|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- source_data |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: SourceData_GP-CHEM-ROLES_v_1-0-0_BioLinkBERT_large |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: source_data |
|
type: source_data |
|
args: ROLES_MULTI |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.9572859572859573 |
|
- name: Recall |
|
type: recall |
|
value: 0.9649457039436083 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9611005692599621 |
|
--- |
|
|
|
<!-- 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_1-0-0_BioLinkBERT_large |
|
|
|
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: 0.0100 |
|
- Accuracy Score: 0.9975 |
|
- Precision: 0.9573 |
|
- Recall: 0.9649 |
|
- F1: 0.9611 |
|
|
|
## 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: 1.5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 256 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adafactor |
|
- lr_scheduler_type: linear |
|
- num_epochs: 1.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:| |
|
| 0.0068 | 1.0 | 863 | 0.0100 | 0.9975 | 0.9573 | 0.9649 | 0.9611 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.20.1 |
|
- Pytorch 1.11.0a0+bfe5ad2 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.12.1 |
|
|
