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sd-ner

Token Classification

token classification

Inference Endpoints

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Thomas Lemberger commited on Mar 20, 2021

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@@ -51,9 +51,9 @@ The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
 Training code is available at https://github.com/source-data/soda-roberta
-- Command: `python -m tokcl.train /data/json/sd_panels NER  --num_train_epochs=3.5`
 - Tokenizer vocab size: 50265
-- Training data: EMBO/biolang MLM
 - Training with 31410 examples.
 - Evaluating on 8861 examples.
 - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY

 Training code is available at https://github.com/source-data/soda-roberta
+- Command: `python -m tokcl.train NER  --num_train_epochs=3.5`
 - Tokenizer vocab size: 50265
+- Training data: EMBO/sd-nlp NER
 - Training with 31410 examples.
 - Evaluating on 8861 examples.
 - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY