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+ ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - jnlpba
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: electramed-small-JNLPBA-ner
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: jnlpba
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+ type: jnlpba
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+ config: jnlpba
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+ split: train
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+ args: jnlpba
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8224512128396863
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+ - name: Recall
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+ type: recall
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+ value: 0.878188899707887
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+ - name: F1
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+ type: f1
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+ value: 0.8494066679223958
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9620705451213926
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # electramed-small-JNLPBA-ner
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+
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+ This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the jnlpba dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1167
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+ - Precision: 0.8225
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+ - Recall: 0.8782
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+ - F1: 0.8494
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+ - Accuracy: 0.9621
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.398 | 1.0 | 2087 | 0.1941 | 0.7289 | 0.7936 | 0.7599 | 0.9441 |
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+ | 0.0771 | 2.0 | 4174 | 0.1542 | 0.7734 | 0.8348 | 0.8029 | 0.9514 |
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+ | 0.1321 | 3.0 | 6261 | 0.1413 | 0.7890 | 0.8492 | 0.8180 | 0.9546 |
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+ | 0.2302 | 4.0 | 8348 | 0.1326 | 0.8006 | 0.8589 | 0.8287 | 0.9562 |
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+ | 0.0723 | 5.0 | 10435 | 0.1290 | 0.7997 | 0.8715 | 0.8340 | 0.9574 |
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+ | 0.171 | 6.0 | 12522 | 0.1246 | 0.8115 | 0.8722 | 0.8408 | 0.9593 |
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+ | 0.1058 | 7.0 | 14609 | 0.1204 | 0.8148 | 0.8757 | 0.8441 | 0.9604 |
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+ | 0.1974 | 8.0 | 16696 | 0.1178 | 0.8181 | 0.8779 | 0.8470 | 0.9614 |
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+ | 0.0663 | 9.0 | 18783 | 0.1168 | 0.8239 | 0.8781 | 0.8501 | 0.9620 |
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+ | 0.1022 | 10.0 | 20870 | 0.1167 | 0.8225 | 0.8782 | 0.8494 | 0.9621 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.1
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1