SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

8
docs/source/en/model_doc/visual_bert.md
View File

@@ -32,7 +32,9 @@ simpler. Further analysis demonstrates that VisualBERT can ground elements of la
explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between
verbs and image regions corresponding to their arguments.*
Tips:
This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/uclanlp/visualbert).
## Usage tips
1. Most of the checkpoints provided work with the [`VisualBertForPreTraining`] configuration. Other
checkpoints provided are the fine-tuned checkpoints for down-stream tasks - VQA ('visualbert-vqa'), VCR
@@ -43,8 +45,6 @@ Tips:
We do not provide the detector and its weights as a part of the package, but it will be available in the research
projects, and the states can be loaded directly into the detector provided.
## Usage
VisualBERT is a multi-modal vision and language model. It can be used for visual question answering, multiple choice,
visual reasoning and region-to-phrase correspondence tasks. VisualBERT uses a BERT-like transformer to prepare
embeddings for image-text pairs. Both the text and visual features are then projected to a latent space with identical
@@ -92,8 +92,6 @@ The following example shows how to get the last hidden state using [`VisualBertM
>>> last_hidden_state = outputs.last_hidden_state
```
This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/uclanlp/visualbert).
## VisualBertConfig
[[autodoc]] VisualBertConfig