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--- |
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license: mit |
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base_model: roberta-base |
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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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model-index: |
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- name: roberta-base-emotion-prediction-phr |
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results: [] |
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datasets: |
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- vibhorag101/sem_eval_2018_task_1_english_cleaned_labels |
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- sem_eval_2018_task_1 |
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language: |
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- en |
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pipeline_tag: text-classification |
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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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# roberta-base-emotion-prediction-phr |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3301 |
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- Accuracy: 0.2814 |
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- Micro Precision: 0.7422 |
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- Micro Recall: 0.6510 |
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- Micro F1: 0.6945 |
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- Micro Roc Auc: 0.7940 |
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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: 2e-05 |
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- train_batch_size: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Micro Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:-------------:| |
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| 0.4952 | 0.12 | 100 | 0.4515 | 0.1574 | 0.5861 | 0.3505 | 0.4386 | 0.6404 | |
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| 0.4152 | 0.23 | 200 | 0.3839 | 0.2041 | 0.7102 | 0.4593 | 0.5578 | 0.7033 | |
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| 0.3878 | 0.35 | 300 | 0.3625 | 0.2341 | 0.7384 | 0.5198 | 0.6101 | 0.7340 | |
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| 0.3764 | 0.47 | 400 | 0.3506 | 0.2412 | 0.7666 | 0.5092 | 0.6119 | 0.7328 | |
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| 0.372 | 0.58 | 500 | 0.3450 | 0.2375 | 0.7686 | 0.5251 | 0.6239 | 0.7403 | |
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| 0.3588 | 0.7 | 600 | 0.3464 | 0.2249 | 0.7804 | 0.4964 | 0.6068 | 0.7286 | |
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| 0.3383 | 0.82 | 700 | 0.3471 | 0.2470 | 0.7503 | 0.5578 | 0.6398 | 0.7528 | |
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| 0.3489 | 0.94 | 800 | 0.3284 | 0.2620 | 0.7702 | 0.5682 | 0.6539 | 0.7603 | |
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| 0.3287 | 1.05 | 900 | 0.3214 | 0.2820 | 0.7707 | 0.5936 | 0.6706 | 0.7720 | |
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| 0.3158 | 1.17 | 1000 | 0.3352 | 0.2657 | 0.7580 | 0.5814 | 0.6580 | 0.7646 | |
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| 0.3247 | 1.29 | 1100 | 0.3219 | 0.2811 | 0.7696 | 0.6031 | 0.6763 | 0.7762 | |
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| 0.3159 | 1.4 | 1200 | 0.3237 | 0.2688 | 0.7479 | 0.6138 | 0.6743 | 0.7778 | |
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| 0.3207 | 1.52 | 1300 | 0.3217 | 0.2461 | 0.7676 | 0.5767 | 0.6586 | 0.7638 | |
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| 0.3087 | 1.64 | 1400 | 0.3253 | 0.2424 | 0.7484 | 0.5883 | 0.6587 | 0.7663 | |
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| 0.3057 | 1.75 | 1500 | 0.3174 | 0.2728 | 0.7587 | 0.6116 | 0.6773 | 0.7785 | |
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| 0.3099 | 1.87 | 1600 | 0.3150 | 0.2774 | 0.7683 | 0.6001 | 0.6738 | 0.7746 | |
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| 0.3006 | 1.99 | 1700 | 0.3176 | 0.2633 | 0.7636 | 0.5881 | 0.6645 | 0.7685 | |
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| 0.285 | 2.11 | 1800 | 0.3177 | 0.2722 | 0.7363 | 0.6484 | 0.6896 | 0.7915 | |
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| 0.2886 | 2.22 | 1900 | 0.3156 | 0.2768 | 0.7734 | 0.5935 | 0.6716 | 0.7723 | |
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| 0.2785 | 2.34 | 2000 | 0.3101 | 0.2808 | 0.7692 | 0.6151 | 0.6836 | 0.7816 | |
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| 0.2801 | 2.46 | 2100 | 0.3121 | 0.2728 | 0.7739 | 0.5956 | 0.6732 | 0.7734 | |
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| 0.2876 | 2.57 | 2200 | 0.3166 | 0.2777 | 0.7577 | 0.6157 | 0.6794 | 0.7802 | |
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| 0.2769 | 2.69 | 2300 | 0.3143 | 0.2881 | 0.7691 | 0.6124 | 0.6819 | 0.7803 | |
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| 0.2755 | 2.81 | 2400 | 0.3133 | 0.2792 | 0.7577 | 0.6263 | 0.6857 | 0.7850 | |
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| 0.2815 | 2.92 | 2500 | 0.3197 | 0.2716 | 0.7406 | 0.6466 | 0.6904 | 0.7914 | |
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| 0.2671 | 3.04 | 2600 | 0.3133 | 0.2857 | 0.7549 | 0.6438 | 0.6949 | 0.7925 | |
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| 0.2431 | 3.16 | 2700 | 0.3225 | 0.2722 | 0.7515 | 0.6320 | 0.6866 | 0.7866 | |
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| 0.2512 | 3.27 | 2800 | 0.3221 | 0.2743 | 0.7616 | 0.6106 | 0.6778 | 0.7784 | |
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| 0.2574 | 3.39 | 2900 | 0.3191 | 0.2737 | 0.7561 | 0.6214 | 0.6822 | 0.7825 | |
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| 0.2527 | 3.51 | 3000 | 0.3207 | 0.2666 | 0.7443 | 0.6315 | 0.6833 | 0.7852 | |
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| 0.2615 | 3.63 | 3100 | 0.3170 | 0.2670 | 0.7443 | 0.6471 | 0.6923 | 0.7923 | |
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| 0.2583 | 3.74 | 3200 | 0.3122 | 0.2685 | 0.7729 | 0.6068 | 0.6799 | 0.7783 | |
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| 0.2543 | 3.86 | 3300 | 0.3175 | 0.2709 | 0.7492 | 0.6432 | 0.6921 | 0.7913 | |
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| 0.2546 | 3.98 | 3400 | 0.3164 | 0.2752 | 0.7661 | 0.6186 | 0.6845 | 0.7828 | |
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| 0.2274 | 4.09 | 3500 | 0.3172 | 0.2759 | 0.7437 | 0.6426 | 0.6895 | 0.7902 | |
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| 0.2328 | 4.21 | 3600 | 0.3214 | 0.2737 | 0.7548 | 0.6297 | 0.6866 | 0.7861 | |
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| 0.2354 | 4.33 | 3700 | 0.3192 | 0.2792 | 0.7546 | 0.6310 | 0.6872 | 0.7866 | |
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| 0.2238 | 4.44 | 3800 | 0.3199 | 0.2709 | 0.7453 | 0.6444 | 0.6912 | 0.7912 | |
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| 0.2376 | 4.56 | 3900 | 0.3176 | 0.2734 | 0.7599 | 0.6247 | 0.6857 | 0.7846 | |
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| 0.2344 | 4.68 | 4000 | 0.3189 | 0.2639 | 0.7437 | 0.6390 | 0.6874 | 0.7885 | |
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| 0.2222 | 4.8 | 4100 | 0.3222 | 0.2636 | 0.7436 | 0.6409 | 0.6884 | 0.7894 | |
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| 0.232 | 4.91 | 4200 | 0.3227 | 0.2725 | 0.7472 | 0.6426 | 0.6910 | 0.7907 | |
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| 0.2367 | 5.03 | 4300 | 0.3243 | 0.2670 | 0.7463 | 0.6339 | 0.6855 | 0.7866 | |
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| 0.2154 | 5.15 | 4400 | 0.3257 | 0.2593 | 0.7366 | 0.6513 | 0.6913 | 0.7929 | |
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| 0.2089 | 5.26 | 4500 | 0.3261 | 0.2700 | 0.7416 | 0.6453 | 0.6901 | 0.7910 | |
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| 0.2081 | 5.38 | 4600 | 0.3269 | 0.2731 | 0.7602 | 0.6133 | 0.6789 | 0.7794 | |
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| 0.2116 | 5.5 | 4700 | 0.3308 | 0.2593 | 0.7229 | 0.6687 | 0.6947 | 0.7983 | |
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| 0.2128 | 5.61 | 4800 | 0.3263 | 0.2660 | 0.7422 | 0.6432 | 0.6891 | 0.7902 | |
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| 0.2059 | 5.73 | 4900 | 0.3295 | 0.2728 | 0.7356 | 0.6550 | 0.6929 | 0.7944 | |
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| 0.2103 | 5.85 | 5000 | 0.3301 | 0.2814 | 0.7442 | 0.6510 | 0.6945 | 0.7940 | |
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| 0.2151 | 5.96 | 5100 | 0.3300 | 0.2541 | 0.7221 | 0.6598 | 0.6896 | 0.7942 | |
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| 0.1954 | 6.08 | 5200 | 0.3325 | 0.2765 | 0.7476 | 0.6381 | 0.6885 | 0.7887 | |
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| 0.2028 | 6.2 | 5300 | 0.3316 | 0.2559 | 0.7364 | 0.6400 | 0.6848 | 0.7878 | |
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| 0.1911 | 6.32 | 5400 | 0.3332 | 0.2553 | 0.7370 | 0.6386 | 0.6843 | 0.7873 | |
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| 0.2015 | 6.43 | 5500 | 0.3349 | 0.2645 | 0.7308 | 0.6538 | 0.6902 | 0.7931 | |
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| 0.1901 | 6.55 | 5600 | 0.3389 | 0.2587 | 0.7197 | 0.6682 | 0.6930 | 0.7975 | |
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| 0.197 | 6.67 | 5700 | 0.3349 | 0.2728 | 0.7400 | 0.6424 | 0.6878 | 0.7895 | |
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| 0.1907 | 6.78 | 5800 | 0.3354 | 0.2627 | 0.7454 | 0.6349 | 0.6857 | 0.7870 | |
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| 0.1853 | 6.9 | 5900 | 0.3420 | 0.2657 | 0.7356 | 0.6513 | 0.6909 | 0.7927 | |
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| 0.1841 | 7.02 | 6000 | 0.3399 | 0.2584 | 0.7308 | 0.6554 | 0.6910 | 0.7937 | |
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| 0.1739 | 7.13 | 6100 | 0.3409 | 0.2620 | 0.7364 | 0.6446 | 0.6874 | 0.7898 | |
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| 0.1768 | 7.25 | 6200 | 0.3417 | 0.2593 | 0.7314 | 0.6474 | 0.6868 | 0.7902 | |
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| 0.1762 | 7.37 | 6300 | 0.3384 | 0.2654 | 0.7398 | 0.6373 | 0.6847 | 0.7871 | |
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| 0.177 | 7.49 | 6400 | 0.3448 | 0.2541 | 0.7237 | 0.6547 | 0.6875 | 0.7922 | |
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| 0.1787 | 7.6 | 6500 | 0.3422 | 0.2513 | 0.7317 | 0.6425 | 0.6842 | 0.7881 | |
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| 0.1793 | 7.72 | 6600 | 0.3452 | 0.2611 | 0.7231 | 0.6582 | 0.6891 | 0.7936 | |
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| 0.1772 | 7.84 | 6700 | 0.3470 | 0.2587 | 0.7193 | 0.6618 | 0.6894 | 0.7946 | |
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| 0.1799 | 7.95 | 6800 | 0.3459 | 0.2547 | 0.7238 | 0.6494 | 0.6846 | 0.7898 | |
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| 0.1726 | 8.07 | 6900 | 0.3477 | 0.2507 | 0.7259 | 0.6419 | 0.6813 | 0.7869 | |
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| 0.1672 | 8.19 | 7000 | 0.3489 | 0.2492 | 0.7215 | 0.6499 | 0.6838 | 0.7897 | |
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| 0.1664 | 8.3 | 7100 | 0.3474 | 0.2498 | 0.7197 | 0.6491 | 0.6826 | 0.7890 | |
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| 0.1712 | 8.42 | 7200 | 0.3477 | 0.2516 | 0.7309 | 0.6404 | 0.6827 | 0.7870 | |
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| 0.166 | 8.54 | 7300 | 0.3487 | 0.2553 | 0.7209 | 0.6547 | 0.6862 | 0.7917 | |
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| 0.1706 | 8.65 | 7400 | 0.3487 | 0.2538 | 0.7239 | 0.6518 | 0.6860 | 0.7909 | |
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| 0.1674 | 8.77 | 7500 | 0.3506 | 0.2538 | 0.7216 | 0.6541 | 0.6862 | 0.7916 | |
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| 0.1655 | 8.89 | 7600 | 0.3476 | 0.2553 | 0.7283 | 0.6465 | 0.6849 | 0.7893 | |
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| 0.1609 | 9.01 | 7700 | 0.3498 | 0.2495 | 0.7273 | 0.6443 | 0.6833 | 0.7882 | |
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| 0.1647 | 9.12 | 7800 | 0.3507 | 0.2522 | 0.7255 | 0.6423 | 0.6814 | 0.7870 | |
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| 0.1531 | 9.24 | 7900 | 0.3503 | 0.2522 | 0.7292 | 0.6426 | 0.6832 | 0.7878 | |
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| 0.1577 | 9.36 | 8000 | 0.3524 | 0.2528 | 0.7212 | 0.6569 | 0.6875 | 0.7927 | |
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| 0.1592 | 9.47 | 8100 | 0.3517 | 0.2519 | 0.7186 | 0.6536 | 0.6845 | 0.7908 | |
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| 0.1615 | 9.59 | 8200 | 0.3514 | 0.2510 | 0.7183 | 0.6529 | 0.6841 | 0.7905 | |
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| 0.1529 | 9.71 | 8300 | 0.3515 | 0.2516 | 0.7221 | 0.6489 | 0.6835 | 0.7893 | |
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| 0.1607 | 9.82 | 8400 | 0.3520 | 0.2528 | 0.7212 | 0.6499 | 0.6837 | 0.7896 | |
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| 0.1506 | 9.94 | 8500 | 0.3524 | 0.2522 | 0.7220 | 0.6522 | 0.6853 | 0.7908 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
