Text Classification
Transformers
Safetensors
English
xlm-roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use tmnam20/xlm-roberta-base-vsfc-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/xlm-roberta-base-vsfc-10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/xlm-roberta-base-vsfc-10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/xlm-roberta-base-vsfc-10") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/xlm-roberta-base-vsfc-10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3884cd35da960075da41a7b6136dd51969af93c5fd3c2c697e541a992a14fc4b
- Size of remote file:
- 17.1 MB
- SHA256:
- b2116c05e7305eea30394284760789681c5b3440dd4cd9a8c77539da68f9e8a6
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