File size: 3,322 Bytes
6c97110 02456bb 6c97110 |
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-pt-pl30-1
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-pt-pl30-1
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: 0.3434
- Accuracy: 0.8722
- Precision: 0.8485
- Recall: 0.8396
- F1: 0.8438
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5472 | 1.0 | 122 | 0.4993 | 0.7343 | 0.6726 | 0.6245 | 0.6339 |
| 0.4484 | 2.0 | 244 | 0.4157 | 0.7945 | 0.7655 | 0.8096 | 0.7744 |
| 0.3338 | 3.0 | 366 | 0.3279 | 0.8596 | 0.8510 | 0.7982 | 0.8179 |
| 0.2902 | 4.0 | 488 | 0.3037 | 0.8672 | 0.8449 | 0.8285 | 0.8360 |
| 0.2756 | 5.0 | 610 | 0.2922 | 0.8747 | 0.8499 | 0.8463 | 0.8481 |
| 0.2514 | 6.0 | 732 | 0.3059 | 0.8672 | 0.8359 | 0.8560 | 0.8446 |
| 0.2338 | 7.0 | 854 | 0.2970 | 0.8596 | 0.8278 | 0.8432 | 0.8347 |
| 0.2205 | 8.0 | 976 | 0.2967 | 0.8847 | 0.8784 | 0.8359 | 0.8531 |
| 0.2153 | 9.0 | 1098 | 0.2982 | 0.8672 | 0.8393 | 0.8410 | 0.8402 |
| 0.1969 | 10.0 | 1220 | 0.2943 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
| 0.185 | 11.0 | 1342 | 0.2973 | 0.8647 | 0.8359 | 0.8392 | 0.8376 |
| 0.1733 | 12.0 | 1464 | 0.3074 | 0.8672 | 0.8423 | 0.8335 | 0.8377 |
| 0.1616 | 13.0 | 1586 | 0.3186 | 0.8697 | 0.8460 | 0.8353 | 0.8404 |
| 0.16 | 14.0 | 1708 | 0.3222 | 0.8596 | 0.8278 | 0.8432 | 0.8347 |
| 0.1494 | 15.0 | 1830 | 0.3260 | 0.8747 | 0.8523 | 0.8413 | 0.8465 |
| 0.1501 | 16.0 | 1952 | 0.3233 | 0.8647 | 0.8359 | 0.8392 | 0.8376 |
| 0.1468 | 17.0 | 2074 | 0.3296 | 0.8672 | 0.8412 | 0.8360 | 0.8385 |
| 0.1423 | 18.0 | 2196 | 0.3367 | 0.8647 | 0.8398 | 0.8292 | 0.8342 |
| 0.1327 | 19.0 | 2318 | 0.3395 | 0.8697 | 0.8438 | 0.8403 | 0.8420 |
| 0.1413 | 20.0 | 2440 | 0.3434 | 0.8722 | 0.8485 | 0.8396 | 0.8438 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.15.2
|