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bert-base-multilingual-cased-nsmc
sangrimlee/bert-base-multilingual-cased-nsmc
bert-base-multilingual-cased-nsmc is a text classification model from sangrimlee. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tune checkpoint of bert-base-multilingual-cased, fine-tuned on NSMC(Naver Sentiment Movie Corpus).
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jun 2, 2021
- SHA
- 47fdca16
Spaces
Model card
From the Hugging Face model README
BERT multilingual basecased finetuned with NSMC
This model is a fine-tune checkpoint of bert-base-multilingual-cased, fine-tuned on NSMC(Naver Sentiment Movie Corpus).
Usage
You can use this model directly with a pipeline for sentiment-analysis:
>>> from transformers import pipeline
>>> classifier = pipeline(
"sentiment-analysis", model="sangrimlee/bert-base-multilingual-cased-nsmc"
)
>>> classifier("흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나.")
>>> classifier("액션이 없는데도 재미 있는 몇안되는 영화")
[{'label': 'negative', 'score': 0.9642567038536072}]
[{'label': 'positive', 'score': 0.9970554113388062}]
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- ko
- Created
- Mar 2, 2022
- Updated
- Jun 2, 2021