Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use SkyR/hing-roberta-ours-run-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkyR/hing-roberta-ours-run-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkyR/hing-roberta-ours-run-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SkyR/hing-roberta-ours-run-5") model = AutoModelForSequenceClassification.from_pretrained("SkyR/hing-roberta-ours-run-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2847de9f553b3f40120b6bc2be4aa3a2dd420c54889f43c1d2a5a1b4b0b75db7
- Size of remote file:
- 1.11 GB
- SHA256:
- 0962e4f79ef4633a09bff4dd8d1a2c03a4c5bab35c52f7a3f70863a6574d4488
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.