---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-large-finetuned-abbr
  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-unfiltered-plod

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [PLODv2 unfiltered 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: 0.8916
- Recall: 0.9152
- F1: 0.9033


Results on long forms:
- Precision: 0.8607
- Recall: 0.9142
- F1: 0.8867


## 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 |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.167         | 0.25  | 7000   | 0.1616          | 0.9484    | 0.9366 | 0.9424 | 0.9376   |
| 0.1673        | 0.49  | 14000  | 0.1459          | 0.9504    | 0.9370 | 0.9437 | 0.9389   |
| 0.1472        | 0.74  | 21000  | 0.1560          | 0.9531    | 0.9373 | 0.9451 | 0.9398   |
| 0.1519        | 0.98  | 28000  | 0.1434          | 0.9551    | 0.9382 | 0.9466 | 0.9415   |
| 0.1388        | 1.23  | 35000  | 0.1472          | 0.9516    | 0.9374 | 0.9444 | 0.9400   |
| 0.1291        | 1.48  | 42000  | 0.1416          | 0.9557    | 0.9403 | 0.9479 | 0.9431   |
| 0.1298        | 1.72  | 49000  | 0.1394          | 0.9577    | 0.9459 | 0.9517 | 0.9470   |
| 0.1269        | 1.97  | 56000  | 0.1401          | 0.9587    | 0.9446 | 0.9516 | 0.9468   |
| 0.1128        | 2.21  | 63000  | 0.1410          | 0.9568    | 0.9497 | 0.9533 | 0.9486   |
| 0.1154        | 2.46  | 70000  | 0.1366          | 0.9583    | 0.9495 | 0.9539 | 0.9493   |
| 0.1138        | 2.71  | 77000  | 0.1413          | 0.9600    | 0.9502 | 0.9551 | 0.9506   |
| 0.1117        | 2.95  | 84000  | 0.1313          | 0.9605    | 0.9501 | 0.9552 | 0.9508   |
| 0.0997        | 3.2   | 91000  | 0.1503          | 0.9577    | 0.9527 | 0.9552 | 0.9507   |
| 0.1008        | 3.44  | 98000  | 0.1360          | 0.9587    | 0.9536 | 0.9561 | 0.9515   |
| 0.0909        | 3.69  | 105000 | 0.1435          | 0.9619    | 0.9520 | 0.9569 | 0.9525   |
| 0.0903        | 3.93  | 112000 | 0.1482          | 0.9619    | 0.9522 | 0.9570 | 0.9528   |
| 0.075         | 4.18  | 119000 | 0.1603          | 0.9616    | 0.9546 | 0.9581 | 0.9537   |
| 0.0804        | 4.43  | 126000 | 0.1512          | 0.9600    | 0.9560 | 0.9580 | 0.9536   |
| 0.0811        | 4.67  | 133000 | 0.1435          | 0.9628    | 0.9543 | 0.9585 | 0.9540   |
| 0.0778        | 4.92  | 140000 | 0.1384          | 0.9616    | 0.9566 | 0.9591 | 0.9548   |
| 0.065         | 5.16  | 147000 | 0.1640          | 0.9622    | 0.9567 | 0.9595 | 0.9550   |
| 0.0607        | 5.41  | 154000 | 0.1755          | 0.9632    | 0.9562 | 0.9597 | 0.9554   |
| 0.0587        | 5.66  | 161000 | 0.1643          | 0.9622    | 0.9575 | 0.9599 | 0.9555   |
| 0.062         | 5.9   | 168000 | 0.1663          | 0.9628    | 0.9569 | 0.9598 | 0.9556   |


### Framework versions

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