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+ ---
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+ license: mit
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+ base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
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+ tags:
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+ - generated_from_trainer
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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: PubMedBERT_JNLPBA_NER_new
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+ results: []
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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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+ # PubMedBERT_JNLPBA_NER_new
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+
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+ This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1464
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+ - Precision: 0.9598
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+ - Recall: 0.9557
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+ - F1: 0.9577
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+ - Accuracy: 0.9520
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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: 3
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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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+ | 0.1709 | 1.0 | 1164 | 0.1493 | 0.9566 | 0.9534 | 0.9550 | 0.9492 |
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+ | 0.134 | 2.0 | 2328 | 0.1501 | 0.9585 | 0.9549 | 0.9567 | 0.9501 |
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+ | 0.112 | 3.0 | 3492 | 0.1464 | 0.9598 | 0.9557 | 0.9577 | 0.9520 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0