/longt5), [M2M100](../model_doc/m2m_100), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MT5](../model_doc/mt5), [MVP](../model_doc/mvp), [NLLB](../model_doc/nllb), [Pegasus](../model_doc/pegasus), [PEGASUS-X](../model_doc/pegasus_x), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [SwitchTransformers](../model_doc/switch_transformers), [T5](../model_doc/t5), [XLM-ProphetNet](../model_doc/xlm-prophetnet)
+
+<!--End of the generated tip-->
 
 </Tip>
 

docs/source/en/tasks/video_classification.mdx

@@ -22,8 +22,13 @@ This guide will show you how to:
 2. Use your fine-tuned model for inference.
 
 <Tip>
+The task illustrated in this tutorial is supported by the following model architectures:
 
-See the video classification [task page](https://huggingface.co/tasks/video-classification) for more information about its associated models, datasets, and metrics.
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae)
+
+<!--End of the generated tip-->
 
 </Tip>
 

utils/check_task_guides.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# 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.
+
+import argparse
+import importlib.util
+import os
+
+
+# All paths are set with the intent you should run this script from the root of the repo with the command
+# python utils/check_task_guides.py
+TRANSFORMERS_PATH = "src/transformers"
+PATH_TO_TASK_GUIDES = "docs/source/en/tasks"
+
+
+def _find_text_in_file(filename, start_prompt, end_prompt):
+    """
+    Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
+    lines.
+    """
+    with open(filename, "r", encoding="utf-8", newline="\n") as f:
+        lines = f.readlines()
+    # Find the start prompt.
+    start_index = 0
+    while not lines[start_index].startswith(start_prompt):
+        start_index += 1
+    start_index += 1
+
+    end_index = start_index
+    while not lines[end_index].startswith(end_prompt):
+        end_index += 1
+    end_index -= 1
+
+    while len(lines[start_index]) <= 1:
+        start_index += 1
+    while len(lines[end_index]) <= 1:
+        end_index -= 1
+    end_index += 1
+    return "".join(lines[start_index:end_index]), start_index, end_index, lines
+
+
+# This is to make sure the transformers module imported is the one in the repo.
+spec = importlib.util.spec_from_file_location(
+    "transformers",
+    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
+    submodule_search_locations=[TRANSFORMERS_PATH],
+)
+transformers_module = spec.loader.load_module()
+
+TASK_GUIDE_TO_MODELS = {
+    "asr.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
+    "audio_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
+    "language_modeling.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
+    "image_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
+    "masked_language_modeling.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
+    "multiple_choice.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
+    "object_detection.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
+    "question_answering.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
+    "semantic_segmentation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
+    "sequence_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
+    "summarization.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+    "token_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
+    "translation.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+    "video_classification.mdx": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
+}
+
+
+def get_model_list_for_task(task_guide):
+    """
+    Return the list of models supporting given task.
+    """
+    config_maping_names = TASK_GUIDE_TO_MODELS[task_guide]
+    model_names = {
+        code: name for code, name in transformers_module.MODEL_NAMES_MAPPING.items() if code in config_maping_names
+    }
+    return ", ".join([f"[{name}](../model_doc/{code})" for code, name in model_names.items()]) + "\n"
+
+
+def check_model_list_for_task(task_guide, overwrite=False):
+    """For a given task guide, checks the model list in the generated tip for consistency with the state of the lib and overwrites if needed."""
+
+    current_list, start_index, end_index, lines = _find_text_in_file(
+        filename=os.path.join(PATH_TO_TASK_GUIDES, task_guide),
+        start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->",
+        end_prompt="<!--End of the generated tip-->",
+    )
+
+    new_list = get_model_list_for_task(task_guide)
+
+    if current_list != new_list:
+        if overwrite:
+            with open(os.path.join(PATH_TO_TASK_GUIDES, task_guide), "w", encoding="utf-8", newline="\n") as f:
+                f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
+        else:
+            raise ValueError(
+                f"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
+                " to fix this."
+            )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
+    args = parser.parse_args()
+
+    for task_guide in TASK_GUIDE_TO_MODELS.keys():
+        check_model_list_for_task(task_guide, args.fix_and_overwrite)