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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ElKulako/cryptobert") model = AutoModelForSequenceClassification.from_pretrained("ElKulako/cryptobert") - Inference
- Notebooks
- Google Colab
- Kaggle
add AIBOM
#11 opened 12 months ago
by
RiccardoDav
Adding `safetensors` variant of this model
#9 opened about 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
1
#8 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#7 opened over 1 year ago
by
SFconvertbot
Extracting all three probabilities (neutral, bearish, bullish) rather than just the one with the highest probability
1
#6 opened over 2 years ago
by
Honza70
Inquiry Regarding cryptobert Model and Related Research
2
#5 opened almost 3 years ago
by
cryptron
Adding `safetensors` variant of this model
#4 opened about 3 years ago
by
SFconvertbot
Question about accuracy
3
#3 opened over 3 years ago
by
shubham12et1062
Improving the model predictions
1
#2 opened over 3 years ago
by
petarulev
How can we extend this model to a binary classification ie Crypto related or not?
1
#1 opened almost 4 years ago
by
Hardik1347