Convert tutorials (#14665)

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* New syntax for colab links

* Convert a few docs

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* New syntax for colab links

docs/source/multilingual.mdx

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+<!--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
+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.
+-->
+
+# Multi-lingual models
+
+[[open-in-colab]]
+
+Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
+models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
+models.
+
+## XLM
+
+XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
+be split in two categories: the checkpoints that make use of language embeddings, and those that don't
+
+### XLM & Language Embeddings
+
+This section concerns the following checkpoints:
+
+- `xlm-mlm-ende-1024` (Masked language modeling, English-German)
+- `xlm-mlm-enfr-1024` (Masked language modeling, English-French)
+- `xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
+- `xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
+- `xlm-mlm-tlm-xnli15-1024` (Masked language modeling + Translation, XNLI languages)
+- `xlm-clm-enfr-1024` (Causal language modeling, English-French)
+- `xlm-clm-ende-1024` (Causal language modeling, English-German)
+
+These checkpoints require language embeddings that will specify the language used at inference time. These language
+embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
+these tensors depend on the language used and are identifiable using the `lang2id` and `id2lang` attributes from
+the tokenizer.
+
+Here is an example using the `xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
+
+
+```py
+>>> import torch
+>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
+
+>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
+>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
+```
+
+The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
+`lang2id` attribute:
+
+```py
+>>> print(tokenizer.lang2id)
+{'en': 0, 'fr': 1}
+```
+
+These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
+
+```py
+>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
+```
+
+We should now define the language embedding by using the previously defined language id. We want to create a tensor
+filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
+
+```py
+>>> language_id = tokenizer.lang2id['en']  # 0
+>>> langs = torch.tensor([language_id] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
+
+>>> # We reshape it to be of size (batch_size, sequence_length)
+>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
+```
+
+You can then feed it all as input to your model:
+
+```py
+>>> outputs = model(input_ids, langs=langs)
+```
+
+The example [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) can generate text
+using the CLM checkpoints from XLM, using the language embeddings.
+
+### XLM without Language Embeddings
+
+This section concerns the following checkpoints:
+
+- `xlm-mlm-17-1280` (Masked language modeling, 17 languages)
+- `xlm-mlm-100-1280` (Masked language modeling, 100 languages)
+
+These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
+representations, differently from previously-mentioned XLM checkpoints.
+
+
+## BERT
+
+BERT has two checkpoints that can be used for multi-lingual tasks:
+
+- `bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
+- `bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
+
+These checkpoints do not require language embeddings at inference time. They should identify the language used in the
+context and infer accordingly.
+
+## XLM-RoBERTa
+
+XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
+over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
+labeling and question answering.
+
+Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
+
+- `xlm-roberta-base` (Masked language modeling, 100 languages)
+- `xlm-roberta-large` (Masked language modeling, 100 languages)