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