File size: 2,381 Bytes
b9b363f 943af17 b9b363f 943af17 b9b363f |
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 |
---
license: mit
base_model: indobenchmark/indobert-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentiment_model
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_model
This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1665
- Accuracy: 0.9720
- F1: 0.9259
- Precision: 0.9615
- Recall: 0.8929
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 72 | 0.2060 | 0.9510 | 0.8727 | 0.8889 | 0.8571 |
| No log | 2.0 | 144 | 0.2337 | 0.9580 | 0.8846 | 0.9583 | 0.8214 |
| No log | 3.0 | 216 | 0.2416 | 0.9441 | 0.8571 | 0.8571 | 0.8571 |
| No log | 4.0 | 288 | 0.1905 | 0.9580 | 0.8846 | 0.9583 | 0.8214 |
| No log | 5.0 | 360 | 0.2029 | 0.9580 | 0.8929 | 0.8929 | 0.8929 |
| No log | 6.0 | 432 | 0.1665 | 0.9720 | 0.9259 | 0.9615 | 0.8929 |
| 0.0706 | 7.0 | 504 | 0.1899 | 0.9580 | 0.8889 | 0.9231 | 0.8571 |
| 0.0706 | 8.0 | 576 | 0.1990 | 0.9580 | 0.8889 | 0.9231 | 0.8571 |
| 0.0706 | 9.0 | 648 | 0.2139 | 0.9580 | 0.8889 | 0.9231 | 0.8571 |
| 0.0706 | 10.0 | 720 | 0.2171 | 0.9580 | 0.8889 | 0.9231 | 0.8571 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|