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Sentiment140_DistilBERT_5E

Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results
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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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+ - sentiment140
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: Sentiment140_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: sentiment140
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+ type: sentiment140
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+ config: sentiment140
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+ split: train
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+ args: sentiment140
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8333333333333334
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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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+ # Sentiment140_DistilBERT_5E
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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 the sentiment140 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4897
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+ - Accuracy: 0.8333
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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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+ | 0.6784 | 0.08 | 50 | 0.6516 | 0.6933 |
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+ | 0.6301 | 0.16 | 100 | 0.5384 | 0.7533 |
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+ | 0.5438 | 0.24 | 150 | 0.4559 | 0.8 |
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+ | 0.4625 | 0.32 | 200 | 0.4287 | 0.8133 |
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+ | 0.4528 | 0.4 | 250 | 0.4056 | 0.8267 |
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+ | 0.4609 | 0.48 | 300 | 0.3883 | 0.8333 |
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+ | 0.4705 | 0.56 | 350 | 0.3886 | 0.8067 |
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+ | 0.4539 | 0.64 | 400 | 0.3967 | 0.82 |
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+ | 0.4483 | 0.72 | 450 | 0.3758 | 0.82 |
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+ | 0.4699 | 0.8 | 500 | 0.4003 | 0.8133 |
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+ | 0.467 | 0.88 | 550 | 0.4021 | 0.8267 |
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+ | 0.454 | 0.96 | 600 | 0.3735 | 0.8333 |
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+ | 0.4227 | 1.04 | 650 | 0.3840 | 0.8267 |
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+ | 0.3584 | 1.12 | 700 | 0.3775 | 0.8333 |
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+ | 0.3618 | 1.2 | 750 | 0.4026 | 0.8267 |
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+ | 0.3634 | 1.28 | 800 | 0.3891 | 0.8133 |
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+ | 0.3751 | 1.36 | 850 | 0.3895 | 0.8267 |
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+ | 0.3484 | 1.44 | 900 | 0.3919 | 0.8267 |
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+ | 0.3764 | 1.52 | 950 | 0.3770 | 0.84 |
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+ | 0.3488 | 1.6 | 1000 | 0.4028 | 0.82 |
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+ | 0.3665 | 1.68 | 1050 | 0.3779 | 0.8333 |
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+ | 0.3925 | 1.76 | 1100 | 0.3726 | 0.84 |
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+ | 0.3624 | 1.84 | 1150 | 0.3655 | 0.84 |
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+ | 0.3876 | 1.92 | 1200 | 0.3648 | 0.8133 |
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+ | 0.3935 | 2.0 | 1250 | 0.3633 | 0.8467 |
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+ | 0.2944 | 2.08 | 1300 | 0.3808 | 0.8333 |
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+ | 0.2957 | 2.16 | 1350 | 0.3836 | 0.8333 |
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+ | 0.266 | 2.24 | 1400 | 0.3940 | 0.8267 |
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+ | 0.2747 | 2.32 | 1450 | 0.3952 | 0.84 |
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+ | 0.314 | 2.4 | 1500 | 0.4060 | 0.8133 |
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+ | 0.3419 | 2.48 | 1550 | 0.4025 | 0.8133 |
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+ | 0.2782 | 2.56 | 1600 | 0.4218 | 0.82 |
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+ | 0.3218 | 2.64 | 1650 | 0.4039 | 0.8333 |
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+ | 0.2863 | 2.72 | 1700 | 0.4130 | 0.8267 |
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+ | 0.3336 | 2.8 | 1750 | 0.4026 | 0.8133 |
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+ | 0.3224 | 2.88 | 1800 | 0.3910 | 0.8267 |
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+ | 0.2709 | 2.96 | 1850 | 0.3979 | 0.84 |
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+ | 0.2701 | 3.04 | 1900 | 0.4127 | 0.8333 |
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+ | 0.2782 | 3.12 | 1950 | 0.4335 | 0.82 |
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+ | 0.2425 | 3.2 | 2000 | 0.4229 | 0.8333 |
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+ | 0.2457 | 3.28 | 2050 | 0.4168 | 0.8333 |
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+ | 0.217 | 3.36 | 2100 | 0.4264 | 0.8267 |
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+ | 0.2522 | 3.44 | 2150 | 0.4250 | 0.8333 |
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+ | 0.2402 | 3.52 | 2200 | 0.4371 | 0.8333 |
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+ | 0.2465 | 3.6 | 2250 | 0.4429 | 0.8333 |
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+ | 0.2427 | 3.68 | 2300 | 0.4435 | 0.8333 |
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+ | 0.2408 | 3.76 | 2350 | 0.4500 | 0.84 |
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+ | 0.1976 | 3.84 | 2400 | 0.4536 | 0.8333 |
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+ | 0.23 | 3.92 | 2450 | 0.4645 | 0.8333 |
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+ | 0.2449 | 4.0 | 2500 | 0.4557 | 0.8467 |
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+ | 0.1933 | 4.08 | 2550 | 0.4672 | 0.84 |
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+ | 0.213 | 4.16 | 2600 | 0.4717 | 0.84 |
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+ | 0.1772 | 4.24 | 2650 | 0.4843 | 0.8267 |
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+ | 0.1917 | 4.32 | 2700 | 0.4690 | 0.8467 |
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+ | 0.2094 | 4.4 | 2750 | 0.4728 | 0.8467 |
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+ | 0.1903 | 4.48 | 2800 | 0.4755 | 0.8467 |
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+ | 0.2541 | 4.56 | 2850 | 0.4791 | 0.84 |
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+ | 0.1805 | 4.64 | 2900 | 0.4877 | 0.84 |
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+ | 0.2183 | 4.72 | 2950 | 0.4940 | 0.8267 |
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+ | 0.2257 | 4.8 | 3000 | 0.4905 | 0.8333 |
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+ | 0.2496 | 4.88 | 3050 | 0.4883 | 0.84 |
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+ | 0.1846 | 4.96 | 3100 | 0.4897 | 0.8333 |
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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 1.12.1+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.13.1