This model is a fine-tuned version of surrey-nlp/roberta-large-finetuned-abbr on the plod-filtered dataset. It achieves the following results on the evaluation set:

Loss: 0.0913
Precision: 0.9800
Recall: 0.9767
F1: 0.9784
Accuracy: 0.9762

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: 16
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
0.0805	0.99	7000	0.0761	0.9762	0.9722	0.9742	0.9720
0.0655	1.99	14000	0.0682	0.9769	0.9748	0.9759	0.9735
0.0469	2.98	21000	0.0718	0.9787	0.9746	0.9767	0.9744
0.0336	3.98	28000	0.0851	0.9800	0.9753	0.9776	0.9753
0.0259	4.97	35000	0.0913	0.9800	0.9767	0.9784	0.9762
0.0197	5.97	42000	0.0948	0.9801	0.9774	0.9787	0.9766

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

EvgeniaKomleva/roberta-large-finetuned-abbr-finetuned-ner

roberta-large-finetuned-abbr-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

EvgeniaKomleva/
roberta-large-finetuned-abbr-finetuned-ner