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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: nlp_til |
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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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# nlp_til |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1994 |
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- Precision: 0.4726 |
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- Recall: 0.5278 |
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- F1: 0.4987 |
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- Accuracy: 0.9007 |
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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: 2e-05 |
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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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- num_epochs: 18 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 219 | 0.2462 | 0.3017 | 0.3623 | 0.3292 | 0.8584 | |
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| No log | 2.0 | 438 | 0.2436 | 0.3176 | 0.3485 | 0.3323 | 0.8656 | |
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| 0.2463 | 3.0 | 657 | 0.2434 | 0.3333 | 0.4792 | 0.3932 | 0.8622 | |
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| 0.2463 | 4.0 | 876 | 0.2402 | 0.3398 | 0.3567 | 0.3480 | 0.8675 | |
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| 0.2453 | 5.0 | 1095 | 0.2388 | 0.3299 | 0.3708 | 0.3491 | 0.8686 | |
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| 0.2453 | 6.0 | 1314 | 0.2381 | 0.3230 | 0.3740 | 0.3467 | 0.8689 | |
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| 0.2421 | 7.0 | 1533 | 0.2384 | 0.3448 | 0.3508 | 0.3477 | 0.8691 | |
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| 0.2421 | 8.0 | 1752 | 0.2343 | 0.3427 | 0.3711 | 0.3563 | 0.8705 | |
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| 0.2421 | 9.0 | 1971 | 0.2334 | 0.3448 | 0.3433 | 0.3440 | 0.8713 | |
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| 0.2388 | 10.0 | 2190 | 0.2314 | 0.3696 | 0.4533 | 0.4072 | 0.8768 | |
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| 0.2388 | 11.0 | 2409 | 0.2238 | 0.3846 | 0.4643 | 0.4207 | 0.8812 | |
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| 0.2337 | 12.0 | 2628 | 0.2216 | 0.3968 | 0.4703 | 0.4305 | 0.8832 | |
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| 0.2337 | 13.0 | 2847 | 0.2135 | 0.4169 | 0.4939 | 0.4521 | 0.8898 | |
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| 0.2268 | 14.0 | 3066 | 0.2117 | 0.4387 | 0.5200 | 0.4759 | 0.8919 | |
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| 0.2268 | 15.0 | 3285 | 0.2059 | 0.4565 | 0.5146 | 0.4838 | 0.8963 | |
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| 0.2197 | 16.0 | 3504 | 0.2043 | 0.4669 | 0.5359 | 0.4990 | 0.8977 | |
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| 0.2197 | 17.0 | 3723 | 0.2005 | 0.4701 | 0.5356 | 0.5007 | 0.8997 | |
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| 0.2197 | 18.0 | 3942 | 0.1994 | 0.4726 | 0.5278 | 0.4987 | 0.9007 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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