Text Classification
Transformers
PyTorch
Safetensors
English
bert
Generated from Trainer
crypto
sentiment
analysis
text-embeddings-inference
Instructions to use kk08/CryptoBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kk08/CryptoBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kk08/CryptoBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kk08/CryptoBERT") model = AutoModelForSequenceClassification.from_pretrained("kk08/CryptoBERT") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8ed61cda23d4ac80e40a81f3ddd4c85c8498c504c889f4b009029eec515983a1
- Size of remote file:
- 3.58 kB
- SHA256:
- 69391455f75a3f937422568da63fc3dcaebd269af2783cffd0a8ba022bfd8694
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.