File size: 1,907 Bytes
07c1e48
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
118f83f
 
 
 
 
 
 
07c1e48
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
118f83f
 
 
07c1e48
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
---
license: mit
base_model: facebook/esm2_t12_35M_UR50D
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: esm2_t12_35M_UR50D-finetuned-SO2F
  results: []
---

<!-- 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. -->

# esm2_t12_35M_UR50D-finetuned-SO2F

This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6696
- Accuracy: 0.7332
- Precision: 0.1614
- Recall: 0.4329
- F1: 0.2351
- Auc: 0.5987
- Mcc: 0.1329

## 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
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    | Mcc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
| No log        | 1.0   | 108  | 0.6843          | 0.7039   | 0.1240    | 0.3507 | 0.1832 | 0.5458 | 0.0605 |
| No log        | 2.0   | 216  | 0.6733          | 0.7257   | 0.1561    | 0.4301 | 0.2290 | 0.5934 | 0.1245 |
| No log        | 3.0   | 324  | 0.6696          | 0.7332   | 0.1614    | 0.4329 | 0.2351 | 0.5987 | 0.1329 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2