Add a direct link to the big table (#8850)

README.md

@@ -198,6 +198,8 @@ ultilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/
 1. **[Other community models](https://huggingface.co/models)**, contributed by the [community](https://huggingface.co/users).
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
+To cehck if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)
+
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations. You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
 

docs/source/index.rst

@@ -172,6 +172,8 @@ and conversion utilities for the following models:
     <https://huggingface.co/users>`__.
 
 
+.. _bigtable:
+
 The table below represents the current support in the library for each of those models, whether they have a Python
 tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
 TensorFlow and/or Flax.

utils/check_copies.py

@@ -282,7 +282,7 @@ def check_model_list_copy(overwrite=False, max_per_line=119):
     rst_list, start_index, end_index, lines = _find_text_in_file(
         filename=os.path.join(PATH_TO_DOCS, "index.rst"),
         start_prompt="    This list is updated automatically from the README",
-        end_prompt="The table below represents the current support",
+        end_prompt=".. _bigtable:",
     )
     md_list = get_model_list()
     converted_list = convert_to_rst(md_list, max_per_line=max_per_line)