File size: 3,318 Bytes
3043053 33e5cf0 3043053 ac9207e 3043053 ac9207e 3043053 |
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-r8
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-r8
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: 0.2786
- Accuracy: 0.8847
- Precision: 0.8648
- Recall: 0.8534
- F1: 0.8588
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5623 | 1.0 | 122 | 0.5217 | 0.7268 | 0.6604 | 0.6217 | 0.6301 |
| 0.5061 | 2.0 | 244 | 0.4898 | 0.7569 | 0.7074 | 0.7105 | 0.7089 |
| 0.4443 | 3.0 | 366 | 0.4085 | 0.8120 | 0.7751 | 0.7620 | 0.7679 |
| 0.3805 | 4.0 | 488 | 0.3672 | 0.8246 | 0.7980 | 0.7609 | 0.7752 |
| 0.3488 | 5.0 | 610 | 0.3535 | 0.8521 | 0.8207 | 0.8254 | 0.8229 |
| 0.3156 | 6.0 | 732 | 0.3337 | 0.8571 | 0.8299 | 0.8214 | 0.8255 |
| 0.3055 | 7.0 | 854 | 0.3217 | 0.8622 | 0.8385 | 0.8225 | 0.8298 |
| 0.2995 | 8.0 | 976 | 0.3145 | 0.8596 | 0.8347 | 0.8207 | 0.8272 |
| 0.2825 | 9.0 | 1098 | 0.3090 | 0.8672 | 0.8402 | 0.8385 | 0.8394 |
| 0.272 | 10.0 | 1220 | 0.2992 | 0.8722 | 0.8453 | 0.8471 | 0.8462 |
| 0.2626 | 11.0 | 1342 | 0.3008 | 0.8747 | 0.8568 | 0.8338 | 0.8440 |
| 0.2641 | 12.0 | 1464 | 0.2949 | 0.8747 | 0.8488 | 0.8488 | 0.8488 |
| 0.257 | 13.0 | 1586 | 0.2885 | 0.8772 | 0.8592 | 0.8381 | 0.8475 |
| 0.2473 | 14.0 | 1708 | 0.2826 | 0.8822 | 0.8596 | 0.8542 | 0.8568 |
| 0.2456 | 15.0 | 1830 | 0.2826 | 0.8847 | 0.8609 | 0.8609 | 0.8609 |
| 0.2477 | 16.0 | 1952 | 0.2795 | 0.8847 | 0.8621 | 0.8584 | 0.8602 |
| 0.2426 | 17.0 | 2074 | 0.2794 | 0.8797 | 0.8585 | 0.8474 | 0.8526 |
| 0.2359 | 18.0 | 2196 | 0.2796 | 0.8872 | 0.8658 | 0.8602 | 0.8629 |
| 0.2417 | 19.0 | 2318 | 0.2787 | 0.8847 | 0.8648 | 0.8534 | 0.8588 |
| 0.2319 | 20.0 | 2440 | 0.2786 | 0.8847 | 0.8648 | 0.8534 | 0.8588 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
|