File size: 4,873 Bytes
ee11e38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- Language
- image-Emotion
- miniLM
- PyTorch
- Trainer
- SequenceClassification
- WeightedLoss
- CrossEntropyLoss
- F1Score
- HuggingFaceHub
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: miniLM_finetuned_Emotion_2024_06_17
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: F1
      type: f1
      value: 0.9349971922956838
---

<!-- 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. -->

# miniLM_finetuned_Emotion_2024_06_17

This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4059
- F1: 0.9350

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.3684        | 1.0   | 250   | 1.0416          | 0.5803 |
| 0.8635        | 2.0   | 500   | 0.6225          | 0.8729 |
| 0.5165        | 3.0   | 750   | 0.3755          | 0.9130 |
| 0.3319        | 4.0   | 1000  | 0.2792          | 0.9256 |
| 0.2494        | 5.0   | 1250  | 0.2474          | 0.9252 |
| 0.1914        | 6.0   | 1500  | 0.2182          | 0.9290 |
| 0.156         | 7.0   | 1750  | 0.2140          | 0.9307 |
| 0.1435        | 8.0   | 2000  | 0.1807          | 0.9351 |
| 0.1258        | 9.0   | 2250  | 0.1830          | 0.9353 |
| 0.1128        | 10.0  | 2500  | 0.1655          | 0.9404 |
| 0.1023        | 11.0  | 2750  | 0.1968          | 0.9339 |
| 0.0967        | 12.0  | 3000  | 0.1816          | 0.9333 |
| 0.0914        | 13.0  | 3250  | 0.1840          | 0.9338 |
| 0.0818        | 14.0  | 3500  | 0.2094          | 0.9316 |
| 0.0755        | 15.0  | 3750  | 0.1945          | 0.9345 |
| 0.0718        | 16.0  | 4000  | 0.2040          | 0.9325 |
| 0.0641        | 17.0  | 4250  | 0.2230          | 0.9369 |
| 0.0613        | 18.0  | 4500  | 0.2349          | 0.9332 |
| 0.0556        | 19.0  | 4750  | 0.2530          | 0.9249 |
| 0.0521        | 20.0  | 5000  | 0.2334          | 0.9376 |
| 0.0526        | 21.0  | 5250  | 0.2531          | 0.9306 |
| 0.0423        | 22.0  | 5500  | 0.2336          | 0.9383 |
| 0.039         | 23.0  | 5750  | 0.2848          | 0.9352 |
| 0.0435        | 24.0  | 6000  | 0.2955          | 0.9363 |
| 0.0371        | 25.0  | 6250  | 0.3075          | 0.9362 |
| 0.0338        | 26.0  | 6500  | 0.2910          | 0.9339 |
| 0.0319        | 27.0  | 6750  | 0.3133          | 0.9343 |
| 0.0305        | 28.0  | 7000  | 0.3106          | 0.9344 |
| 0.0254        | 29.0  | 7250  | 0.3155          | 0.9370 |
| 0.0288        | 30.0  | 7500  | 0.3310          | 0.9339 |
| 0.0228        | 31.0  | 7750  | 0.3463          | 0.9364 |
| 0.0224        | 32.0  | 8000  | 0.3618          | 0.9353 |
| 0.0207        | 33.0  | 8250  | 0.3720          | 0.9347 |
| 0.022         | 34.0  | 8500  | 0.3672          | 0.9374 |
| 0.0222        | 35.0  | 8750  | 0.3525          | 0.9388 |
| 0.0197        | 36.0  | 9000  | 0.3848          | 0.9384 |
| 0.0196        | 37.0  | 9250  | 0.3722          | 0.9369 |
| 0.0175        | 38.0  | 9500  | 0.3490          | 0.9350 |
| 0.0168        | 39.0  | 9750  | 0.3539          | 0.9365 |
| 0.0167        | 40.0  | 10000 | 0.3590          | 0.9391 |
| 0.0144        | 41.0  | 10250 | 0.3824          | 0.9382 |
| 0.0164        | 42.0  | 10500 | 0.3973          | 0.9322 |
| 0.0124        | 43.0  | 10750 | 0.3892          | 0.9372 |
| 0.012         | 44.0  | 11000 | 0.4102          | 0.9333 |
| 0.0142        | 45.0  | 11250 | 0.3921          | 0.9366 |
| 0.012         | 46.0  | 11500 | 0.3925          | 0.9361 |
| 0.0097        | 47.0  | 11750 | 0.3924          | 0.9360 |
| 0.0107        | 48.0  | 12000 | 0.3952          | 0.9330 |
| 0.0093        | 49.0  | 12250 | 0.4067          | 0.9360 |
| 0.0104        | 50.0  | 12500 | 0.4059          | 0.9350 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1