Instructions to use Josef0801/model_deberta_3_labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Josef0801/model_deberta_3_labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Josef0801/model_deberta_3_labels")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Josef0801/model_deberta_3_labels") model = AutoModelForSequenceClassification.from_pretrained("Josef0801/model_deberta_3_labels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Josef0801/model_deberta_3_labels: direct link, hf CLI and curl.
- Browser
- Download file 953 Bytes
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https://huggingface.co/Josef0801/model_deberta_3_labels/resolve/main/README.md
- Command line
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hf download hf://Josef0801/model_deberta_3_labels/README.md
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curl -L -o README.md https://huggingface.co/Josef0801/model_deberta_3_labels/resolve/main/README.md
953 Bytes
Based on microsoft/deberta-v3-base, finetuned on a synthetic dataset (6 labels were converted to 3 labels).
Performance on test dataset: precision recall f1-score support
0 0.98 0.99 0.98 94
1 0.96 0.96 0.96 28
2 1.00 0.98 0.99 66
accuracy 0.98 188
macro avg 0.98 0.98 0.98 188
weighted avg 0.98 0.98 0.98 188
Performance on similar benchmark: precision recall f1-score support
0 0.13 0.52 0.21 23
1 0.44 0.15 0.22 75
2 0.00 0.00 0.00 19
accuracy 0.20 117
macro avg 0.19 0.22 0.14 117
weighted avg 0.31 0.20 0.18 117