Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use OliverHeine/roberta-base_fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/roberta-base_fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/roberta-base_fold_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/roberta-base_fold_3") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/roberta-base_fold_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from OliverHeine/roberta-base_fold_3: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/OliverHeine/roberta-base_fold_3/resolve/main/training_args.bin
- Command line
-
hf download hf://OliverHeine/roberta-base_fold_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/OliverHeine/roberta-base_fold_3/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- a19ba99955a3447935670a44a6956cdc53f034a6e3b19c00ba2656eeec39756f
- Size of remote file:
- 5.27 kB
- SHA256:
- 3a691304151cf49dd2aecb150ca5edc0f538927d7e476f8c243eae51a060552a
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