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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: debert-imeocap |
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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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# debert-imeocap |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3914 |
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- F1: 0.6372 |
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- Precision: 0.6448 |
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- Recall: 0.6365 |
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- Accuracy: 0.6365 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 1.5405 | 1.0 | 74 | 1.4488 | 0.3206 | 0.2386 | 0.4885 | 0.4885 | |
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| 1.3156 | 2.0 | 148 | 1.1964 | 0.5541 | 0.5627 | 0.575 | 0.575 | |
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| 1.0728 | 3.0 | 222 | 1.1077 | 0.6001 | 0.6189 | 0.5981 | 0.5981 | |
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| 0.9239 | 4.0 | 296 | 1.0742 | 0.6324 | 0.6361 | 0.6365 | 0.6365 | |
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| 0.7802 | 5.0 | 370 | 1.0834 | 0.6073 | 0.6333 | 0.6058 | 0.6058 | |
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| 0.661 | 6.0 | 444 | 1.1733 | 0.5984 | 0.6166 | 0.5962 | 0.5962 | |
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| 0.602 | 7.0 | 518 | 1.1786 | 0.5911 | 0.6193 | 0.5885 | 0.5885 | |
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| 0.5391 | 8.0 | 592 | 1.2171 | 0.6156 | 0.6251 | 0.6154 | 0.6154 | |
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| 0.4815 | 9.0 | 666 | 1.2566 | 0.6259 | 0.6399 | 0.625 | 0.625 | |
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| 0.4548 | 10.0 | 740 | 1.2927 | 0.6233 | 0.6417 | 0.6212 | 0.6212 | |
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| 0.4538 | 11.0 | 814 | 1.2969 | 0.6385 | 0.6461 | 0.6385 | 0.6385 | |
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| 0.4119 | 12.0 | 888 | 1.3455 | 0.6376 | 0.6464 | 0.6365 | 0.6365 | |
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| 0.3968 | 13.0 | 962 | 1.3709 | 0.6304 | 0.6413 | 0.6288 | 0.6288 | |
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| 0.352 | 14.0 | 1036 | 1.3823 | 0.6246 | 0.6360 | 0.6231 | 0.6231 | |
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| 0.3551 | 15.0 | 1110 | 1.3914 | 0.6372 | 0.6448 | 0.6365 | 0.6365 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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