mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-02 12:20:05 +06:00
Update roc bert docs (#38835)
* Moved the sources to the right * small Changes * Some Changes to moonshine * Added the install to pipline * updated the monshine model card * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Updated Documentation According to changes * Fixed the model with the commits * Changes to the roc_bert * Final Update to the branch * Adds Quantizaiton to the model * Finsihed Fixing the Roc_bert docs * Fixed Moshi * Fixed Problems * Fixed Problems * Fixed Problems * Fixed Problems * Fixed Problems * Fixed Problems * Added the install to pipline * updated the monshine model card * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/moonshine.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Updated Documentation According to changes * Fixed the model with the commits * Fixed the problems * Final Fix * Final Fix * Final Fix * Update roc_bert.md --------- Co-authored-by: Your Name <sohamprabhu@Mac.fios-router.home> Co-authored-by: Your Name <sohamprabhu@Sohams-MacBook-Air.local> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
This commit is contained in:
SohamPrabhu
GitHub
parent
3ae52cc312
commit
a396f4324b
@ -14,39 +14,78 @@ rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# RoCBert
|
||||
|
||||
<div class="flex flex-wrap space-x-1">
|
||||
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
||||
<div style="float: right;">
|
||||
<div class="flex flex-wrap space-x-1">
|
||||
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
||||
</div>
|
||||
</div>
|
||||
|
||||
## Overview
|
||||
# RoCBert
|
||||
|
||||
The RoCBert model was proposed in [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
|
||||
It's a pretrained Chinese language model that is robust under various forms of adversarial attacks.
|
||||
[RoCBert](https://aclanthology.org/2022.acl-long.65.pdf) is a pretrained Chinese [BERT](./bert) model designed against adversarial attacks like typos and synonyms. It is pretrained with a contrastive learning objective to align normal and adversarial text examples. The examples include different semantic, phonetic, and visual features of Chinese. This makes RoCBert more robust against manipulation.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
You can find all the original RoCBert checkpoints under the [weiweishi](https://huggingface.co/weiweishi) profile.
|
||||
|
||||
*Large-scale pretrained language models have achieved SOTA results on NLP tasks. However, they have been shown
|
||||
vulnerable to adversarial attacks especially for logographic languages like Chinese. In this work, we propose
|
||||
ROCBERT: a pretrained Chinese Bert that is robust to various forms of adversarial attacks like word perturbation,
|
||||
synonyms, typos, etc. It is pretrained with the contrastive learning objective which maximizes the label consistency
|
||||
under different synthesized adversarial examples. The model takes as input multimodal information including the
|
||||
semantic, phonetic and visual features. We show all these features are important to the model robustness since the
|
||||
attack can be performed in all the three forms. Across 5 Chinese NLU tasks, ROCBERT outperforms strong baselines under
|
||||
three blackbox adversarial algorithms without sacrificing the performance on clean testset. It also performs the best
|
||||
in the toxic content detection task under human-made attacks.*
|
||||
> [!TIP]
|
||||
> This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
|
||||
>
|
||||
> Click on the RoCBert models in the right sidebar for more examples of how to apply RoCBert to different Chinese language tasks.
|
||||
|
||||
This model was contributed by [weiweishi](https://huggingface.co/weiweishi).
|
||||
The example below demonstrates how to predict the [MASK] token with [`Pipeline`], [`AutoModel`], and from the command line.
|
||||
|
||||
## Resources
|
||||
<hfoptions id="usage">
|
||||
<hfoption id="Pipeline">
|
||||
|
||||
- [Text classification task guide](../tasks/sequence_classification)
|
||||
- [Token classification task guide](../tasks/token_classification)
|
||||
- [Question answering task guide](../tasks/question_answering)
|
||||
- [Causal language modeling task guide](../tasks/language_modeling)
|
||||
- [Masked language modeling task guide](../tasks/masked_language_modeling)
|
||||
- [Multiple choice task guide](../tasks/multiple_choice)
|
||||
```py
|
||||
import torch
|
||||
from transformers import pipeline
|
||||
|
||||
pipeline = pipeline(
|
||||
task="fill-mask",
|
||||
model="weiweishi/roc-bert-base-zh",
|
||||
torch_dtype=torch.float16,
|
||||
device=0
|
||||
)
|
||||
pipeline("這家餐廳的拉麵是我[MASK]過的最好的拉麵之")
|
||||
```
|
||||
|
||||
</hfoption>
|
||||
<hfoption id="AutoModel">
|
||||
|
||||
```py
|
||||
import torch
|
||||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"weiweishi/roc-bert-base-zh",
|
||||
)
|
||||
model = AutoModelForMaskedLM.from_pretrained(
|
||||
"weiweishi/roc-bert-base-zh",
|
||||
torch_dtype=torch.float16,
|
||||
device_map="auto",
|
||||
)
|
||||
inputs = tokenizer("這家餐廳的拉麵是我[MASK]過的最好的拉麵之", return_tensors="pt").to("cuda")
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
predictions = outputs.logits
|
||||
|
||||
masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
|
||||
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
|
||||
predicted_token = tokenizer.decode(predicted_token_id)
|
||||
|
||||
print(f"The predicted token is: {predicted_token}")
|
||||
```
|
||||
|
||||
</hfoption>
|
||||
<hfoption id="transformers CLI">
|
||||
|
||||
```bash
|
||||
echo -e "這家餐廳的拉麵是我[MASK]過的最好的拉麵之" | transformers-cli run --task fill-mask --model weiweishi/roc-bert-base-zh --device 0
|
||||
```
|
||||
|
||||
</hfoption>
|
||||
</hfoptions>
|
||||
|
||||
## RoCBertConfig
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user