Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use maticzav/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maticzav/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="maticzav/model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("maticzav/model") model = AutoModelForSequenceClassification.from_pretrained("maticzav/model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d83469f6b856bd72fd37e0773a42ba42d40fc219e2962ebb9c541a70d686857b
- Size of remote file:
- 5.27 kB
- SHA256:
- c2d0e7e28e361714bce17d1ea48001cb2efd278bb896e97b861173fe738c8379
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