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doc-topic-model_eval-01_train-03

Text Classification
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deberta-v2
Generated from Trainer
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avsolatorio/doc-topic-model_eval-01_train-03

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/deberta-v3-small
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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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+ - precision
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+ - recall
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+ model-index:
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+ - name: doc-topic-model_eval-01_train-03
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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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+ # doc-topic-model_eval-01_train-03
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0378
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+ - Accuracy: 0.9879
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+ - F1: 0.6261
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+ - Precision: 0.7349
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+ - Recall: 0.5454
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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: 4
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+ - eval_batch_size: 256
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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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+ - 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 | F1 | Precision | Recall |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 0.0935 | 0.4931 | 1000 | 0.0898 | 0.9814 | 0.0 | 0.0 | 0.0 |
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+ | 0.0764 | 0.9862 | 2000 | 0.0702 | 0.9814 | 0.0 | 0.0 | 0.0 |
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+ | 0.0621 | 1.4793 | 3000 | 0.0570 | 0.9820 | 0.0695 | 0.8912 | 0.0362 |
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+ | 0.0542 | 1.9724 | 4000 | 0.0498 | 0.9840 | 0.2864 | 0.8319 | 0.1730 |
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+ | 0.0468 | 2.4655 | 5000 | 0.0468 | 0.9852 | 0.4191 | 0.7753 | 0.2872 |
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+ | 0.0441 | 2.9586 | 6000 | 0.0435 | 0.9861 | 0.4898 | 0.7741 | 0.3582 |
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+ | 0.0395 | 3.4517 | 7000 | 0.0418 | 0.9860 | 0.5279 | 0.7116 | 0.4196 |
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+ | 0.0384 | 3.9448 | 8000 | 0.0401 | 0.9866 | 0.5588 | 0.7206 | 0.4564 |
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+ | 0.0343 | 4.4379 | 9000 | 0.0392 | 0.9869 | 0.5774 | 0.7226 | 0.4809 |
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+ | 0.0337 | 4.9310 | 10000 | 0.0378 | 0.9873 | 0.5919 | 0.7400 | 0.4932 |
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+ | 0.0305 | 5.4241 | 11000 | 0.0373 | 0.9876 | 0.5989 | 0.7503 | 0.4983 |
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+ | 0.0295 | 5.9172 | 12000 | 0.0378 | 0.9875 | 0.6108 | 0.7303 | 0.5249 |
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+ | 0.0271 | 6.4103 | 13000 | 0.0375 | 0.9877 | 0.6080 | 0.7490 | 0.5116 |
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+ | 0.0257 | 6.9034 | 14000 | 0.0377 | 0.9876 | 0.6145 | 0.7284 | 0.5313 |
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+ | 0.0234 | 7.3964 | 15000 | 0.0377 | 0.9876 | 0.6243 | 0.7147 | 0.5542 |
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+ | 0.0241 | 7.8895 | 16000 | 0.0378 | 0.9879 | 0.6261 | 0.7349 | 0.5454 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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