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
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README.md
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(4) Twitter - 496K posts with hashtags XBT, Bitcoin or BTC. Collected for May 2018. Courtesy of [Paul](https://www.kaggle.com/datasets/paul92s/bitcoin-tweets-14m).
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tags:
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- sentiment classification
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datasets:
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- ElKulako/StockTwits-crypto
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(4) Twitter - 496K posts with hashtags XBT, Bitcoin or BTC. Collected for May 2018. Courtesy of [Paul](https://www.kaggle.com/datasets/paul92s/bitcoin-tweets-14m).
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datasets:
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- ElKulako/StockTwits-crypto
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