Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned") model = AutoModelForSequenceClassification.from_pretrained("Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned/resolve/main/training_args.bin
- Command line
-
hf download hf://Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Carick/xlm-roberta-base-wordnet_combined_four-fine-tuned/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 17b67c01ec39d404a2a3a73d7b20c9bbb02bcb62515d6961708966a3e1700c0e
- Size of remote file:
- 5.43 kB
- SHA256:
- 8fca1eb82c25e0e14157fdeb21048a162e279fc608c5c9c7a5c775e3998478c4
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