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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-large-finetuned-abbr-filtered-plod |
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results: [] |
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language: |
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- en |
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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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# roberta-large-finetuned-abbr-filtered-plod |
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This model is a fine-tuned version of the [roberta-large](https://huggingface.co/roberta-large) on the [PLODv2 filtered dataset](https://github.com/shenbinqian/PLODv2-CLM4AbbrDetection). |
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It is released with our LREC-COLING 2024 publication [Using character-level models for efficient abbreviation and long-form detection](https://aclanthology.org/2024.lrec-main.270/). It achieves the following results on the test set: |
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Results on abbreviations: |
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- Precision: 0.9073 |
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- Recall: 0.9348 |
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- F1: 0.9208 |
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Results on long forms: |
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- Precision: 0.8908 |
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- Recall: 0.9318 |
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- F1: 0.9108 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.1169 | 0.25 | 7000 | 0.1114 | 0.9639 | 0.9581 | 0.9610 | 0.9575 | |
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| 0.1171 | 0.5 | 14000 | 0.1150 | 0.9655 | 0.9534 | 0.9594 | 0.9554 | |
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| 0.1202 | 0.75 | 21000 | 0.1058 | 0.9644 | 0.9578 | 0.9611 | 0.9575 | |
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| 0.1105 | 0.99 | 28000 | 0.1098 | 0.9664 | 0.9549 | 0.9606 | 0.9566 | |
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| 0.0935 | 1.24 | 35000 | 0.1270 | 0.9643 | 0.9570 | 0.9606 | 0.9570 | |
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| 0.0999 | 1.49 | 42000 | 0.1112 | 0.9626 | 0.9605 | 0.9615 | 0.9580 | |
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| 0.0948 | 1.74 | 49000 | 0.1114 | 0.9670 | 0.9606 | 0.9638 | 0.9603 | |
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| 0.1015 | 1.99 | 56000 | 0.1146 | 0.9680 | 0.9589 | 0.9634 | 0.9597 | |
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| 0.0816 | 2.24 | 63000 | 0.1244 | 0.9670 | 0.9607 | 0.9638 | 0.9603 | |
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| 0.0855 | 2.49 | 70000 | 0.1107 | 0.9675 | 0.9623 | 0.9649 | 0.9614 | |
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| 0.0814 | 2.73 | 77000 | 0.1047 | 0.9661 | 0.9630 | 0.9645 | 0.9611 | |
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| 0.0827 | 2.98 | 84000 | 0.1082 | 0.9665 | 0.9631 | 0.9648 | 0.9614 | |
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| 0.0655 | 3.23 | 91000 | 0.1485 | 0.9690 | 0.9615 | 0.9653 | 0.9618 | |
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| 0.0631 | 3.48 | 98000 | 0.1314 | 0.9683 | 0.9639 | 0.9661 | 0.9627 | |
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| 0.0667 | 3.73 | 105000 | 0.1164 | 0.9683 | 0.9643 | 0.9663 | 0.9629 | |
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| 0.0652 | 3.98 | 112000 | 0.1297 | 0.9681 | 0.9653 | 0.9667 | 0.9633 | |
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| 0.0485 | 4.23 | 119000 | 0.1441 | 0.9697 | 0.9645 | 0.9671 | 0.9636 | |
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| 0.0505 | 4.47 | 126000 | 0.1350 | 0.9700 | 0.9651 | 0.9675 | 0.9642 | |
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| 0.0498 | 4.72 | 133000 | 0.1243 | 0.9691 | 0.9657 | 0.9674 | 0.9640 | |
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| 0.0463 | 4.97 | 140000 | 0.1392 | 0.9699 | 0.9660 | 0.9679 | 0.9645 | |
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| 0.0371 | 5.22 | 147000 | 0.1527 | 0.9709 | 0.9658 | 0.9683 | 0.9649 | |
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| 0.0363 | 5.47 | 154000 | 0.1490 | 0.9703 | 0.9667 | 0.9685 | 0.9651 | |
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| 0.0341 | 5.72 | 161000 | 0.1538 | 0.9712 | 0.9666 | 0.9689 | 0.9656 | |
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| 0.0338 | 5.97 | 168000 | 0.1488 | 0.9705 | 0.9668 | 0.9687 | 0.9653 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.10.3 |
