Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use saattrupdan/verdict-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saattrupdan/verdict-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="saattrupdan/verdict-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("saattrupdan/verdict-classifier") model = AutoModelForSequenceClassification.from_pretrained("saattrupdan/verdict-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from saattrupdan/verdict-classifier: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/saattrupdan/verdict-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://saattrupdan/verdict-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/saattrupdan/verdict-classifier/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- f940bf2a1d025c3f693c3b01e920d57b63db741af94b7a2478970f1747cf4469
- Size of remote file:
- 2.93 kB
- SHA256:
- 37b307abcdee41aa02e40bfc29c245f29c0c785b57aeb93b4acce07c9d5fd50a
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