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
Question about accuracy
#3
by shubham12et1062 - opened
shubham12et1062 changed discussion title from Request: DOI to Model Accuracy is doubtful
Hello there,
You're saying that "the model is wrong because it is showing Neutral sentiment", yet on the output that you've shared, it says that the predicted label is Bearish, which is negative.
So why are you saying that it is forecasting "neutral", when it is forecasting "negative"?
Bearish means negative, so what seems to be the problem?
Best, Nick
ElKulako changed discussion title from Model Accuracy is doubtful to Question about accuracy
How much RAM /Storage do these models require If we want to deploy in ec2 instances

