File size: 3,334 Bytes
9aa1f02 72a0b5e 9aa1f02 |
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 |
---
language:
- id
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-lora-r4a0d0.1-1
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. -->
# sentiment-lora-r4a0d0.1-1
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3239
- Accuracy: 0.8622
- Precision: 0.8373
- Recall: 0.8250
- F1: 0.8307
## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5658 | 1.0 | 122 | 0.5195 | 0.7268 | 0.6646 | 0.6492 | 0.6550 |
| 0.5125 | 2.0 | 244 | 0.5060 | 0.7293 | 0.6805 | 0.6935 | 0.6855 |
| 0.4809 | 3.0 | 366 | 0.4686 | 0.7669 | 0.7184 | 0.7151 | 0.7167 |
| 0.4353 | 4.0 | 488 | 0.4295 | 0.7920 | 0.7500 | 0.7353 | 0.7417 |
| 0.4116 | 5.0 | 610 | 0.4171 | 0.8020 | 0.7628 | 0.7849 | 0.7714 |
| 0.3809 | 6.0 | 732 | 0.3865 | 0.8446 | 0.8148 | 0.8051 | 0.8096 |
| 0.3681 | 7.0 | 854 | 0.3697 | 0.8496 | 0.8193 | 0.8161 | 0.8177 |
| 0.3469 | 8.0 | 976 | 0.3554 | 0.8471 | 0.8206 | 0.8018 | 0.8102 |
| 0.3455 | 9.0 | 1098 | 0.3494 | 0.8496 | 0.8211 | 0.8111 | 0.8158 |
| 0.3284 | 10.0 | 1220 | 0.3437 | 0.8496 | 0.8289 | 0.7961 | 0.8096 |
| 0.3132 | 11.0 | 1342 | 0.3371 | 0.8596 | 0.8389 | 0.8132 | 0.8243 |
| 0.3042 | 12.0 | 1464 | 0.3371 | 0.8546 | 0.8254 | 0.8221 | 0.8238 |
| 0.3063 | 13.0 | 1586 | 0.3317 | 0.8596 | 0.8406 | 0.8107 | 0.8233 |
| 0.3013 | 14.0 | 1708 | 0.3304 | 0.8622 | 0.8373 | 0.8250 | 0.8307 |
| 0.2928 | 15.0 | 1830 | 0.3295 | 0.8596 | 0.8325 | 0.8257 | 0.8290 |
| 0.2864 | 16.0 | 1952 | 0.3284 | 0.8622 | 0.8351 | 0.8300 | 0.8325 |
| 0.2819 | 17.0 | 2074 | 0.3254 | 0.8596 | 0.8347 | 0.8207 | 0.8272 |
| 0.2877 | 18.0 | 2196 | 0.3249 | 0.8596 | 0.8336 | 0.8232 | 0.8281 |
| 0.2819 | 19.0 | 2318 | 0.3241 | 0.8647 | 0.8410 | 0.8267 | 0.8333 |
| 0.2803 | 20.0 | 2440 | 0.3239 | 0.8622 | 0.8373 | 0.8250 | 0.8307 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
|