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doc-topic-model_eval-02_train-03

Text Classification
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deberta-v2
Generated from Trainer
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avsolatorio/doc-topic-model_eval-02_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-02_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-02_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: 0.0381
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+ - Accuracy: 0.9877
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+ - F1: 0.6218
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+ - Precision: 0.7302
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+ - Recall: 0.5414
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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 | 0.0899 | 0.9814 | 0.0 | 0.0 | 0.0 |
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+ | 0.0764 | 0.9862 | 2000 | 0.0701 | 0.9814 | 0.0 | 0.0 | 0.0 |
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+ | 0.0621 | 1.4793 | 3000 | 0.0569 | 0.9820 | 0.0746 | 0.9191 | 0.0389 |
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+ | 0.0542 | 1.9724 | 4000 | 0.0500 | 0.9840 | 0.2899 | 0.8341 | 0.1755 |
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+ | 0.0468 | 2.4655 | 5000 | 0.0468 | 0.9852 | 0.4234 | 0.7741 | 0.2914 |
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+ | 0.0441 | 2.9586 | 6000 | 0.0437 | 0.9861 | 0.4909 | 0.7705 | 0.3601 |
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+ | 0.0395 | 3.4517 | 7000 | 0.0420 | 0.9860 | 0.5308 | 0.7110 | 0.4235 |
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+ | 0.0384 | 3.9448 | 8000 | 0.0399 | 0.9867 | 0.5640 | 0.7255 | 0.4613 |
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+ | 0.0343 | 4.4379 | 9000 | 0.0392 | 0.9868 | 0.5773 | 0.7176 | 0.4829 |
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+ | 0.0337 | 4.9310 | 10000 | 0.0380 | 0.9873 | 0.5936 | 0.7367 | 0.4970 |
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+ | 0.0305 | 5.4241 | 11000 | 0.0374 | 0.9875 | 0.5965 | 0.7448 | 0.4974 |
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+ | 0.0295 | 5.9172 | 12000 | 0.0379 | 0.9874 | 0.6077 | 0.7252 | 0.5230 |
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+ | 0.0271 | 6.4103 | 13000 | 0.0375 | 0.9876 | 0.6052 | 0.7476 | 0.5083 |
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+ | 0.0257 | 6.9034 | 14000 | 0.0376 | 0.9877 | 0.6152 | 0.7354 | 0.5288 |
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+ | 0.0234 | 7.3964 | 15000 | 0.0374 | 0.9877 | 0.6281 | 0.7177 | 0.5583 |
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+ | 0.0241 | 7.8895 | 16000 | 0.0381 | 0.9877 | 0.6218 | 0.7302 | 0.5414 |
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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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