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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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- tweet_eval |
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metrics: |
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- accuracy |
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model-index: |
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- name: TweetEval_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: tweet_eval |
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type: tweet_eval |
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config: sentiment |
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split: train |
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args: sentiment |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9266666666666666 |
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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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# TweetEval_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 tweet_eval dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1990 |
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- Accuracy: 0.9267 |
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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: 3e-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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| 0.4636 | 0.04 | 50 | 0.3662 | 0.8667 | |
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| 0.442 | 0.08 | 100 | 0.3471 | 0.84 | |
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| 0.3574 | 0.12 | 150 | 0.3446 | 0.86 | |
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| 0.392 | 0.16 | 200 | 0.6776 | 0.6267 | |
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| 0.4801 | 0.2 | 250 | 0.4307 | 0.7667 | |
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| 0.487 | 0.24 | 300 | 0.5127 | 0.8 | |
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| 0.4414 | 0.28 | 350 | 0.3912 | 0.8133 | |
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| 0.4495 | 0.32 | 400 | 0.4056 | 0.8333 | |
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| 0.4637 | 0.37 | 450 | 0.3635 | 0.8533 | |
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| 0.4231 | 0.41 | 500 | 0.4235 | 0.84 | |
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| 0.4049 | 0.45 | 550 | 0.4094 | 0.8067 | |
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| 0.4481 | 0.49 | 600 | 0.3977 | 0.7733 | |
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| 0.4024 | 0.53 | 650 | 0.3361 | 0.8733 | |
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| 0.3901 | 0.57 | 700 | 0.3014 | 0.8667 | |
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| 0.3872 | 0.61 | 750 | 0.3363 | 0.8533 | |
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| 0.377 | 0.65 | 800 | 0.3754 | 0.8 | |
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| 0.459 | 0.69 | 850 | 0.3861 | 0.8 | |
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| 0.437 | 0.73 | 900 | 0.3834 | 0.8333 | |
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| 0.3823 | 0.77 | 950 | 0.3541 | 0.8733 | |
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| 0.3561 | 0.81 | 1000 | 0.3177 | 0.84 | |
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| 0.4536 | 0.85 | 1050 | 0.4291 | 0.78 | |
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| 0.4457 | 0.89 | 1100 | 0.3193 | 0.86 | |
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| 0.3478 | 0.93 | 1150 | 0.3159 | 0.8533 | |
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| 0.4613 | 0.97 | 1200 | 0.3605 | 0.84 | |
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| 0.4081 | 1.01 | 1250 | 0.4291 | 0.7867 | |
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| 0.3849 | 1.06 | 1300 | 0.3114 | 0.8733 | |
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| 0.4071 | 1.1 | 1350 | 0.2939 | 0.8667 | |
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| 0.3484 | 1.14 | 1400 | 0.3212 | 0.84 | |
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| 0.3869 | 1.18 | 1450 | 0.2717 | 0.8933 | |
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| 0.3877 | 1.22 | 1500 | 0.3459 | 0.84 | |
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| 0.4245 | 1.26 | 1550 | 0.3404 | 0.8733 | |
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| 0.4148 | 1.3 | 1600 | 0.2863 | 0.8667 | |
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| 0.3542 | 1.34 | 1650 | 0.3377 | 0.86 | |
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| 0.4093 | 1.38 | 1700 | 0.2972 | 0.8867 | |
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| 0.3579 | 1.42 | 1750 | 0.3926 | 0.86 | |
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| 0.3892 | 1.46 | 1800 | 0.2870 | 0.8667 | |
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| 0.3569 | 1.5 | 1850 | 0.4027 | 0.8467 | |
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| 0.3493 | 1.54 | 1900 | 0.3069 | 0.8467 | |
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| 0.36 | 1.58 | 1950 | 0.3197 | 0.8733 | |
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| 0.3532 | 1.62 | 2000 | 0.3711 | 0.8667 | |
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| 0.3311 | 1.66 | 2050 | 0.2897 | 0.8867 | |
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| 0.346 | 1.7 | 2100 | 0.2938 | 0.88 | |
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| 0.3389 | 1.75 | 2150 | 0.2734 | 0.8933 | |
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| 0.3289 | 1.79 | 2200 | 0.2606 | 0.8867 | |
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| 0.3558 | 1.83 | 2250 | 0.3070 | 0.88 | |
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| 0.3277 | 1.87 | 2300 | 0.2757 | 0.8867 | |
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| 0.3166 | 1.91 | 2350 | 0.2759 | 0.8733 | |
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| 0.3223 | 1.95 | 2400 | 0.2053 | 0.9133 | |
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| 0.317 | 1.99 | 2450 | 0.2307 | 0.8867 | |
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| 0.3408 | 2.03 | 2500 | 0.2557 | 0.9067 | |
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| 0.3212 | 2.07 | 2550 | 0.2508 | 0.8867 | |
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| 0.2806 | 2.11 | 2600 | 0.2472 | 0.88 | |
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| 0.3567 | 2.15 | 2650 | 0.2790 | 0.8933 | |
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| 0.2887 | 2.19 | 2700 | 0.3197 | 0.88 | |
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| 0.3222 | 2.23 | 2750 | 0.2943 | 0.8667 | |
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| 0.2773 | 2.27 | 2800 | 0.2297 | 0.88 | |
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| 0.2728 | 2.31 | 2850 | 0.2813 | 0.8733 | |
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| 0.3115 | 2.35 | 2900 | 0.3470 | 0.8867 | |
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| 0.3001 | 2.39 | 2950 | 0.2702 | 0.8933 | |
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| 0.3464 | 2.44 | 3000 | 0.2855 | 0.9 | |
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| 0.3041 | 2.48 | 3050 | 0.2366 | 0.8867 | |
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| 0.2717 | 2.52 | 3100 | 0.3220 | 0.88 | |
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| 0.2903 | 2.56 | 3150 | 0.2230 | 0.9 | |
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| 0.2959 | 2.6 | 3200 | 0.2439 | 0.9067 | |
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| 0.2753 | 2.64 | 3250 | 0.2918 | 0.8733 | |
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| 0.2515 | 2.68 | 3300 | 0.2493 | 0.88 | |
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| 0.295 | 2.72 | 3350 | 0.2673 | 0.8867 | |
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| 0.2572 | 2.76 | 3400 | 0.2842 | 0.8733 | |
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| 0.2988 | 2.8 | 3450 | 0.2306 | 0.9067 | |
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| 0.2923 | 2.84 | 3500 | 0.2329 | 0.8933 | |
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| 0.2856 | 2.88 | 3550 | 0.2374 | 0.88 | |
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| 0.2867 | 2.92 | 3600 | 0.2294 | 0.8733 | |
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| 0.306 | 2.96 | 3650 | 0.2169 | 0.92 | |
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| 0.2312 | 3.0 | 3700 | 0.2456 | 0.88 | |
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| 0.2438 | 3.04 | 3750 | 0.2134 | 0.8867 | |
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| 0.2103 | 3.08 | 3800 | 0.2242 | 0.92 | |
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| 0.2469 | 3.12 | 3850 | 0.2407 | 0.92 | |
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| 0.2346 | 3.17 | 3900 | 0.1866 | 0.92 | |
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| 0.2275 | 3.21 | 3950 | 0.2318 | 0.92 | |
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| 0.2542 | 3.25 | 4000 | 0.2256 | 0.9 | |
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| 0.2544 | 3.29 | 4050 | 0.2246 | 0.9133 | |
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| 0.2468 | 3.33 | 4100 | 0.2436 | 0.8733 | |
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| 0.2105 | 3.37 | 4150 | 0.2098 | 0.9067 | |
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| 0.2818 | 3.41 | 4200 | 0.2304 | 0.88 | |
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| 0.2041 | 3.45 | 4250 | 0.2430 | 0.8933 | |
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| 0.28 | 3.49 | 4300 | 0.1990 | 0.9067 | |
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| 0.1997 | 3.53 | 4350 | 0.2515 | 0.8933 | |
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| 0.2409 | 3.57 | 4400 | 0.2315 | 0.9 | |
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| 0.1969 | 3.61 | 4450 | 0.2160 | 0.8933 | |
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| 0.2246 | 3.65 | 4500 | 0.1979 | 0.92 | |
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| 0.2185 | 3.69 | 4550 | 0.2238 | 0.9 | |
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| 0.259 | 3.73 | 4600 | 0.2011 | 0.9067 | |
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| 0.2407 | 3.77 | 4650 | 0.1911 | 0.92 | |
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| 0.2198 | 3.81 | 4700 | 0.2083 | 0.92 | |
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| 0.235 | 3.86 | 4750 | 0.1724 | 0.9267 | |
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| 0.26 | 3.9 | 4800 | 0.1640 | 0.9333 | |
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| 0.2334 | 3.94 | 4850 | 0.1778 | 0.9267 | |
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| 0.2121 | 3.98 | 4900 | 0.2062 | 0.8933 | |
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| 0.173 | 4.02 | 4950 | 0.1987 | 0.92 | |
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| 0.1942 | 4.06 | 5000 | 0.2509 | 0.8933 | |
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| 0.1703 | 4.1 | 5050 | 0.2179 | 0.9 | |
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| 0.1735 | 4.14 | 5100 | 0.2429 | 0.8867 | |
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| 0.2098 | 4.18 | 5150 | 0.1938 | 0.9267 | |
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| 0.2126 | 4.22 | 5200 | 0.1971 | 0.92 | |
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| 0.164 | 4.26 | 5250 | 0.2539 | 0.9067 | |
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| 0.2271 | 4.3 | 5300 | 0.1765 | 0.94 | |
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| 0.2245 | 4.34 | 5350 | 0.1894 | 0.94 | |
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| 0.182 | 4.38 | 5400 | 0.1790 | 0.9467 | |
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| 0.1835 | 4.42 | 5450 | 0.2014 | 0.9333 | |
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| 0.2185 | 4.46 | 5500 | 0.1881 | 0.9467 | |
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| 0.2113 | 4.5 | 5550 | 0.1742 | 0.9333 | |
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| 0.1997 | 4.55 | 5600 | 0.1762 | 0.94 | |
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| 0.1959 | 4.59 | 5650 | 0.1657 | 0.9467 | |
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| 0.2035 | 4.63 | 5700 | 0.1973 | 0.92 | |
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| 0.228 | 4.67 | 5750 | 0.1769 | 0.9467 | |
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| 0.1632 | 4.71 | 5800 | 0.1968 | 0.9267 | |
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| 0.1468 | 4.75 | 5850 | 0.1822 | 0.9467 | |
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| 0.1936 | 4.79 | 5900 | 0.1832 | 0.94 | |
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| 0.1743 | 4.83 | 5950 | 0.1987 | 0.9267 | |
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| 0.1654 | 4.87 | 6000 | 0.1943 | 0.9267 | |
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| 0.1859 | 4.91 | 6050 | 0.1990 | 0.92 | |
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| 0.2039 | 4.95 | 6100 | 0.1982 | 0.9267 | |
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| 0.2325 | 4.99 | 6150 | 0.1990 | 0.9267 | |
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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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