Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use phunganhsang/XMLRoberta_Dataset9kMetaGemini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phunganhsang/XMLRoberta_Dataset9kMetaGemini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phunganhsang/XMLRoberta_Dataset9kMetaGemini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("phunganhsang/XMLRoberta_Dataset9kMetaGemini") model = AutoModelForSequenceClassification.from_pretrained("phunganhsang/XMLRoberta_Dataset9kMetaGemini") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19b5b8c5dd8644e6e5c18cd646d118cec34bbedb1df9074c1781666bc3011308
- Size of remote file:
- 5.11 kB
- SHA256:
- 77ba9f0cd6290f7a349c39298e94f92a313fd4a8bf45b402c54be395ebd13896
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.