roberta-large-finetuned-abbr-unfiltered-plod

surrey-nlp/roberta-large-finetuned-abbr-unfiltered-plod

roberta-large-finetuned-abbr-unfiltered-plod is a token classification model from surrey-nlp. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-sa-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-sa-4.0
Model type: roberta
Access: Public
Created: Apr 20, 2023
Updated: May 28, 2024
SHA: fdcc6dcd

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Model card

From the Hugging Face model README

roberta-large-finetuned-abbr-unfiltered-plod

This model is a fine-tuned version of roberta-large on the PLODv2 unfiltered dataset. It is released with our LREC-COLING 2024 publication Using character-level models for efficient abbreviation and long-form detection. 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

Task: Token Classification
Library: transformers
Type: roberta
License: cc-by-sa-4.0
Languages: en
Created: Apr 20, 2023
Updated: May 28, 2024