abk20031218/checkpoints

1---2library_name: transformers3license: mit4base_model: facebook/esm2_t12_35M_UR50D5tags:6- generated_from_trainer7metrics:8- accuracy9- precision10- recall11- f112model-index:13- name: checkpoints14  results: []15---16 17<!-- This model card has been generated automatically according to the information the Trainer had access to. You18should probably proofread and complete it, then remove this comment. -->19 20# checkpoints21 22This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.23It achieves the following results on the evaluation set:24- Loss: 0.677325- Accuracy: 0.725326- Precision: 0.363327- Recall: 0.492328- F1: 0.418029 30## Model description31 32More information needed33 34## Intended uses & limitations35 36More information needed37 38## Training and evaluation data39 40More information needed41 42## Training procedure43 44### Training hyperparameters45 46The following hyperparameters were used during training:47- learning_rate: 2e-0548- train_batch_size: 849- eval_batch_size: 850- seed: 4251- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments52- lr_scheduler_type: linear53- num_epochs: 554- mixed_precision_training: Native AMP55 56### Training results57 58| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |59|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|60| 0.6281        | 1.0   | 1028 | 0.6312          | 0.7622   | 0.4035    | 0.3903 | 0.3968 |61| 0.5836        | 2.0   | 2056 | 0.6297          | 0.7553   | 0.3973    | 0.4273 | 0.4118 |62| 0.5832        | 3.0   | 3084 | 0.6605          | 0.7754   | 0.4326    | 0.3880 | 0.4091 |63| 0.5177        | 4.0   | 4112 | 0.6891          | 0.7604   | 0.4052    | 0.4179 | 0.4114 |64| 0.5016        | 5.0   | 5140 | 0.6773          | 0.7253   | 0.3633    | 0.4923 | 0.4180 |65 66 67### Framework versions68 69- Transformers 5.13.170- Pytorch 2.11.0+cu12871- Datasets 4.0.072- Tokenizers 0.22.273