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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/m2m_100.md
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@@ -38,7 +38,7 @@ open-source our scripts so that others may reproduce the data, evaluation, and f
This model was contributed by [valhalla](https://huggingface.co/valhalla).
### Training and Generation
## Usage tips and examples
M2M100 is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation tasks. As the model is
multilingual it expects the sequences in a certain format: A special language id token is used as prefix in both the
@@ -48,7 +48,7 @@ id for source text and target language id for target text, with `X` being the so
The [`M2M100Tokenizer`] depends on `sentencepiece` so be sure to install it before running the
examples. To install `sentencepiece` run `pip install sentencepiece`.
- Supervised Training
**Supervised Training**
```python
from transformers import M2M100Config, M2M100ForConditionalGeneration, M2M100Tokenizer
@@ -64,12 +64,12 @@ model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
loss = model(**model_inputs).loss # forward pass
```
- Generation
**Generation**
M2M100 uses the `eos_token_id` as the `decoder_start_token_id` for generation with the target language id
being forced as the first generated token. To force the target language id as the first generated token, pass the
*forced_bos_token_id* parameter to the *generate* method. The following example shows how to translate between
Hindi to French and Chinese to English using the *facebook/m2m100_418M* checkpoint.
M2M100 uses the `eos_token_id` as the `decoder_start_token_id` for generation with the target language id
being forced as the first generated token. To force the target language id as the first generated token, pass the
*forced_bos_token_id* parameter to the *generate* method. The following example shows how to translate between
Hindi to French and Chinese to English using the *facebook/m2m100_418M* checkpoint.
```python
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
@@ -95,7 +95,7 @@ loss = model(**model_inputs).loss # forward pass
"Life is like a box of chocolate."
```
## Documentation resources
## Resources
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization)