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bertweet-finetuned_twitch-sentiment-analysis

Text Classification
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roberta
Generated from Trainer
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ base_model: finiteautomata/bertweet-base-sentiment-analysis
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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: bertweet-finetuned_twitch-sentiment-analysis
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+ results: []
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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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+ # bertweet-finetuned_twitch-sentiment-analysis
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+
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+ This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.3828
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+ - Accuracy: 0.6513
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+ - F1: 0.6513
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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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: 100
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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 | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 1.0 | 79 | 0.9173 | 0.5424 | 0.5424 |
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+ | 0.9476 | 2.0 | 158 | 0.9454 | 0.5701 | 0.5701 |
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+ | 0.8032 | 3.0 | 237 | 0.8781 | 0.6107 | 0.6107 |
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+ | 0.7289 | 4.0 | 316 | 0.9143 | 0.6218 | 0.6218 |
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+ | 0.7289 | 5.0 | 395 | 0.8310 | 0.6513 | 0.6513 |
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+ | 0.5873 | 6.0 | 474 | 0.9353 | 0.6624 | 0.6624 |
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+ | 0.4568 | 7.0 | 553 | 0.9365 | 0.6734 | 0.6734 |
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+ | 0.3544 | 8.0 | 632 | 1.0126 | 0.6494 | 0.6494 |
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+ | 0.3161 | 9.0 | 711 | 1.0378 | 0.6494 | 0.6494 |
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+ | 0.3161 | 10.0 | 790 | 1.2249 | 0.6568 | 0.6568 |
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+ | 0.2757 | 11.0 | 869 | 1.1352 | 0.6808 | 0.6808 |
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+ | 0.2619 | 12.0 | 948 | 1.2467 | 0.6697 | 0.6697 |
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+ | 0.2292 | 13.0 | 1027 | 1.3262 | 0.6716 | 0.6716 |
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+ | 0.2115 | 14.0 | 1106 | 1.3367 | 0.6697 | 0.6697 |
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+ | 0.2115 | 15.0 | 1185 | 1.3757 | 0.6882 | 0.6882 |
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+ | 0.1848 | 16.0 | 1264 | 1.3650 | 0.6697 | 0.6697 |
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+ | 0.1916 | 17.0 | 1343 | 1.4940 | 0.6587 | 0.6587 |
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+ | 0.1734 | 18.0 | 1422 | 1.5929 | 0.6808 | 0.6808 |
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+ | 0.1715 | 19.0 | 1501 | 1.5662 | 0.6734 | 0.6734 |
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+ | 0.1715 | 20.0 | 1580 | 1.6073 | 0.6845 | 0.6845 |
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+ | 0.1711 | 21.0 | 1659 | 1.5038 | 0.6808 | 0.6808 |
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+ | 0.1735 | 22.0 | 1738 | 1.8104 | 0.6587 | 0.6587 |
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+ | 0.142 | 23.0 | 1817 | 1.4715 | 0.6900 | 0.6900 |
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+ | 0.142 | 24.0 | 1896 | 1.7028 | 0.6863 | 0.6863 |
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+ | 0.1504 | 25.0 | 1975 | 1.5413 | 0.6900 | 0.6900 |
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+ | 0.1536 | 26.0 | 2054 | 1.7148 | 0.6624 | 0.6624 |
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+ | 0.1405 | 27.0 | 2133 | 1.5510 | 0.6624 | 0.6624 |
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+ | 0.1296 | 28.0 | 2212 | 1.6857 | 0.6863 | 0.6863 |
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+ | 0.1296 | 29.0 | 2291 | 1.6228 | 0.6679 | 0.6679 |
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+ | 0.1247 | 30.0 | 2370 | 1.7248 | 0.6716 | 0.6716 |
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+ | 0.1181 | 31.0 | 2449 | 1.7833 | 0.6716 | 0.6716 |
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+ | 0.1342 | 32.0 | 2528 | 1.9463 | 0.6661 | 0.6661 |
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+ | 0.1412 | 33.0 | 2607 | 1.9416 | 0.6734 | 0.6734 |
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+ | 0.1412 | 34.0 | 2686 | 1.7277 | 0.6679 | 0.6679 |
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+ | 0.1114 | 35.0 | 2765 | 1.7833 | 0.6734 | 0.6734 |
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+ | 0.1139 | 36.0 | 2844 | 1.8031 | 0.6753 | 0.6753 |
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+ | 0.1143 | 37.0 | 2923 | 1.7150 | 0.6716 | 0.6716 |
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+ | 0.1031 | 38.0 | 3002 | 1.9060 | 0.6827 | 0.6827 |
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+ | 0.1031 | 39.0 | 3081 | 1.8854 | 0.6587 | 0.6587 |
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+ | 0.1162 | 40.0 | 3160 | 1.8868 | 0.6753 | 0.6753 |
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+ | 0.1115 | 41.0 | 3239 | 1.7967 | 0.6808 | 0.6808 |
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+ | 0.1118 | 42.0 | 3318 | 1.9692 | 0.6661 | 0.6661 |
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+ | 0.1118 | 43.0 | 3397 | 1.9876 | 0.6661 | 0.6661 |
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+ | 0.1017 | 44.0 | 3476 | 1.9332 | 0.6642 | 0.6642 |
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+ | 0.1172 | 45.0 | 3555 | 1.8807 | 0.6679 | 0.6679 |
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+ | 0.1128 | 46.0 | 3634 | 1.9357 | 0.7011 | 0.7011 |
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+ | 0.1196 | 47.0 | 3713 | 2.0208 | 0.6679 | 0.6679 |
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+ | 0.1196 | 48.0 | 3792 | 1.9668 | 0.6679 | 0.6679 |
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+ | 0.0955 | 49.0 | 3871 | 2.0051 | 0.6661 | 0.6661 |
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+ | 0.0959 | 50.0 | 3950 | 1.9267 | 0.6661 | 0.6661 |
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+ | 0.1144 | 51.0 | 4029 | 2.0940 | 0.6716 | 0.6716 |
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+ | 0.107 | 52.0 | 4108 | 2.1097 | 0.6697 | 0.6697 |
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+ | 0.107 | 53.0 | 4187 | 2.0383 | 0.6624 | 0.6624 |
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+ | 0.1176 | 54.0 | 4266 | 1.9996 | 0.6587 | 0.6587 |
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+ | 0.112 | 55.0 | 4345 | 2.0815 | 0.6716 | 0.6716 |
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+ | 0.1033 | 56.0 | 4424 | 1.8365 | 0.6661 | 0.6661 |
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+ | 0.116 | 57.0 | 4503 | 2.0785 | 0.6679 | 0.6679 |
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+ | 0.116 | 58.0 | 4582 | 2.0580 | 0.6624 | 0.6624 |
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+ | 0.1048 | 59.0 | 4661 | 2.0619 | 0.6863 | 0.6863 |
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+ | 0.0907 | 60.0 | 4740 | 2.0260 | 0.6753 | 0.6753 |
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+ | 0.1021 | 61.0 | 4819 | 2.0572 | 0.6753 | 0.6753 |
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+ | 0.1021 | 62.0 | 4898 | 1.9949 | 0.6753 | 0.6753 |
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+ | 0.0921 | 63.0 | 4977 | 2.0043 | 0.6808 | 0.6808 |
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+ | 0.099 | 64.0 | 5056 | 2.1510 | 0.6697 | 0.6697 |
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+ | 0.0792 | 65.0 | 5135 | 2.1658 | 0.6642 | 0.6642 |
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+ | 0.1056 | 66.0 | 5214 | 2.0118 | 0.6734 | 0.6734 |
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+ | 0.1056 | 67.0 | 5293 | 2.1683 | 0.6661 | 0.6661 |
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+ | 0.0994 | 68.0 | 5372 | 2.1810 | 0.6734 | 0.6734 |
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+ | 0.1054 | 69.0 | 5451 | 2.0225 | 0.6900 | 0.6900 |
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+ | 0.0975 | 70.0 | 5530 | 2.1230 | 0.6679 | 0.6679 |
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+ | 0.0885 | 71.0 | 5609 | 2.0770 | 0.6808 | 0.6808 |
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+ | 0.0885 | 72.0 | 5688 | 2.0654 | 0.6771 | 0.6771 |
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+ | 0.0939 | 73.0 | 5767 | 2.1239 | 0.6624 | 0.6624 |
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+ | 0.1028 | 74.0 | 5846 | 2.1897 | 0.6771 | 0.6771 |
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+ | 0.0851 | 75.0 | 5925 | 2.0848 | 0.6790 | 0.6790 |
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+ | 0.0783 | 76.0 | 6004 | 2.1199 | 0.6734 | 0.6734 |
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+ | 0.0783 | 77.0 | 6083 | 2.2011 | 0.6734 | 0.6734 |
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+ | 0.0874 | 78.0 | 6162 | 2.1734 | 0.6679 | 0.6679 |
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+ | 0.0878 | 79.0 | 6241 | 2.1986 | 0.6624 | 0.6624 |
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+ | 0.0939 | 80.0 | 6320 | 2.2401 | 0.6642 | 0.6642 |
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+ | 0.0939 | 81.0 | 6399 | 2.3477 | 0.6605 | 0.6605 |
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+ | 0.0835 | 82.0 | 6478 | 2.3740 | 0.6605 | 0.6605 |
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+ | 0.0887 | 83.0 | 6557 | 2.3200 | 0.6661 | 0.6661 |
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+ | 0.0943 | 84.0 | 6636 | 2.3248 | 0.6642 | 0.6642 |
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+ | 0.0875 | 85.0 | 6715 | 2.3079 | 0.6605 | 0.6605 |
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+ | 0.0875 | 86.0 | 6794 | 2.3209 | 0.6568 | 0.6568 |
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+ | 0.0822 | 87.0 | 6873 | 2.3303 | 0.6587 | 0.6587 |
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+ | 0.0846 | 88.0 | 6952 | 2.3620 | 0.6531 | 0.6531 |
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+ | 0.0909 | 89.0 | 7031 | 2.3498 | 0.6587 | 0.6587 |
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+ | 0.0871 | 90.0 | 7110 | 2.3323 | 0.6513 | 0.6513 |
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+ | 0.0871 | 91.0 | 7189 | 2.3494 | 0.6513 | 0.6513 |
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+ | 0.0796 | 92.0 | 7268 | 2.3677 | 0.6513 | 0.6513 |
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+ | 0.0797 | 93.0 | 7347 | 2.3887 | 0.6513 | 0.6513 |
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+ | 0.0959 | 94.0 | 7426 | 2.3747 | 0.6513 | 0.6513 |
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+ | 0.0861 | 95.0 | 7505 | 2.3896 | 0.6550 | 0.6550 |
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+ | 0.0861 | 96.0 | 7584 | 2.3786 | 0.6531 | 0.6531 |
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+ | 0.089 | 97.0 | 7663 | 2.3692 | 0.6531 | 0.6531 |
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+ | 0.0764 | 98.0 | 7742 | 2.3789 | 0.6494 | 0.6494 |
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+ | 0.0874 | 99.0 | 7821 | 2.3833 | 0.6513 | 0.6513 |
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+ | 0.0852 | 100.0 | 7900 | 2.3828 | 0.6513 | 0.6513 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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