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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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+ - species_800
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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-SPECIES800-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: species_800
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+ type: species_800
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+ config: species_800
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+ split: train
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+ args: species_800
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.6221498371335505
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+ - name: Recall
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+ type: recall
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+ value: 0.7470664928292047
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+ - name: F1
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+ type: f1
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+ value: 0.6789099526066352
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9831434110359828
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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-SPECIES800-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 species_800 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0513
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+ - Precision: 0.6221
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+ - Recall: 0.7471
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+ - F1: 0.6789
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+ - Accuracy: 0.9831
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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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+ | 0.0536 | 1.0 | 359 | 0.0971 | 0.6138 | 0.5554 | 0.5832 | 0.9795 |
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+ | 0.0309 | 2.0 | 718 | 0.0692 | 0.6175 | 0.6063 | 0.6118 | 0.9808 |
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+ | 0.0563 | 3.0 | 1077 | 0.0582 | 0.6424 | 0.6910 | 0.6658 | 0.9819 |
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+ | 0.0442 | 4.0 | 1436 | 0.0553 | 0.5900 | 0.7523 | 0.6613 | 0.9814 |
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+ | 0.0069 | 5.0 | 1795 | 0.0511 | 0.6291 | 0.7497 | 0.6841 | 0.9827 |
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+ | 0.0141 | 6.0 | 2154 | 0.0505 | 0.6579 | 0.7471 | 0.6996 | 0.9837 |
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+ | 0.0052 | 7.0 | 2513 | 0.0513 | 0.5965 | 0.7458 | 0.6628 | 0.9826 |
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+ | 0.0573 | 8.0 | 2872 | 0.0509 | 0.6140 | 0.7445 | 0.6730 | 0.9828 |
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+ | 0.0203 | 9.0 | 3231 | 0.0516 | 0.6118 | 0.7458 | 0.6722 | 0.9830 |
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+ | 0.0101 | 10.0 | 3590 | 0.0513 | 0.6221 | 0.7471 | 0.6789 | 0.9831 |
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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