
# BERT multilingual basecased finetuned with NSMC

This model is a fine-tune checkpoint of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased), fine-tuned on [NSMC(Naver Sentiment Movie Corpus)](https://github.com/e9t/nsmc).

## Usage

You can use this model directly with a pipeline for sentiment-analysis:

```python
>>> 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}]
```

{"language": "ko"}
transformers pytorch bert text-classification ko autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #region-us 


# 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:



# 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:
TAGS
#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #region-us 
 # 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:
passage: TAGS
#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #region-us 
# 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: