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Classifier_30k

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-large
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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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+ model-index:
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+ - name: Classifier_30k
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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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+ # Classifier_30k
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1296
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+ - Accuracy: 0.9876
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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-06
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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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: 50
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+ - mixed_precision_training: Native AMP
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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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+ | 0.3588 | 0.9994 | 831 | 0.3084 | 0.9091 |
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+ | 0.1252 | 2.0 | 1663 | 0.2260 | 0.9453 |
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+ | 0.1123 | 2.9994 | 2494 | 0.1241 | 0.9604 |
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+ | 0.0896 | 4.0 | 3326 | 0.1372 | 0.9655 |
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+ | 0.0749 | 4.9994 | 4157 | 0.1541 | 0.9708 |
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+ | 0.0743 | 6.0 | 4989 | 0.1127 | 0.9715 |
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+ | 0.0596 | 6.9994 | 5820 | 0.1782 | 0.9672 |
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+ | 0.0494 | 8.0 | 6652 | 0.1352 | 0.9749 |
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+ | 0.0443 | 8.9994 | 7483 | 0.1232 | 0.9681 |
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+ | 0.0405 | 10.0 | 8315 | 0.0756 | 0.9838 |
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+ | 0.0383 | 10.9994 | 9146 | 0.2025 | 0.9600 |
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+ | 0.0361 | 12.0 | 9978 | 0.1130 | 0.9796 |
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+ | 0.0288 | 12.9994 | 10809 | 0.0906 | 0.9855 |
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+ | 0.0249 | 14.0 | 11641 | 0.1122 | 0.9827 |
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+ | 0.0222 | 14.9994 | 12472 | 0.0713 | 0.9862 |
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+ | 0.0239 | 16.0 | 13304 | 0.0552 | 0.9876 |
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+ | 0.0234 | 16.9994 | 14135 | 0.0728 | 0.9864 |
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+ | 0.0258 | 18.0 | 14967 | 0.0558 | 0.9891 |
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+ | 0.0208 | 18.9994 | 15798 | 0.0715 | 0.9879 |
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+ | 0.0199 | 20.0 | 16630 | 0.0753 | 0.9885 |
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+ | 0.0143 | 20.9994 | 17461 | 0.0812 | 0.9872 |
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+ | 0.0255 | 22.0 | 18293 | 0.1661 | 0.9744 |
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+ | 0.0156 | 22.9994 | 19124 | 0.0751 | 0.9883 |
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+ | 0.013 | 24.0 | 19956 | 0.0718 | 0.9862 |
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+ | 0.0126 | 24.9994 | 20787 | 0.0829 | 0.9853 |
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+ | 0.0123 | 26.0 | 21619 | 0.0848 | 0.9857 |
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+ | 0.0109 | 26.9994 | 22450 | 0.0913 | 0.9864 |
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+ | 0.0095 | 28.0 | 23282 | 0.1607 | 0.9774 |
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+ | 0.0096 | 28.9994 | 24113 | 0.0958 | 0.9853 |
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+ | 0.0074 | 30.0 | 24945 | 0.1264 | 0.9857 |
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+ | 0.0091 | 30.9994 | 25776 | 0.1030 | 0.9881 |
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+ | 0.0096 | 32.0 | 26608 | 0.0954 | 0.9879 |
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+ | 0.0074 | 32.9994 | 27439 | 0.1103 | 0.9885 |
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+ | 0.0067 | 34.0 | 28271 | 0.1803 | 0.9791 |
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+ | 0.0044 | 34.9994 | 29102 | 0.1597 | 0.9817 |
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+ | 0.0045 | 36.0 | 29934 | 0.0878 | 0.9894 |
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+ | 0.0034 | 36.9994 | 30765 | 0.1680 | 0.9806 |
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+ | 0.0066 | 38.0 | 31597 | 0.1114 | 0.9870 |
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+ | 0.0041 | 38.9994 | 32428 | 0.0910 | 0.9896 |
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+ | 0.0043 | 40.0 | 33260 | 0.1435 | 0.9840 |
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+ | 0.0037 | 40.9994 | 34091 | 0.1233 | 0.9881 |
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+ | 0.0046 | 42.0 | 34923 | 0.1347 | 0.9864 |
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+ | 0.0029 | 42.9994 | 35754 | 0.1134 | 0.9883 |
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+ | 0.0017 | 44.0 | 36586 | 0.1125 | 0.9879 |
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+ | 0.0025 | 44.9994 | 37417 | 0.1400 | 0.9859 |
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+ | 0.0023 | 46.0 | 38249 | 0.1228 | 0.9879 |
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+ | 0.0017 | 46.9994 | 39080 | 0.1445 | 0.9862 |
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+ | 0.0011 | 48.0 | 39912 | 0.1375 | 0.9876 |
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+ | 0.0013 | 48.9994 | 40743 | 0.1323 | 0.9876 |
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+ | 0.0021 | 49.9699 | 41550 | 0.1296 | 0.9876 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.0
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+ - Pytorch 2.2.2+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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