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---
library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: phobert-human-tl-seed-6969
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. -->
# phobert-human-tl-seed-6969
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4535
- Accuracy: 0.8387
- Precision: 0.6438
- Recall: 0.4677
- F1: 0.4914
## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 346 | 0.5154 | 0.8241 | 0.5456 | 0.3492 | 0.3314 |
| 0.5778 | 2.0 | 692 | 0.4712 | 0.8305 | 0.6220 | 0.4062 | 0.4191 |
| 0.4667 | 3.0 | 1038 | 0.4650 | 0.8331 | 0.6490 | 0.4069 | 0.4227 |
| 0.4667 | 4.0 | 1384 | 0.4665 | 0.8365 | 0.6819 | 0.4147 | 0.4337 |
| 0.4617 | 5.0 | 1730 | 0.4639 | 0.8357 | 0.6591 | 0.4129 | 0.4337 |
| 0.4577 | 6.0 | 2076 | 0.4606 | 0.8368 | 0.6775 | 0.4282 | 0.4479 |
| 0.4577 | 7.0 | 2422 | 0.4626 | 0.8361 | 0.6851 | 0.4134 | 0.4359 |
| 0.4554 | 8.0 | 2768 | 0.4530 | 0.8394 | 0.6468 | 0.4436 | 0.4674 |
| 0.4545 | 9.0 | 3114 | 0.4599 | 0.8342 | 0.6459 | 0.4083 | 0.4288 |
| 0.4545 | 10.0 | 3460 | 0.4603 | 0.8350 | 0.6825 | 0.4375 | 0.4543 |
| 0.4587 | 11.0 | 3806 | 0.4594 | 0.8346 | 0.6499 | 0.4092 | 0.4321 |
| 0.4491 | 12.0 | 4152 | 0.4535 | 0.8387 | 0.6438 | 0.4677 | 0.4914 |
| 0.4491 | 13.0 | 4498 | 0.4555 | 0.8353 | 0.6372 | 0.4213 | 0.4475 |
| 0.4579 | 14.0 | 4844 | 0.4563 | 0.8357 | 0.6552 | 0.4129 | 0.4359 |
| 0.4488 | 15.0 | 5190 | 0.4595 | 0.8335 | 0.6553 | 0.4019 | 0.4217 |
| 0.4587 | 16.0 | 5536 | 0.4663 | 0.8327 | 0.6580 | 0.3987 | 0.4161 |
| 0.4587 | 17.0 | 5882 | 0.4515 | 0.8387 | 0.6235 | 0.4357 | 0.4612 |
### Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
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