File size: 1,923 Bytes
527707d
 
 
 
 
 
 
 
9a16e81
527707d
 
 
 
 
9a16e81
527707d
 
 
 
 
 
 
9a16e81
2fa671a
9a16e81
527707d
 
 
 
 
 
 
 
 
2fa671a
 
527707d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2fa671a
 
 
 
 
527707d
 
 
 
 
 
 
 
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
73
74
75
76
77
78
79
80
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
base_model: distilbert-base-uncased
model-index:
- name: kd-distilBERT-clinc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: clinc_oos
      type: clinc_oos
      config: plus
      split: train
      args: plus
    metrics:
    - type: accuracy
      value: 0.9129032258064517
      name: Accuracy
---

<!-- 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. -->

# kd-distilBERT-clinc

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7752
- Accuracy: 0.9129

## 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.3211        | 1.0   | 318  | 3.3313          | 0.7235   |
| 2.6568        | 2.0   | 636  | 1.9016          | 0.8452   |
| 1.5575        | 3.0   | 954  | 1.1668          | 0.8955   |
| 1.0094        | 4.0   | 1272 | 0.8619          | 0.9087   |
| 0.7914        | 5.0   | 1590 | 0.7752          | 0.9129   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2