Instructions to use sangrimlee/bert-base-multilingual-cased-nsmc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sangrimlee/bert-base-multilingual-cased-nsmc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sangrimlee/bert-base-multilingual-cased-nsmc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sangrimlee/bert-base-multilingual-cased-nsmc") model = AutoModelForSequenceClassification.from_pretrained("sangrimlee/bert-base-multilingual-cased-nsmc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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("ν ...ν¬μ€ν°λ³΄κ³ μ΄λ©μνμ€....μ€λ²μ°κΈ°μ‘°μ°¨ κ°λ³μ§ μꡬλ.")
>>> classifier("μ‘μ
μ΄ μλλ°λ μ¬λ―Έ μλ λͺμλλ μν")
[{'label': 'negative', 'score': 0.9642567038536072}]
[{'label': 'positive', 'score': 0.9970554113388062}]
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