Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use PracticalWork/xlm-roberta-large-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PracticalWork/xlm-roberta-large-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PracticalWork/xlm-roberta-large-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PracticalWork/xlm-roberta-large-classifier") model = AutoModelForSequenceClassification.from_pretrained("PracticalWork/xlm-roberta-large-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from PracticalWork/xlm-roberta-large-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/PracticalWork/xlm-roberta-large-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://PracticalWork/xlm-roberta-large-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PracticalWork/xlm-roberta-large-classifier/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 07e7420e7cdced4fb7b52343a25ab0fbe91429bf54bc87d6a1316b7470eabfe2
- Size of remote file:
- 5.37 kB
- SHA256:
- 803f49867aa7f34cec7506e82900a925bb01a34cbfc0adfa4313c0054f1c38e7
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