Text Classification
Transformers
Safetensors
Chinese
bert
biomedical
pharmacovigilance
adverse-drug-reaction
meddra
chinese
macbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use xumingjun/mention2meddra-macbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xumingjun/mention2meddra-macbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xumingjun/mention2meddra-macbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xumingjun/mention2meddra-macbert-base") model = AutoModelForSequenceClassification.from_pretrained("xumingjun/mention2meddra-macbert-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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The public code repository contains the template and evaluation utilities:
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https://github.com/xumingjun5208/mention2meddra
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## Files Included
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- `model.safetensors`: fine-tuned sequence-classification weights
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- `config.json`: sanitized model configuration with explicit label names
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- `tokenizer.json`, `vocab.txt`, `tokenizer_config.json`, `special_tokens_map.json`: tokenizer files
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- `.gitattributes`: Git LFS rules for model artifacts
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## Files Intentionally Excluded
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- real adverse-event records
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- expert annotation files
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- licensed MedDRA dictionary files
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- training, development, or test JSONL/CSV files
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- optimizer, scheduler, random-state, and trainer-state checkpoints
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- downstream FAERS, JADER, or regional pharmacovigilance analysis data
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## Citation
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Please cite the associated Journal of Biomedical Informatics manuscript once available.
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The public code repository contains the template and evaluation utilities:
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https://github.com/xumingjun5208/mention2meddra
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