CausalNLP/gpt2-multilingual-20-zh-repair_3epochs_lr1e-4
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Model: CausalNLP/gpt2-multilingual-20-zh-repair_3epochs_lr1e-4
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---
library_name: transformers
base_model: CausalNLP/gpt2-hf_multilingual-20
tags:
- generated_from_trainer
model-index:
- name: gpt2-multilingual-20-zh-repair_3epochs_lr1e-4
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. -->
# gpt2-multilingual-20-zh-repair_3epochs_lr1e-4
This model is a fine-tuned version of [CausalNLP/gpt2-hf_multilingual-20](https://huggingface.co/CausalNLP/gpt2-hf_multilingual-20) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5282
## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 3.6036 | 0.0626 | 500 | 3.6583 |
| 3.6222 | 0.1252 | 1000 | 3.6372 |
| 3.5958 | 0.1879 | 1500 | 3.6347 |
| 3.6297 | 0.2505 | 2000 | 3.6452 |
| 3.6016 | 0.3131 | 2500 | 3.6591 |
| 3.6284 | 0.3757 | 3000 | 3.6530 |
| 3.5823 | 0.4384 | 3500 | 3.6493 |
| 3.646 | 0.5010 | 4000 | 3.6412 |
| 3.6329 | 0.5636 | 4500 | 3.6358 |
| 3.5469 | 0.6262 | 5000 | 3.6302 |
| 3.5987 | 0.6889 | 5500 | 3.6242 |
| 3.4773 | 0.7515 | 6000 | 3.6181 |
| 3.561 | 0.8141 | 6500 | 3.6142 |
| 3.5243 | 0.8767 | 7000 | 3.6092 |
| 3.5998 | 0.9393 | 7500 | 3.6058 |
| 3.6267 | 1.0019 | 8000 | 3.6007 |
| 3.5314 | 1.0645 | 8500 | 3.5958 |
| 3.5579 | 1.1271 | 9000 | 3.5915 |
| 3.541 | 1.1897 | 9500 | 3.5861 |
| 3.5823 | 1.2524 | 10000 | 3.5820 |
| 3.534 | 1.3150 | 10500 | 3.5767 |
| 3.4906 | 1.3776 | 11000 | 3.5718 |
| 3.5538 | 1.4402 | 11500 | 3.5673 |
| 3.527 | 1.5029 | 12000 | 3.5631 |
| 3.5119 | 1.5655 | 12500 | 3.5584 |
| 3.4633 | 1.6281 | 13000 | 3.5537 |
| 3.5098 | 1.6907 | 13500 | 3.5503 |
| 3.4336 | 1.7534 | 14000 | 3.5474 |
| 3.5241 | 1.8160 | 14500 | 3.5444 |
| 3.4846 | 1.8786 | 15000 | 3.5409 |
| 3.4802 | 1.9412 | 15500 | 3.5385 |
| 3.5026 | 2.0038 | 16000 | 3.5368 |
| 3.494 | 2.0664 | 16500 | 3.5351 |
| 3.4524 | 2.1290 | 17000 | 3.5341 |
| 3.4478 | 2.1916 | 17500 | 3.5329 |
| 3.4458 | 2.2543 | 18000 | 3.5317 |
| 3.4925 | 2.3169 | 18500 | 3.5305 |
| 3.4913 | 2.3795 | 19000 | 3.5298 |
| 3.4331 | 2.4421 | 19500 | 3.5293 |
| 3.5182 | 2.5047 | 20000 | 3.5290 |
| 3.446 | 2.5674 | 20500 | 3.5286 |
| 3.5018 | 2.6300 | 21000 | 3.5285 |
| 3.475 | 2.6926 | 21500 | 3.5283 |
| 3.4299 | 2.7552 | 22000 | 3.5282 |
| 3.4538 | 2.8179 | 22500 | 3.5282 |
| 3.4471 | 2.8805 | 23000 | 3.5282 |
| 3.4295 | 2.9431 | 23500 | 3.5282 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0
- Datasets 4.4.1
- Tokenizers 0.22.1

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