---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-abbr-filtered-plod
  results: []
language:
- en
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-large-finetuned-abbr-filtered-plod

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).
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:

Results on abbreviations:
- Precision: 0.9073
- Recall: 0.9348
- F1: 0.9208

Results on long forms:
- Precision: 0.8908
- Recall: 0.9318
- F1: 0.9108

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1169        | 0.25  | 7000   | 0.1114          | 0.9639    | 0.9581 | 0.9610 | 0.9575   |
| 0.1171        | 0.5   | 14000  | 0.1150          | 0.9655    | 0.9534 | 0.9594 | 0.9554   |
| 0.1202        | 0.75  | 21000  | 0.1058          | 0.9644    | 0.9578 | 0.9611 | 0.9575   |
| 0.1105        | 0.99  | 28000  | 0.1098          | 0.9664    | 0.9549 | 0.9606 | 0.9566   |
| 0.0935        | 1.24  | 35000  | 0.1270          | 0.9643    | 0.9570 | 0.9606 | 0.9570   |
| 0.0999        | 1.49  | 42000  | 0.1112          | 0.9626    | 0.9605 | 0.9615 | 0.9580   |
| 0.0948        | 1.74  | 49000  | 0.1114          | 0.9670    | 0.9606 | 0.9638 | 0.9603   |
| 0.1015        | 1.99  | 56000  | 0.1146          | 0.9680    | 0.9589 | 0.9634 | 0.9597   |
| 0.0816        | 2.24  | 63000  | 0.1244          | 0.9670    | 0.9607 | 0.9638 | 0.9603   |
| 0.0855        | 2.49  | 70000  | 0.1107          | 0.9675    | 0.9623 | 0.9649 | 0.9614   |
| 0.0814        | 2.73  | 77000  | 0.1047          | 0.9661    | 0.9630 | 0.9645 | 0.9611   |
| 0.0827        | 2.98  | 84000  | 0.1082          | 0.9665    | 0.9631 | 0.9648 | 0.9614   |
| 0.0655        | 3.23  | 91000  | 0.1485          | 0.9690    | 0.9615 | 0.9653 | 0.9618   |
| 0.0631        | 3.48  | 98000  | 0.1314          | 0.9683    | 0.9639 | 0.9661 | 0.9627   |
| 0.0667        | 3.73  | 105000 | 0.1164          | 0.9683    | 0.9643 | 0.9663 | 0.9629   |
| 0.0652        | 3.98  | 112000 | 0.1297          | 0.9681    | 0.9653 | 0.9667 | 0.9633   |
| 0.0485        | 4.23  | 119000 | 0.1441          | 0.9697    | 0.9645 | 0.9671 | 0.9636   |
| 0.0505        | 4.47  | 126000 | 0.1350          | 0.9700    | 0.9651 | 0.9675 | 0.9642   |
| 0.0498        | 4.72  | 133000 | 0.1243          | 0.9691    | 0.9657 | 0.9674 | 0.9640   |
| 0.0463        | 4.97  | 140000 | 0.1392          | 0.9699    | 0.9660 | 0.9679 | 0.9645   |
| 0.0371        | 5.22  | 147000 | 0.1527          | 0.9709    | 0.9658 | 0.9683 | 0.9649   |
| 0.0363        | 5.47  | 154000 | 0.1490          | 0.9703    | 0.9667 | 0.9685 | 0.9651   |
| 0.0341        | 5.72  | 161000 | 0.1538          | 0.9712    | 0.9666 | 0.9689 | 0.9656   |
| 0.0338        | 5.97  | 168000 | 0.1488          | 0.9705    | 0.9668 | 0.9687 | 0.9653   |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.10.3