File size: 2,133 Bytes
2297bf7
354bbad
2297bf7
354bbad
 
 
 
 
 
 
2297bf7
 
354bbad
 
2297bf7
354bbad
2297bf7
354bbad
 
6913a83
 
2297bf7
354bbad
2297bf7
354bbad
2297bf7
354bbad
2297bf7
354bbad
2297bf7
354bbad
2297bf7
354bbad
2297bf7
 
 
354bbad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6913a83
 
 
 
 
 
 
 
 
 
2297bf7
 
 
 
6913a83
 
 
 
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
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-lora-text-classification
  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. -->

# distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0099
- Accuracy: {'accuracy': 0.888}

## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy            |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log        | 1.0   | 250  | 0.3473          | {'accuracy': 0.874} |
| 0.4088        | 2.0   | 500  | 0.5087          | {'accuracy': 0.873} |
| 0.4088        | 3.0   | 750  | 0.6246          | {'accuracy': 0.866} |
| 0.2221        | 4.0   | 1000 | 0.7013          | {'accuracy': 0.887} |
| 0.2221        | 5.0   | 1250 | 0.7331          | {'accuracy': 0.876} |
| 0.1013        | 6.0   | 1500 | 0.8383          | {'accuracy': 0.88}  |
| 0.1013        | 7.0   | 1750 | 0.8908          | {'accuracy': 0.886} |
| 0.0269        | 8.0   | 2000 | 1.0219          | {'accuracy': 0.884} |
| 0.0269        | 9.0   | 2250 | 1.0187          | {'accuracy': 0.878} |
| 0.0102        | 10.0  | 2500 | 1.0099          | {'accuracy': 0.888} |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1