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README.md
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---
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license: apache-2.0
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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_GP-CHEM-ROLES_v_1-0-0_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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args: ROLES_MULTI
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metrics:
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- name: Precision
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type: precision
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value: 0.9572859572859573
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- name: Recall
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type: recall
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value: 0.9649457039436083
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- name: F1
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type: f1
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value: 0.9611005692599621
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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_GP-CHEM-ROLES_v_1-0-0_BioLinkBERT_large
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the source_data dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0100
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- Accuracy Score: 0.9975
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- Precision: 0.9573
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- Recall: 0.9649
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- F1: 0.9611
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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: 1.5e-05
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- train_batch_size: 32
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- eval_batch_size: 256
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 64
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- optimizer: Adafactor
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- lr_scheduler_type: linear
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- num_epochs: 1.0
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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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| 0.0068 | 1.0 | 863 | 0.0100 | 0.9975 | 0.9573 | 0.9649 | 0.9611 |
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### Framework versions
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- Transformers 4.20.1
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- Pytorch 1.11.0a0+bfe5ad2
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- Datasets 2.10.1
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- Tokenizers 0.12.1
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