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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: michiyasunaga/BioLinkBERT-large
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tags:
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- generated_from_trainer
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datasets:
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- source_data
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metrics:
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- precision
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- recall
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- f1
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model-index:
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- name: SourceData_NER_v1_0_0_BioLinkBERT_large
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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: source_data
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type: source_data
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config: NER
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split: validation
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args: NER
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metrics:
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- name: Precision
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type: precision
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value: 0.8240023449604288
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- name: Recall
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type: recall
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value: 0.8621374536320355
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- name: F1
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type: f1
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value: 0.8426386519836992
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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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# SourceData_NER_v1_0_0_BioLinkBERT_large
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on the source_data dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1297
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- Accuracy Score: 0.9589
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- Precision: 0.8240
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- Recall: 0.8621
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- F1: 0.8426
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Use adafactor and the args are:
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No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 2.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
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| 0.1052 | 0.9994 | 863 | 0.1325 | 0.9569 | 0.8153 | 0.8534 | 0.8339 |
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| 0.0753 | 1.9988 | 1726 | 0.1297 | 0.9589 | 0.8240 | 0.8621 | 0.8426 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 1.13.1+cu117
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- Datasets 3.1.0
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- Tokenizers 0.20.3
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