Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
sentiment_analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use cvnberk/crypto_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/crypto_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cvnberk/crypto_sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cvnberk/crypto_sentiment") model = AutoModelForSequenceClassification.from_pretrained("cvnberk/crypto_sentiment") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 355b69948fd193c3e99082763e9ff357f0e2d236fa1a58a11cb1c39be115ac2c
- Size of remote file:
- 4.54 kB
- SHA256:
- c87220bec2b5737acfd4ab66eb3c66b83a2ca68d4bb31691c965d487a62ed8f3
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