| license: mit | |
| datasets: | |
| - bigbio/chemdner | |
| - ncbi_disease | |
| - jnlpba | |
| - bigbio/n2c2_2018_track2 | |
| - bigbio/bc5cdr | |
| language: | |
| - en | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| pipeline_tag: token-classification | |
| tags: | |
| - token-classification | |
| - biology | |
| - medical | |
| - zero-shot | |
| - few-shot | |
| # Zero and few shot NER for biomedical texts | |
| ## Model description | |
| Model takes as input two strings. String1 is NER label. String1 must be phrase for entity. String2 is short text where String1 is searched for semantically. | |
| model outputs list of zeros and ones corresponding to the occurance of NER and corresponing to tokens(tokens given by transformer tokenizer) of the Sring2, not to words. | |
| ## Example of usage | |
| ## Code availibility | |
| Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER | |
| ## Citation |