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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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datasets: |
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- imdb |
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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: deberta-v3-large-imdb-v0.2 |
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results: [] |
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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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# deberta-v3-large-imdb-v0.2 |
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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 [imdb](https://huggingface.co./datasets/stanfordnlp/imdb) dataset. |
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It achieves the following results on the evaluation set @ epoch 9 of 10, which is loaded as the best model here: |
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- Accuracy: 0.9656 |
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- F1: 0.9657 |
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- Precision: 0.9640 |
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- Recall: 0.9673 |
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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: 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: cosine |
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- lr_scheduler_warmup_ratio: 0.2 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.2279 | 1.0 | 3125 | 0.1466 | 0.9603 | 0.9599 | 0.9693 | 0.9506 | |
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| 0.2689 | 2.0 | 6250 | 0.1929 | 0.9550 | 0.9546 | 0.9626 | 0.9467 | |
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| 0.1728 | 3.0 | 9375 | 0.1807 | 0.9584 | 0.9579 | 0.9697 | 0.9463 | |
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| 0.1937 | 4.0 | 12500 | 0.1734 | 0.9435 | 0.9457 | 0.9102 | 0.9841 | |
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| 0.2044 | 5.0 | 15625 | 0.2102 | 0.9510 | 0.9523 | 0.9272 | 0.9788 | |
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| 0.0484 | 6.0 | 18750 | 0.2134 | 0.9593 | 0.9599 | 0.9448 | 0.9756 | |
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| 0.0336 | 7.0 | 21875 | 0.2278 | 0.9610 | 0.9614 | 0.9524 | 0.9706 | |
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| 0.0704 | 8.0 | 25000 | 0.2039 | 0.9648 | 0.9651 | 0.9581 | 0.9721 | |
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| 0.0004 | 9.0 | 28125 | 0.2241 | 0.9656 | 0.9657 | 0.9640 | 0.9673 | |
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| 0.0004 | 10.0 | 31250 | 0.2233 | 0.9653 | 0.9654 | 0.9637 | 0.9670 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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