Instructions to use onionLad/identifier-deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onionLad/identifier-deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="onionLad/identifier-deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("onionLad/identifier-deberta") model = AutoModelForTokenClassification.from_pretrained("onionLad/identifier-deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from onionLad/identifier-deberta: direct link, hf CLI and curl.
- Browser
- Download file 380 Bytes
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https://huggingface.co/onionLad/identifier-deberta/resolve/main/README.md
- Command line
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hf download hf://onionLad/identifier-deberta/README.md
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curl -L -o README.md https://huggingface.co/onionLad/identifier-deberta/resolve/main/README.md
380 Bytes
| license: apache-2.0 | |
| language: | |
| - en | |
| # identifier-deberta | |
| This model is a fine-tuned version of microsoft/deberta-v3-base on data derived from the PLABA dataset. Training was performed over 3 epochs | |
| with learning rate 2e-5. The model achieves the following performance: | |
| - Validation Loss: 0.112134 | |
| - Precision: 0.455793 | |
| - Recall: 0.379442 | |
| - F1: 0.414127 | |
| - Accuracy: 0.961042 |