Text Classification
Transformers
PyTorch
Safetensors
English
roberta
cryptocurrency
crypto
BERT
sentiment classification
NLP
bitcoin
ethereum
shib
social media
sentiment analysis
cryptocurrency sentiment analysis
text-embeddings-inference
Instructions to use ElKulako/cryptobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ElKulako/cryptobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ElKulako/cryptobert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElKulako/cryptobert") model = AutoModelForSequenceClassification.from_pretrained("ElKulako/cryptobert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ElKulako/cryptobert: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ElKulako/cryptobert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ElKulako/cryptobert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ElKulako/cryptobert/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- cc0f4e920cf374f12689aefa8556da998683d02a920237d865b815654604558d
- Size of remote file:
- 499 MB
- SHA256:
- 80aaa3c6d754b07e5dd23e4dbe93d7adf9d053d4f132ef556ec14668e371743d
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