Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use Defensa2025/C2ROBERTA4060 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defensa2025/C2ROBERTA4060 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Defensa2025/C2ROBERTA4060")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Defensa2025/C2ROBERTA4060") model = AutoModelForSequenceClassification.from_pretrained("Defensa2025/C2ROBERTA4060", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a438cf235374838a5a8a31fe2d592cb5938090286c93306842fdc8611773319
- Size of remote file:
- 1.11 GB
- SHA256:
- 5d4dfbb26d6dec3a2001d7ab6aee914a2c09f3715874a6ccb02cdd8c07f226e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.