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distilbert
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: distilbert-base-uncased
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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: results
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+ results: []
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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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+ # results
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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: 0.3359
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+ - Accuracy: 0.8828
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+ - F1: 0.4499
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+ - Precision: 0.4845
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+ - Recall: 0.4348
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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-06
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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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: 100
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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+ - label_smoothing_factor: 0.1
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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 | F1 | Precision | Recall |
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+ |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.3514 | 0.1149 | 50 | 0.3432 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3392 | 0.2299 | 100 | 0.3374 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3378 | 0.3448 | 150 | 0.3371 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3375 | 0.4598 | 200 | 0.3370 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3369 | 0.5747 | 250 | 0.3368 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3366 | 0.6897 | 300 | 0.3368 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3366 | 0.8046 | 350 | 0.3367 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.336 | 0.9195 | 400 | 0.3367 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3375 | 1.0345 | 450 | 0.3366 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3363 | 1.1494 | 500 | 0.3365 | 0.8724 | 0.3106 | 0.2908 | 0.3333 |
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+ | 0.3362 | 1.2644 | 550 | 0.3365 | 0.8736 | 0.3335 | 0.4917 | 0.3445 |
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+ | 0.3358 | 1.3793 | 600 | 0.3365 | 0.8724 | 0.3184 | 0.4578 | 0.3369 |
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+ | 0.337 | 1.4943 | 650 | 0.3363 | 0.8747 | 0.3409 | 0.5143 | 0.3485 |
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+ | 0.3363 | 1.6092 | 700 | 0.3363 | 0.8690 | 0.3771 | 0.4353 | 0.3714 |
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+ | 0.3367 | 1.7241 | 750 | 0.3362 | 0.8736 | 0.3532 | 0.4744 | 0.3552 |
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+ | 0.3367 | 1.8391 | 800 | 0.3362 | 0.8736 | 0.3532 | 0.4744 | 0.3552 |
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+ | 0.3363 | 1.9540 | 850 | 0.3360 | 0.8690 | 0.3957 | 0.4466 | 0.3857 |
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+ | 0.3363 | 2.0690 | 900 | 0.3359 | 0.8713 | 0.3933 | 0.4558 | 0.3830 |
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+ | 0.3363 | 2.1839 | 950 | 0.3359 | 0.8713 | 0.3977 | 0.4565 | 0.3865 |
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+ | 0.3356 | 2.2989 | 1000 | 0.3358 | 0.8655 | 0.4048 | 0.4388 | 0.3951 |
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+ | 0.3359 | 2.4138 | 1050 | 0.3358 | 0.8701 | 0.3628 | 0.4412 | 0.3611 |
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+ | 0.3362 | 2.5287 | 1100 | 0.3358 | 0.8678 | 0.4243 | 0.4509 | 0.4138 |
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+ | 0.3359 | 2.6437 | 1150 | 0.3358 | 0.8644 | 0.4274 | 0.4443 | 0.4197 |
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+ | 0.336 | 2.7586 | 1200 | 0.3357 | 0.8655 | 0.4048 | 0.4388 | 0.3951 |
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+ | 0.3352 | 2.8736 | 1250 | 0.3357 | 0.8667 | 0.4095 | 0.4435 | 0.3991 |
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+ | 0.3356 | 2.9885 | 1300 | 0.3357 | 0.8667 | 0.4095 | 0.4435 | 0.3991 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 2.17.0
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+ - Tokenizers 0.19.1
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