
# deberta-base-thai

## Model Description

This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune `deberta-base-thai` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-thai-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-thai-ud-head), and so on.

## How to Use

```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-thai")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-thai")
```


{"language": ["th"], "license": "apache-2.0", "tags": ["thai", "masked-lm", "wikipedia"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]"}
transformers pytorch deberta-v2 fill-mask thai masked-lm wikipedia th license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #fill-mask #thai #masked-lm #wikipedia #th #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# deberta-base-thai

## Model Description

This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.

## How to Use




# deberta-base-thai ## Model Description

This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on. ## How to Use
TAGS
#transformers #pytorch #deberta-v2 #fill-mask #thai #masked-lm #wikipedia #th #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # deberta-base-thai ## Model Description

This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on. ## How to Use
passage: TAGS
#transformers #pytorch #deberta-v2 #fill-mask #thai #masked-lm #wikipedia #th #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# deberta-base-thai## Model Description

This is a DeBERTa(V2) model pre-trained on Thai Wikipedia texts. NVIDIA A100-SXM4-40GB took 10 hours 17 minutes for training. You can fine-tune 'deberta-base-thai' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.## How to Use