metadata

license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-gec-roberta-large-v4
    results: []

ner-gec-roberta-large-v4

This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.2491
Precision: 0.6427
Recall: 0.5771
F1: 0.6081
Accuracy: 0.9614

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: 5e-05
train_batch_size: 64
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.2536	0.58	500	0.9347	0.0376	0.2469	0.0814	0.0245
0.2316	1.15	1000	0.9359	0.1365	0.2339	0.2272	0.0975
0.2175	1.73	1500	0.9392	0.1823	0.2172	0.2842	0.1342
0.1757	2.3	2000	0.9438	0.3123	0.1979	0.4011	0.2556
0.1682	2.88	2500	0.9502	0.3911	0.1817	0.4787	0.3307
0.121	3.46	3000	0.9537	0.4504	0.1753	0.5310	0.3910
0.0982	4.03	3500	0.9556	0.4980	0.1807	0.5606	0.4480
0.0858	4.61	4000	0.9577	0.5304	0.1732	0.5867	0.4839
0.0563	5.18	4500	0.1839	0.6007	0.5155	0.5548	0.9585
0.0586	5.76	5000	0.1804	0.6231	0.5237	0.5691	0.9605
0.0404	6.34	5500	0.1948	0.6214	0.5423	0.5792	0.9599
0.0397	6.91	6000	0.1994	0.6309	0.5458	0.5852	0.9610
0.0281	7.49	6500	0.2131	0.6345	0.5568	0.5931	0.9610
0.0182	8.06	7000	0.2249	0.6507	0.5649	0.6047	0.9625
0.0188	8.64	7500	0.2322	0.6413	0.5782	0.6081	0.9612
0.0123	9.22	8000	0.2473	0.6506	0.5777	0.6120	0.9622
0.0123	9.79	8500	0.2491	0.6427	0.5771	0.6081	0.9614

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu118
Datasets 2.16.1
Tokenizers 0.15.0