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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- source_data
metrics:
- precision
- recall
- f1
model-index:
- name: SourceData_GP-CHEM-ROLES_v_1-0-1_BioLinkBERT_base
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: source_data
type: source_data
args: ROLES_MULTI
metrics:
- name: Precision
type: precision
value: 0.9617897446050288
- name: Recall
type: recall
value: 0.9671511049173801
- name: F1
type: f1
value: 0.964462973992456
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SourceData_GP-CHEM-ROLES_v_1-0-1_BioLinkBERT_base
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the source_data dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0096
- Accuracy Score: 0.9974
- Precision: 0.9618
- Recall: 0.9672
- F1: 0.9645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
| 0.006 | 1.0 | 432 | 0.0096 | 0.9974 | 0.9618 | 0.9672 | 0.9645 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0a0+bfe5ad2
- Datasets 2.10.1
- Tokenizers 0.12.1
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