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[Docs] Model_doc structure/clarity improvements (#26876)

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* first batch of structure improvements for model_docs

* second batch of structure improvements for model_docs

* more structure improvements for model_docs

* more structure improvements for model_docs

* structure improvements for cv model_docs

* more structural refactoring

* addressed feedback about image processors
This commit is contained in:
Maria Khalusova
2023-11-03 10:57:03 -04:00
committed by GitHub
parent ad8ff96224
commit 5964f820db
223 changed files with 1796 additions and 1116 deletions
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docs/source/en/model_doc/luke.md
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@@ -37,7 +37,9 @@ state-of-the-art results on five well-known datasets: Open Entity (entity typing
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question
answering).*
Tips:
This model was contributed by [ikuyamada](https://huggingface.co/ikuyamada) and [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/studio-ousia/luke).
## Usage tips
- This implementation is the same as [`RobertaModel`] with the addition of entity embeddings as well
as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
@@ -75,13 +77,7 @@ Tips:
head models by specifying `task="entity_classification"`, `task="entity_pair_classification"`, or
`task="entity_span_classification"`. Please refer to the example code of each head models.
A demo notebook on how to fine-tune [`LukeForEntityPairClassification`] for relation
classification can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LUKE).
There are also 3 notebooks available, which showcase how you can reproduce the results as reported in the paper with
the HuggingFace implementation of LUKE. They can be found [here](https://github.com/studio-ousia/luke/tree/master/notebooks).
Example:
Usage example:
```python
>>> from transformers import LukeTokenizer, LukeModel, LukeForEntityPairClassification
@@ -119,10 +115,10 @@ Example:
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
```
This model was contributed by [ikuyamada](https://huggingface.co/ikuyamada) and [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/studio-ousia/luke).
## Documentation resources
## Resources
- [A demo notebook on how to fine-tune [`LukeForEntityPairClassification`] for relation classification](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LUKE)
- [Notebooks showcasing how you to reproduce the results as reported in the paper with the HuggingFace implementation of LUKE](https://github.com/studio-ousia/luke/tree/master/notebooks)
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)