metadata

license: mit
base_model: roberta-large
tags:
  - generated_from_trainer
datasets:
  - fursov/gec_ner_val3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-gec-roberta-large-v4
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: fursov/gec_ner_val3
          type: fursov/gec_ner_val3
        metrics:
          - name: Precision
            type: precision
            value: 0.643409688321442
          - name: Recall
            type: recall
            value: 0.5775246056357017
          - name: F1
            type: f1
            value: 0.6086894738711854
          - name: Accuracy
            type: accuracy
            value: 0.9614897122818877

ner-gec-roberta-large-v4

This model is a fine-tuned version of roberta-large on the fursov/gec_ner_val3 dataset. It achieves the following results on the evaluation set:

Loss: 0.2489
Precision: 0.6434
Recall: 0.5775
F1: 0.6087
Accuracy: 0.9615

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