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---
base_model: xxxxxxxxx
tags:
- generated_from_trainer
datasets:
- AmazonScience/massive
metrics:
- f1
model-index:
- name: massive_indo
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. -->
# massive_indo
This model is a fine-tuned version of [xxxxxxxxx](https://huggingface.co/xxxxxxxxx) on the massive dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0967
- F1: 0.8702
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.747 | 1.39 | 500 | 1.0303 | 0.5703 |
| 0.5618 | 2.78 | 1000 | 0.9201 | 0.6479 |
| 0.3695 | 4.17 | 1500 | 0.8216 | 0.6990 |
| 0.3392 | 5.56 | 2000 | 0.7637 | 0.7335 |
| 0.2638 | 6.94 | 2500 | 0.8244 | 0.7678 |
| 0.1907 | 8.33 | 3000 | 0.7912 | 0.7979 |
| 0.1661 | 9.72 | 3500 | 0.8266 | 0.7835 |
| 0.1073 | 11.11 | 4000 | 0.8120 | 0.8139 |
| 0.1265 | 12.5 | 4500 | 0.8336 | 0.8344 |
| 0.0481 | 13.89 | 5000 | 0.8240 | 0.8518 |
| 0.0646 | 15.28 | 5500 | 0.9290 | 0.8333 |
| 0.0846 | 16.67 | 6000 | 0.9176 | 0.8461 |
| 0.0228 | 18.06 | 6500 | 0.9600 | 0.8529 |
| 0.0696 | 19.44 | 7000 | 0.9769 | 0.8525 |
| 0.0614 | 20.83 | 7500 | 0.9944 | 0.8545 |
| 0.0173 | 22.22 | 8000 | 1.0110 | 0.8550 |
| 0.004 | 23.61 | 8500 | 1.0140 | 0.8417 |
| 0.0032 | 25.0 | 9000 | 1.0771 | 0.8314 |
| 0.0453 | 26.39 | 9500 | 1.0173 | 0.8424 |
| 0.0471 | 27.78 | 10000 | 1.0068 | 0.8652 |
| 0.0128 | 29.17 | 10500 | 1.0595 | 0.8658 |
| 0.0027 | 30.56 | 11000 | 1.0596 | 0.8506 |
| 0.0198 | 31.94 | 11500 | 1.0468 | 0.8593 |
| 0.0027 | 33.33 | 12000 | 1.0537 | 0.8693 |
| 0.0114 | 34.72 | 12500 | 1.0512 | 0.8620 |
| 0.015 | 36.11 | 13000 | 1.0425 | 0.8813 |
| 0.005 | 37.5 | 13500 | 1.1092 | 0.8749 |
| 0.0038 | 38.89 | 14000 | 1.0829 | 0.8637 |
| 0.0096 | 40.28 | 14500 | 1.0902 | 0.8794 |
| 0.0007 | 41.67 | 15000 | 1.0994 | 0.8651 |
| 0.0109 | 43.06 | 15500 | 1.0957 | 0.8782 |
| 0.0026 | 44.44 | 16000 | 1.0997 | 0.8643 |
| 0.0061 | 45.83 | 16500 | 1.0853 | 0.8672 |
| 0.0005 | 47.22 | 17000 | 1.1082 | 0.8694 |
| 0.0005 | 48.61 | 17500 | 1.1016 | 0.8696 |
| 0.0028 | 50.0 | 18000 | 1.0967 | 0.8702 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
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