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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+ts_cx-en_00000-00009_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 en |
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type: uonlp/CulturaX |
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args: en |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.3894698710798747 |
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license: mit |
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language: |
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- en |
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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+ts_cx-en_00000-00009_50k |
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This model is a fine-tuned version of [](https://huggingface.co/) on the uonlp/CulturaX en dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4121 |
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- Accuracy: 0.3895 |
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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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| 4.375 | 0.04 | 10000 | 4.2815 | 0.3111 | |
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| 4.0754 | 0.08 | 20000 | 3.9984 | 0.3341 | |
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| 3.9409 | 0.11 | 30000 | 3.8615 | 0.3457 | |
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| 3.8554 | 0.15 | 40000 | 3.7798 | 0.3531 | |
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| 3.7973 | 0.19 | 50000 | 3.7210 | 0.3584 | |
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| 3.7421 | 0.23 | 60000 | 3.6750 | 0.3630 | |
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| 3.7097 | 0.27 | 70000 | 3.6378 | 0.3664 | |
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| 3.6741 | 0.3 | 80000 | 3.6061 | 0.3694 | |
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| 3.6599 | 0.34 | 90000 | 3.5803 | 0.3718 | |
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| 3.6356 | 0.38 | 100000 | 3.5584 | 0.3741 | |
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| 3.6131 | 0.42 | 110000 | 3.5423 | 0.3758 | |
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| 3.5991 | 0.46 | 120000 | 3.5254 | 0.3776 | |
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| 3.591 | 0.49 | 130000 | 3.5108 | 0.3790 | |
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| 3.574 | 0.53 | 140000 | 3.4966 | 0.3805 | |
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| 3.5606 | 0.57 | 150000 | 3.4866 | 0.3815 | |
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| 3.5516 | 0.61 | 160000 | 3.4739 | 0.3828 | |
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| 3.5423 | 0.64 | 170000 | 3.4650 | 0.3838 | |
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| 3.5298 | 0.68 | 180000 | 3.4560 | 0.3847 | |
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| 3.5287 | 0.72 | 190000 | 3.4479 | 0.3857 | |
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| 3.5187 | 0.76 | 200000 | 3.4408 | 0.3863 | |
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| 3.5157 | 0.8 | 210000 | 3.4339 | 0.3870 | |
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| 3.5042 | 0.83 | 220000 | 3.4286 | 0.3876 | |
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| 3.5033 | 0.87 | 230000 | 3.4229 | 0.3883 | |
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| 3.501 | 0.91 | 240000 | 3.4188 | 0.3888 | |
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| 3.4946 | 0.95 | 250000 | 3.4149 | 0.3892 | |
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| 3.4971 | 0.99 | 260000 | 3.4126 | 0.3894 | |
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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 |
