Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use phunganhsang/XLM_CITA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phunganhsang/XLM_CITA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="phunganhsang/XLM_CITA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("phunganhsang/XLM_CITA") model = AutoModelForSequenceClassification.from_pretrained("phunganhsang/XLM_CITA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f6010c4fd6fdc7cd61d78eaf83a2c1d3b717cf0f3236f3c3f2cfe818e130baf
- Size of remote file:
- 5.3 kB
- SHA256:
- 91808a954990642a994e9644d37cc10a69836f231396b1a27f3e601c030b0a55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.