Text Classification
Transformers
PyTorch
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/stereoset_trainer_roberta-base_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/stereoset_trainer_roberta-base_finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/stereoset_trainer_roberta-base_finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/stereoset_trainer_roberta-base_finetuned") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/stereoset_trainer_roberta-base_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from henryscheible/stereoset_trainer_roberta-base_finetuned: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/henryscheible/stereoset_trainer_roberta-base_finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://henryscheible/stereoset_trainer_roberta-base_finetuned/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/henryscheible/stereoset_trainer_roberta-base_finetuned/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 10811f4ef321c3dc49d5522fd0a1a9db39bd118f5b1f5f72fd230b69b97792cf
- Size of remote file:
- 3.38 kB
- SHA256:
- 2f635e354bcea8405968d20a3595accacbc7cd55f6f7d5335254e7bfe56f7f3b
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