metadata

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

roberta-large-finetuned-abbr-filtered-plod

This model is a fine-tuned version of the roberta-large on the PLODv2 filtered dataset. It is released with our LREC-COLING 2024 publication (coming soon). 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