Text Classification
Transformers
PyTorch
English
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use dadashzadeh/cryptocurrency-intent-search-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dadashzadeh/cryptocurrency-intent-search-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dadashzadeh/cryptocurrency-intent-search-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dadashzadeh/cryptocurrency-intent-search-detection") model = AutoModelForSequenceClassification.from_pretrained("dadashzadeh/cryptocurrency-intent-search-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78a67f36d2c3ba0ee60aa0f193abdb49b9d8334f4f553a81d9019370e868dbda
- Size of remote file:
- 4.47 kB
- SHA256:
- 02193c4ffd326bad233479da4b3d24acd0c32fa98a5519f6ddcc815963520083
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.