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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- uonlp/CulturaX |
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metrics: |
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- accuracy |
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model-index: |
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- name: gpt2_cx-cs_00000-00019_50k |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: uonlp/CulturaX cs |
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type: uonlp/CulturaX |
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args: cs |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.38830943632679016 |
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license: mit |
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language: |
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- cs |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# gpt2_cx-cs_00000-00019_50k |
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This model is a fine-tuned version of [](https://huggingface.co/) on the uonlp/CulturaX cs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5060 |
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- Accuracy: 0.3883 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:| |
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| 4.729 | 0.04 | 10000 | 4.6077 | 0.2836 | |
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| 4.3383 | 0.07 | 20000 | 4.2318 | 0.3162 | |
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| 4.1706 | 0.11 | 30000 | 4.0651 | 0.3316 | |
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| 4.0594 | 0.15 | 40000 | 3.9599 | 0.3416 | |
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| 3.9842 | 0.19 | 50000 | 3.8825 | 0.3487 | |
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| 3.9298 | 0.22 | 60000 | 3.8244 | 0.3545 | |
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| 3.8777 | 0.26 | 70000 | 3.7791 | 0.3592 | |
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| 3.8455 | 0.3 | 80000 | 3.7436 | 0.3629 | |
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| 3.8104 | 0.33 | 90000 | 3.7120 | 0.3660 | |
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| 3.7908 | 0.37 | 100000 | 3.6862 | 0.3687 | |
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| 3.7613 | 0.41 | 110000 | 3.6628 | 0.3712 | |
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| 3.7492 | 0.45 | 120000 | 3.6434 | 0.3731 | |
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| 3.7228 | 0.48 | 130000 | 3.6246 | 0.3751 | |
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| 3.7127 | 0.52 | 140000 | 3.6090 | 0.3767 | |
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| 3.694 | 0.56 | 150000 | 3.5962 | 0.3783 | |
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| 3.6871 | 0.59 | 160000 | 3.5831 | 0.3797 | |
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| 3.6784 | 0.63 | 170000 | 3.5708 | 0.3810 | |
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| 3.6606 | 0.67 | 180000 | 3.5593 | 0.3823 | |
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| 3.646 | 0.71 | 190000 | 3.5491 | 0.3835 | |
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| 3.6453 | 0.74 | 200000 | 3.5410 | 0.3843 | |
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| 3.6393 | 0.78 | 210000 | 3.5342 | 0.3851 | |
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| 3.6207 | 0.82 | 220000 | 3.5280 | 0.3857 | |
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| 3.6288 | 0.86 | 230000 | 3.5218 | 0.3865 | |
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| 3.6176 | 0.89 | 240000 | 3.5151 | 0.3872 | |
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| 3.6099 | 0.93 | 250000 | 3.5108 | 0.3878 | |
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| 3.6093 | 0.97 | 260000 | 3.5079 | 0.3881 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
