Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use xszhou/test-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xszhou/test-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xszhou/test-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xszhou/test-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("xszhou/test-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b564e0604095be67b1ef3e0c492220917997a5a635a343a69b8680f81b834eac
- Size of remote file:
- 268 MB
- SHA256:
- 85e8ea2966f80a008a48456aabbaae9a190ac7238dd508268df1fcdf2c3f12f9
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