File size: 2,235 Bytes
94ab53d ed974ef 94ab53d e1dee94 94ab53d e1dee94 94ab53d e1dee94 94ab53d ed974ef 94ab53d ed974ef 94ab53d ed974ef e1dee94 94ab53d ed974ef 94ab53d |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: jq_emo_distilbert
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9385
---
<!-- 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. -->
# jq_emo_distilbert
This model is a fine-tuned version of [tingtone/jq_emo_distilbert](https://huggingface.co/tingtone/jq_emo_distilbert) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3185
- Accuracy: 0.9385
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1042 | 1.0 | 1000 | 0.1816 | 0.932 |
| 0.0998 | 2.0 | 2000 | 0.1799 | 0.934 |
| 0.0957 | 3.0 | 3000 | 0.2015 | 0.935 |
| 0.0846 | 4.0 | 4000 | 0.2129 | 0.9335 |
| 0.0943 | 5.0 | 5000 | 0.2215 | 0.935 |
| 0.075 | 6.0 | 6000 | 0.2627 | 0.9375 |
| 0.0607 | 7.0 | 7000 | 0.2908 | 0.9345 |
| 0.0636 | 8.0 | 8000 | 0.3207 | 0.935 |
| 0.0953 | 9.0 | 9000 | 0.3165 | 0.936 |
| 0.0748 | 10.0 | 10000 | 0.3185 | 0.9385 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|