metadata
base_model: AIRI-Institute/gena-lm-bert-base-t2t
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
results: []
gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
This model is a fine-tuned version of AIRI-Institute/gena-lm-bert-base-t2t on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7056
- F1 Score: 0.1876
- Precision: 0.5195
- Recall: 0.1145
- Accuracy: 0.4764
- Auc: 0.4935
- Prc: 0.5260
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc | Prc |
|---|---|---|---|---|---|---|---|---|---|
| 0.7004 | 0.0840 | 500 | 0.7056 | 0.1876 | 0.5195 | 0.1145 | 0.4764 | 0.4935 | 0.5260 |
| 0.6952 | 0.1680 | 1000 | 0.7056 | 0.1876 | 0.5195 | 0.1145 | 0.4764 | 0.4935 | 0.5260 |
| 0.6977 | 0.2520 | 1500 | 0.7056 | 0.1876 | 0.5195 | 0.1145 | 0.4764 | 0.4935 | 0.5260 |
| 0.7008 | 0.3360 | 2000 | 0.7056 | 0.1876 | 0.5195 | 0.1145 | 0.4764 | 0.4935 | 0.5260 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.0