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
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 1,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into 65 languages with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/).
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It achieves the following results on the evaluation set, being 1,000 such verdicts, but here including duplicates to represent the true distribution:
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# Multilingual Verdict Classifier
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 1,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into 65 languages with the [Google Cloud Translation API](https://cloud.google.com/translate/docs/reference/rest/).
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It achieves the following results on the evaluation set, being 1,000 such verdicts, but here including duplicates to represent the true distribution:
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