Commit
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bdfdc87
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Parent(s):
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Upload tokenizer
Browse files- README.md +7 -7
- tokenizer.json +0 -9
- tokenizer_config.json +0 -8
README.md
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---
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license: apache-2.0
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datasets:
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- chrisvoncsefalvay/vaers-outcomes
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language:
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- en
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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library_name: transformers
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pipeline_tag: text-classification
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tags:
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- medical
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- pharmacovigilance
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- vaccines
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---
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DAEDRA (Detecting Adverse Event Dispositions for Regulatory Affairs) is a pharmacovigilance language model intended to facilitate the rapid identification and extraction of high-consequence outcomes from passive pharmacovigilance reporting. It was trained on the VAERS data set, and focuses on three main outcomes:
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- medical
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- pharmacovigilance
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- vaccines
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datasets:
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- chrisvoncsefalvay/vaers-outcomes
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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pipeline_tag: text-classification
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---
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DAEDRA (Detecting Adverse Event Dispositions for Regulatory Affairs) is a pharmacovigilance language model intended to facilitate the rapid identification and extraction of high-consequence outcomes from passive pharmacovigilance reporting. It was trained on the VAERS data set, and focuses on three main outcomes:
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tokenizer.json
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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{
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"id": 30522,
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"content": "Pfizer",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": true,
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"special": false
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}
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],
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"normalizer": {
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"rstrip": false,
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"normalized": false,
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"special": true
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}
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],
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"normalizer": {
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tokenizer_config.json
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"30522": {
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"content": "Pfizer",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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}
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},
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"clean_up_tokenization_spaces": true,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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