Hugging Face's logo

jonasknobloch
/
gpt2-ts_cx-cs_00000-00019_50k

Text Generation
Transformers
Safetensors
Czech
gpt2
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
Model card Files Community
jonasknobloch commited on
Commit
faccc14
•
1 Parent(s): 44863b2

Initial commit

Browse files
Files changed (12) hide show
  1. README.md +97 -0
  2. all_results.json +15 -0
  3. config.json +31 -0
  4. eval_results.json +10 -0
  5. generation_config.json +6 -0
  6. model.safetensors +3 -0
  7. special_tokens_map.json +1 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +15 -0
  10. train_results.json +8 -0
  11. trainer_state.json +3567 -0
  12. training_args.bin +3 -0
README.md ADDED
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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-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: 0.39971894120768026
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+ ---
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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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+
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+ # gpt2+ts_cx-cs_00000-00019_50k
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+
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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: 3.4399
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+ - Accuracy: 0.3997
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:------:|:---------------:|:--------:|
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+ | 4.6338 | 0.04 | 10000 | 4.5133 | 0.2968 |
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+ | 4.2588 | 0.07 | 20000 | 4.1531 | 0.3284 |
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+ | 4.0955 | 0.11 | 30000 | 3.9906 | 0.3432 |
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+ | 3.9884 | 0.15 | 40000 | 3.8866 | 0.3530 |
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+ | 3.914 | 0.18 | 50000 | 3.8144 | 0.3601 |
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+ | 3.8563 | 0.22 | 60000 | 3.7592 | 0.3656 |
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+ | 3.8136 | 0.25 | 70000 | 3.7137 | 0.3701 |
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+ | 3.7762 | 0.29 | 80000 | 3.6766 | 0.3740 |
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+ | 3.7481 | 0.33 | 90000 | 3.6468 | 0.3773 |
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+ | 3.7199 | 0.36 | 100000 | 3.6194 | 0.3800 |
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+ | 3.6886 | 0.4 | 110000 | 3.5967 | 0.3824 |
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+ | 3.677 | 0.44 | 120000 | 3.5789 | 0.3843 |
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+ | 3.6611 | 0.47 | 130000 | 3.5600 | 0.3863 |
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+ | 3.6442 | 0.51 | 140000 | 3.5443 | 0.3879 |
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+ | 3.6285 | 0.55 | 150000 | 3.5313 | 0.3894 |
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+ | 3.6126 | 0.58 | 160000 | 3.5176 | 0.3910 |
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+ | 3.6051 | 0.62 | 170000 | 3.5063 | 0.3921 |
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+ | 3.5946 | 0.65 | 180000 | 3.4957 | 0.3933 |
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+ | 3.5883 | 0.69 | 190000 | 3.4858 | 0.3944 |
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+ | 3.5789 | 0.73 | 200000 | 3.4788 | 0.3951 |
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+ | 3.5693 | 0.76 | 210000 | 3.4702 | 0.3963 |
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+ | 3.5584 | 0.8 | 220000 | 3.4632 | 0.3970 |
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+ | 3.5546 | 0.84 | 230000 | 3.4574 | 0.3977 |
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+ | 3.5434 | 0.87 | 240000 | 3.4520 | 0.3983 |
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+ | 3.5447 | 0.91 | 250000 | 3.4473 | 0.3988 |
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+ | 3.5353 | 0.95 | 260000 | 3.4427 | 0.3993 |
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+ | 3.5382 | 0.98 | 270000 | 3.4402 | 0.3997 |
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+
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+
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+ ### Framework versions
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+
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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
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