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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_ALBERT_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: 0.8533333333333334 |
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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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# Sentiment140_ALBERT_5E |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6103 |
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- Accuracy: 0.8533 |
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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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| 0.6713 | 0.08 | 50 | 0.5704 | 0.7333 | |
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| 0.5742 | 0.16 | 100 | 0.4620 | 0.8 | |
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| 0.5104 | 0.24 | 150 | 0.5536 | 0.74 | |
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| 0.5313 | 0.32 | 200 | 0.5198 | 0.76 | |
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| 0.5023 | 0.4 | 250 | 0.4286 | 0.8 | |
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| 0.4871 | 0.48 | 300 | 0.4294 | 0.8267 | |
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| 0.4513 | 0.56 | 350 | 0.4349 | 0.8133 | |
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| 0.4647 | 0.64 | 400 | 0.4046 | 0.8333 | |
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| 0.4827 | 0.72 | 450 | 0.4218 | 0.8333 | |
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| 0.4517 | 0.8 | 500 | 0.4093 | 0.82 | |
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| 0.4417 | 0.88 | 550 | 0.3999 | 0.84 | |
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| 0.4701 | 0.96 | 600 | 0.3779 | 0.8867 | |
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| 0.397 | 1.04 | 650 | 0.3730 | 0.8667 | |
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| 0.3377 | 1.12 | 700 | 0.3833 | 0.8333 | |
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| 0.411 | 1.2 | 750 | 0.3704 | 0.84 | |
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| 0.3796 | 1.28 | 800 | 0.3472 | 0.86 | |
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| 0.3523 | 1.36 | 850 | 0.3512 | 0.8733 | |
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| 0.3992 | 1.44 | 900 | 0.3712 | 0.84 | |
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| 0.3641 | 1.52 | 950 | 0.3718 | 0.82 | |
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| 0.3973 | 1.6 | 1000 | 0.3508 | 0.84 | |
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| 0.3576 | 1.68 | 1050 | 0.3600 | 0.86 | |
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| 0.3701 | 1.76 | 1100 | 0.3287 | 0.8667 | |
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| 0.3721 | 1.84 | 1150 | 0.3794 | 0.82 | |
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| 0.3673 | 1.92 | 1200 | 0.3378 | 0.8733 | |
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| 0.4223 | 2.0 | 1250 | 0.3508 | 0.86 | |
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| 0.2745 | 2.08 | 1300 | 0.3835 | 0.86 | |
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| 0.283 | 2.16 | 1350 | 0.3500 | 0.8533 | |
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| 0.2769 | 2.24 | 1400 | 0.3334 | 0.8733 | |
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| 0.2491 | 2.32 | 1450 | 0.3519 | 0.8867 | |
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| 0.3237 | 2.4 | 1500 | 0.3438 | 0.86 | |
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| 0.2662 | 2.48 | 1550 | 0.3513 | 0.8667 | |
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| 0.2423 | 2.56 | 1600 | 0.3413 | 0.8867 | |
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| 0.2655 | 2.64 | 1650 | 0.3126 | 0.8933 | |
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| 0.2516 | 2.72 | 1700 | 0.3333 | 0.8733 | |
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| 0.252 | 2.8 | 1750 | 0.3316 | 0.88 | |
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| 0.2872 | 2.88 | 1800 | 0.3227 | 0.9 | |
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| 0.306 | 2.96 | 1850 | 0.3383 | 0.8733 | |
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| 0.248 | 3.04 | 1900 | 0.3474 | 0.8733 | |
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| 0.1507 | 3.12 | 1950 | 0.4140 | 0.8667 | |
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| 0.1994 | 3.2 | 2000 | 0.3729 | 0.8533 | |
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| 0.167 | 3.28 | 2050 | 0.3782 | 0.8867 | |
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| 0.1872 | 3.36 | 2100 | 0.4352 | 0.8867 | |
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| 0.1611 | 3.44 | 2150 | 0.4511 | 0.8667 | |
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| 0.2338 | 3.52 | 2200 | 0.4244 | 0.8533 | |
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| 0.1538 | 3.6 | 2250 | 0.4226 | 0.8733 | |
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| 0.1561 | 3.68 | 2300 | 0.4126 | 0.88 | |
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| 0.2156 | 3.76 | 2350 | 0.4382 | 0.86 | |
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| 0.1684 | 3.84 | 2400 | 0.4969 | 0.86 | |
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| 0.1917 | 3.92 | 2450 | 0.4439 | 0.8667 | |
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| 0.1584 | 4.0 | 2500 | 0.4759 | 0.86 | |
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| 0.1038 | 4.08 | 2550 | 0.5042 | 0.8667 | |
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| 0.0983 | 4.16 | 2600 | 0.5527 | 0.8533 | |
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| 0.1404 | 4.24 | 2650 | 0.5801 | 0.84 | |
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| 0.0844 | 4.32 | 2700 | 0.5884 | 0.86 | |
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| 0.1347 | 4.4 | 2750 | 0.5865 | 0.8467 | |
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| 0.1373 | 4.48 | 2800 | 0.5915 | 0.8533 | |
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| 0.1506 | 4.56 | 2850 | 0.5976 | 0.8467 | |
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| 0.1007 | 4.64 | 2900 | 0.6678 | 0.82 | |
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| 0.1311 | 4.72 | 2950 | 0.6082 | 0.8533 | |
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| 0.1402 | 4.8 | 3000 | 0.6180 | 0.8467 | |
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| 0.1363 | 4.88 | 3050 | 0.6107 | 0.8533 | |
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| 0.0995 | 4.96 | 3100 | 0.6103 | 0.8533 | |
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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 2.3.2 |
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- Tokenizers 0.13.1 |
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