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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- rotten_tomatoes |
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metrics: |
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- accuracy |
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model-index: |
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- name: rtm_DistilBERT_5E |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: rotten_tomatoes |
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type: rotten_tomatoes |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.82 |
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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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# rtm_DistilBERT_5E |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the rotten_tomatoes dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6835 |
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- Accuracy: 0.82 |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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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: 5 |
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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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| 0.6822 | 0.09 | 50 | 0.6391 | 0.76 | |
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| 0.5531 | 0.19 | 100 | 0.4684 | 0.7667 | |
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| 0.4546 | 0.28 | 150 | 0.4479 | 0.7733 | |
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| 0.4495 | 0.37 | 200 | 0.3953 | 0.8067 | |
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| 0.4239 | 0.47 | 250 | 0.4211 | 0.7933 | |
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| 0.3951 | 0.56 | 300 | 0.4126 | 0.7933 | |
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| 0.3861 | 0.66 | 350 | 0.3950 | 0.7933 | |
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| 0.4108 | 0.75 | 400 | 0.4091 | 0.82 | |
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| 0.3778 | 0.84 | 450 | 0.4107 | 0.7933 | |
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| 0.3627 | 0.94 | 500 | 0.4203 | 0.7933 | |
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| 0.3648 | 1.03 | 550 | 0.4190 | 0.8 | |
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| 0.2899 | 1.12 | 600 | 0.4436 | 0.8 | |
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| 0.2637 | 1.22 | 650 | 0.4504 | 0.82 | |
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| 0.2885 | 1.31 | 700 | 0.4406 | 0.82 | |
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| 0.3226 | 1.4 | 750 | 0.4398 | 0.8333 | |
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| 0.3147 | 1.5 | 800 | 0.4239 | 0.82 | |
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| 0.2937 | 1.59 | 850 | 0.4227 | 0.8133 | |
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| 0.3149 | 1.69 | 900 | 0.3791 | 0.82 | |
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| 0.3227 | 1.78 | 950 | 0.3888 | 0.8133 | |
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| 0.2727 | 1.87 | 1000 | 0.4215 | 0.82 | |
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| 0.2722 | 1.97 | 1050 | 0.4099 | 0.8333 | |
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| 0.1908 | 2.06 | 1100 | 0.4595 | 0.82 | |
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| 0.2276 | 2.15 | 1150 | 0.4572 | 0.84 | |
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| 0.2239 | 2.25 | 1200 | 0.4545 | 0.8333 | |
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| 0.1986 | 2.34 | 1250 | 0.4895 | 0.82 | |
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| 0.2388 | 2.43 | 1300 | 0.4352 | 0.86 | |
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| 0.1901 | 2.53 | 1350 | 0.4806 | 0.84 | |
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| 0.2227 | 2.62 | 1400 | 0.5473 | 0.8067 | |
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| 0.2221 | 2.72 | 1450 | 0.5010 | 0.84 | |
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| 0.1955 | 2.81 | 1500 | 0.5315 | 0.8267 | |
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| 0.2114 | 2.9 | 1550 | 0.5410 | 0.8133 | |
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| 0.1827 | 3.0 | 1600 | 0.5721 | 0.8133 | |
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| 0.1527 | 3.09 | 1650 | 0.5616 | 0.8133 | |
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| 0.1464 | 3.18 | 1700 | 0.5935 | 0.8067 | |
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| 0.135 | 3.28 | 1750 | 0.6145 | 0.82 | |
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| 0.1668 | 3.37 | 1800 | 0.6901 | 0.8067 | |
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| 0.1702 | 3.46 | 1850 | 0.6067 | 0.8133 | |
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| 0.1738 | 3.56 | 1900 | 0.5981 | 0.82 | |
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| 0.1506 | 3.65 | 1950 | 0.6073 | 0.8267 | |
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| 0.1584 | 3.75 | 2000 | 0.6549 | 0.8133 | |
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| 0.1698 | 3.84 | 2050 | 0.6660 | 0.8267 | |
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| 0.1626 | 3.93 | 2100 | 0.6645 | 0.8267 | |
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| 0.1483 | 4.03 | 2150 | 0.6497 | 0.82 | |
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| 0.1342 | 4.12 | 2200 | 0.6643 | 0.82 | |
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| 0.1064 | 4.21 | 2250 | 0.6775 | 0.82 | |
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| 0.1302 | 4.31 | 2300 | 0.6876 | 0.82 | |
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| 0.1847 | 4.4 | 2350 | 0.6821 | 0.8133 | |
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| 0.1055 | 4.49 | 2400 | 0.6928 | 0.8133 | |
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| 0.1372 | 4.59 | 2450 | 0.6877 | 0.8133 | |
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| 0.131 | 4.68 | 2500 | 0.6769 | 0.8267 | |
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| 0.1242 | 4.78 | 2550 | 0.6769 | 0.8267 | |
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| 0.1289 | 4.87 | 2600 | 0.6810 | 0.82 | |
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| 0.1488 | 4.96 | 2650 | 0.6835 | 0.82 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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