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---
license: mit
base_model: sheepy928/default
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: FT_3
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. -->
# FT_3
This model is a fine-tuned version of [sheepy928/default](https://huggingface.co/sheepy928/default) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7570
- Accuracy: 0.7393
- F1: 0.6292
- Recall: 0.7393
- Precision: 0.7147
- Combined Score: 0.7056
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:--------------:|
| 0.6785 | 1.6 | 300 | 0.7930 | 0.7387 | 0.6276 | 0.7387 | 0.5456 | 0.6627 |
| 0.5583 | 3.19 | 600 | 0.6910 | 0.7613 | 0.7316 | 0.7613 | 0.7072 | 0.7404 |
| 0.7857 | 4.79 | 900 | 0.6515 | 0.7387 | 0.6276 | 0.7387 | 0.5456 | 0.6627 |
| 0.6309 | 6.38 | 1200 | 0.5592 | 0.848 | 0.8270 | 0.848 | 0.8382 | 0.8403 |
| 0.2216 | 7.98 | 1500 | 0.5708 | 0.8773 | 0.8432 | 0.8773 | 0.8496 | 0.8619 |
| 0.3214 | 9.57 | 1800 | 0.4550 | 0.896 | 0.8584 | 0.896 | 0.8776 | 0.8820 |
| 0.7521 | 11.17 | 2100 | 0.3819 | 0.884 | 0.8423 | 0.884 | 0.8059 | 0.8541 |
| 0.5048 | 12.77 | 2400 | 0.6582 | 0.7387 | 0.6276 | 0.7387 | 0.5456 | 0.6627 |
| 0.6435 | 14.36 | 2700 | 0.5365 | 0.8467 | 0.8092 | 0.8467 | 0.7798 | 0.8206 |
| 0.9304 | 15.96 | 3000 | 0.7577 | 0.7387 | 0.6289 | 0.7387 | 0.6302 | 0.6841 |
| 0.7902 | 17.55 | 3300 | 0.7684 | 0.7387 | 0.6289 | 0.7387 | 0.6302 | 0.6841 |
| 0.6364 | 19.15 | 3600 | 0.7638 | 0.7387 | 0.6289 | 0.7387 | 0.6302 | 0.6841 |
| 0.6738 | 20.74 | 3900 | 0.7769 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
| 0.8142 | 22.34 | 4200 | 0.7443 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
| 0.8184 | 23.94 | 4500 | 0.7635 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
| 0.7562 | 25.53 | 4800 | 0.7467 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
| 0.5699 | 27.13 | 5100 | 0.7867 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
| 0.761 | 28.72 | 5400 | 0.7570 | 0.7393 | 0.6292 | 0.7393 | 0.7147 | 0.7056 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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