Instructions to use billfass/bert-base-sentiment-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use billfass/bert-base-sentiment-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="billfass/bert-base-sentiment-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("billfass/bert-base-sentiment-classification") model = AutoModelForSequenceClassification.from_pretrained("billfass/bert-base-sentiment-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9f77cedbb9c5c451db60a4f4e68d6f4c310631f306120caa0a422624bca7e01c
- Size of remote file:
- 670 MB
- SHA256:
- fa061ecb174b2aca646b6f3554accd3e4f3c1455a7b9e518201f5ed3d78c9e20
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