update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,74 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- generated_from_trainer
|
4 |
+
metrics:
|
5 |
+
- accuracy
|
6 |
+
- f1
|
7 |
+
- precision
|
8 |
+
- recall
|
9 |
+
model-index:
|
10 |
+
- name: libCap_prBERTbfd_clf
|
11 |
+
results: []
|
12 |
+
---
|
13 |
+
|
14 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
15 |
+
should probably proofread and complete it, then remove this comment. -->
|
16 |
+
|
17 |
+
# libCap_prBERTbfd_clf
|
18 |
+
|
19 |
+
This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset.
|
20 |
+
It achieves the following results on the evaluation set:
|
21 |
+
- Loss: 0.5197
|
22 |
+
- Accuracy: 0.7457
|
23 |
+
- F1: 0.7940
|
24 |
+
- Precision: 0.7567
|
25 |
+
- Recall: 0.8352
|
26 |
+
- Auroc: 0.7268
|
27 |
+
|
28 |
+
## Model description
|
29 |
+
|
30 |
+
More information needed
|
31 |
+
|
32 |
+
## Intended uses & limitations
|
33 |
+
|
34 |
+
More information needed
|
35 |
+
|
36 |
+
## Training and evaluation data
|
37 |
+
|
38 |
+
More information needed
|
39 |
+
|
40 |
+
## Training procedure
|
41 |
+
|
42 |
+
### Training hyperparameters
|
43 |
+
|
44 |
+
The following hyperparameters were used during training:
|
45 |
+
- learning_rate: 5e-05
|
46 |
+
- train_batch_size: 64
|
47 |
+
- eval_batch_size: 64
|
48 |
+
- seed: 42
|
49 |
+
- gradient_accumulation_steps: 64
|
50 |
+
- total_train_batch_size: 4096
|
51 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
52 |
+
- lr_scheduler_type: linear
|
53 |
+
- num_epochs: 8
|
54 |
+
|
55 |
+
### Training results
|
56 |
+
|
57 |
+
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Auroc |
|
58 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
|
59 |
+
| No log | 0.98 | 34 | 0.6393 | 0.6396 | 0.7053 | 0.6782 | 0.7345 | 0.6197 |
|
60 |
+
| No log | 1.98 | 68 | 0.5713 | 0.6962 | 0.7499 | 0.7256 | 0.7759 | 0.6795 |
|
61 |
+
| No log | 2.98 | 102 | 0.5652 | 0.7126 | 0.7388 | 0.7918 | 0.6924 | 0.7168 |
|
62 |
+
| No log | 3.98 | 136 | 0.5360 | 0.7330 | 0.7896 | 0.7345 | 0.8536 | 0.7076 |
|
63 |
+
| No log | 4.98 | 170 | 0.5223 | 0.7423 | 0.7830 | 0.7740 | 0.7921 | 0.7318 |
|
64 |
+
| No log | 5.98 | 204 | 0.5180 | 0.7454 | 0.7882 | 0.7699 | 0.8075 | 0.7323 |
|
65 |
+
| No log | 6.98 | 238 | 0.5179 | 0.7440 | 0.7934 | 0.7537 | 0.8376 | 0.7243 |
|
66 |
+
| No log | 7.98 | 272 | 0.5197 | 0.7457 | 0.7940 | 0.7567 | 0.8352 | 0.7268 |
|
67 |
+
|
68 |
+
|
69 |
+
### Framework versions
|
70 |
+
|
71 |
+
- Transformers 4.21.1
|
72 |
+
- Pytorch 1.12.0+cu113
|
73 |
+
- Datasets 2.4.0
|
74 |
+
- Tokenizers 0.12.1
|