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 pytorch_model.bin from saattrupdan/verdict-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/saattrupdan/verdict-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://saattrupdan/verdict-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/saattrupdan/verdict-classifier/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 627b3017a243d89c51bbff06a30f9f7a21e0a3fa1242cc1b7ec831ba058ab8b6
- Size of remote file:
- 1.11 GB
- SHA256:
- d164f3a954b616bc4e0557a4f17e978e6352e541597cb2630b3592957aefd81d
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