Text Classification
Transformers
PyTorch
xlm-roberta
classification
Generated from Trainer
text-embeddings-inference
Instructions to use Atram11/ClassificationLanguage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atram11/ClassificationLanguage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Atram11/ClassificationLanguage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Atram11/ClassificationLanguage") model = AutoModelForSequenceClassification.from_pretrained("Atram11/ClassificationLanguage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0841a1d0dc5b0756b3ff732af236599ca05c95c395766907d627daca72f675af
- Size of remote file:
- 1.11 GB
- SHA256:
- 0d3442e56e0fbc62d5c4b9a3b2ecb33f39375f51fac2564beffd4f58966089bf
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