File size: 2,204 Bytes
bc56a75 df4c369 dd7ffb4 df4c369 bc56a75 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
model-index:
- name: bert_emo_classifier
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. -->
# bert_emo_classifier
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3768
## Target Labels
label: a classification label, with possible values including
- sadness : 0
- joy : 1
- love : 2
- anger : 3
- fear : 4
- surprise : 5
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1497 | 0.25 | 500 | 0.2911 |
| 0.1221 | 0.5 | 1000 | 0.3190 |
| 0.108 | 0.75 | 1500 | 0.3343 |
| 0.1296 | 1.0 | 2000 | 0.2803 |
| 0.0611 | 1.25 | 2500 | 0.3392 |
| 0.0651 | 1.5 | 3000 | 0.3400 |
| 0.0588 | 1.75 | 3500 | 0.3733 |
| 0.0993 | 2.0 | 4000 | 0.3672 |
| 0.0385 | 2.25 | 4500 | 0.4041 |
| 0.0509 | 2.5 | 5000 | 0.3906 |
| 0.0651 | 2.75 | 5500 | 0.3809 |
| 0.0693 | 3.0 | 6000 | 0.3944 |
| 0.0471 | 3.25 | 6500 | 0.3926 |
| 0.0462 | 3.5 | 7000 | 0.3837 |
| 0.0326 | 3.75 | 7500 | 0.3752 |
| 0.0233 | 4.0 | 8000 | 0.3768 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.10.3
|