File size: 1,735 Bytes
6a12594
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---
license: mit
base_model: mhr2004/plm-nsp-100000
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: base-nsp-100000
  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. -->

# base-nsp-100000

This model is a fine-tuned version of [mhr2004/plm-nsp-100000](https://huggingface.co/mhr2004/plm-nsp-100000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8933
- Accuracy: 0.4877

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9735        | 1.0   | 183  | 0.8668          | 0.4811   |
| 0.8972        | 2.0   | 366  | 0.8636          | 0.4784   |
| 0.8381        | 3.0   | 549  | 0.8927          | 0.4613   |
| 0.8088        | 4.0   | 732  | 0.9399          | 0.4586   |
| 0.793         | 5.0   | 915  | 0.9159          | 0.4856   |
| 0.767         | 6.0   | 1098 | 0.9487          | 0.4793   |
| 0.7457        | 7.0   | 1281 | 0.9372          | 0.4946   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1