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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: mdeberta-v3-base_binary_2_seed7_NL-IT |
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results: [] |
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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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# mdeberta-v3-base_binary_2_seed7_NL-IT |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5531 |
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- Accuracy: 0.7234 |
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- F1: 0.7266 |
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- Precision: 0.7324 |
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- Recall: 0.7234 |
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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-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.7125 | 0.2105 | 100 | 0.6482 | 0.6667 | 0.5333 | 0.4444 | 0.6667 | |
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| 0.6448 | 0.4211 | 200 | 0.6268 | 0.6667 | 0.5333 | 0.4444 | 0.6667 | |
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| 0.6334 | 0.6316 | 300 | 0.5938 | 0.6667 | 0.5333 | 0.4444 | 0.6667 | |
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| 0.6066 | 0.8421 | 400 | 0.5962 | 0.6667 | 0.5333 | 0.4444 | 0.6667 | |
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| 0.5887 | 1.0526 | 500 | 0.5849 | 0.7106 | 0.6807 | 0.6942 | 0.7106 | |
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| 0.5683 | 1.2632 | 600 | 0.5597 | 0.7034 | 0.6406 | 0.7043 | 0.7034 | |
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| 0.578 | 1.4737 | 700 | 0.5500 | 0.7177 | 0.7172 | 0.7167 | 0.7177 | |
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| 0.5565 | 1.6842 | 800 | 0.5487 | 0.6916 | 0.6992 | 0.7202 | 0.6916 | |
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| 0.5505 | 1.8947 | 900 | 0.5365 | 0.7117 | 0.7062 | 0.7035 | 0.7117 | |
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| 0.5137 | 2.1053 | 1000 | 0.5331 | 0.7236 | 0.7269 | 0.7322 | 0.7236 | |
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| 0.5162 | 2.3158 | 1100 | 0.5339 | 0.7307 | 0.7304 | 0.7300 | 0.7307 | |
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| 0.5022 | 2.5263 | 1200 | 0.5303 | 0.7307 | 0.7336 | 0.7379 | 0.7307 | |
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| 0.5103 | 2.7368 | 1300 | 0.5346 | 0.7426 | 0.7353 | 0.7340 | 0.7426 | |
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| 0.4983 | 2.9474 | 1400 | 0.5239 | 0.7331 | 0.7350 | 0.7374 | 0.7331 | |
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| 0.4902 | 3.1579 | 1500 | 0.5232 | 0.7367 | 0.7397 | 0.7447 | 0.7367 | |
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| 0.4496 | 3.3684 | 1600 | 0.5384 | 0.7497 | 0.7460 | 0.7443 | 0.7497 | |
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| 0.4522 | 3.5789 | 1700 | 0.5386 | 0.7497 | 0.7496 | 0.7495 | 0.7497 | |
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| 0.4597 | 3.7895 | 1800 | 0.5583 | 0.7426 | 0.7373 | 0.7354 | 0.7426 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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