Instructions to use baikalai/dbert-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baikalai/dbert-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="baikalai/dbert-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("baikalai/dbert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("baikalai/dbert-sentiment") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model = BertForSequenceClassification.from_pretrained("deeq/dbert-sentiment")
tokenizer = BertTokenizer.from_pretrained("deeq/dbert")
nlp = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(nlp("์ข์์"))
print(nlp("๊ธ์์"))
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