Instructions to use Josef0801/model_1_deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Josef0801/model_1_deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Josef0801/model_1_deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Josef0801/model_1_deberta") model = AutoModelForSequenceClassification.from_pretrained("Josef0801/model_1_deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Josef0801/model_1_deberta: direct link, hf CLI and curl.
- Browser
- Download file 589 Bytes
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https://huggingface.co/Josef0801/model_1_deberta/resolve/main/README.md
- Command line
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hf download hf://Josef0801/model_1_deberta/README.md
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curl -L -o README.md https://huggingface.co/Josef0801/model_1_deberta/resolve/main/README.md
589 Bytes
| This model is based on svenbl80/deberta-v3-Base-finetuned-chatdoc-V5's model but further finetuned a synthetic dataset. | |
| It performs poorly on a different benchmark from the same document: | |
| precision recall f1-score support | |
| 0 0.19 0.22 0.20 23 | |
| 1 0.62 0.44 0.52 75 | |
| 2 0.00 0.00 0.00 19 | |
| accuracy 0.32 117 | |
| macro avg 0.27 0.22 0.24 117 | |
| weighted avg 0.44 0.32 0.37 117 | |