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pig4431
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rtm_DistilBERT_5E

Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
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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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+
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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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+ # rtm_DistilBERT_5E
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+
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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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+
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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: 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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+
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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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+ | 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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+
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+
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+ ### Framework versions
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+
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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