metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
datasets:
  - fursov/gec_ner_val3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner-gec-roberta-v3
    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: 0.5705440070765149
          - name: Recall
            type: recall
            value: 0.43481191856545776
          - name: F1
            type: f1
            value: 0.493515436703776
          - name: Accuracy
            type: accuracy
            value: 0.9566099116988466

ner-gec-roberta-v3

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

Loss: 0.1759
Precision: 0.5705
Recall: 0.4348
F1: 0.4935
Accuracy: 0.9566

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: 128
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
0.2421	1.15	500	0.9349	0.0868	0.2389	0.1631	0.0591
0.2065	2.3	1000	0.9381	0.2139	0.2182	0.3006	0.1660
0.1729	3.46	1500	0.9446	0.3066	0.1986	0.4014	0.2480
0.1558	4.61	2000	0.9485	0.3556	0.1899	0.4544	0.2921
0.1546	5.76	2500	0.1857	0.4823	0.3191	0.3841	0.9504
0.1343	6.91	3000	0.1784	0.5302	0.3794	0.4423	0.9535
0.1163	8.06	3500	0.1767	0.5563	0.4094	0.4717	0.9556
0.1045	9.22	4000	0.1783	0.5595	0.4328	0.4880	0.9554

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu118
Datasets 2.16.1
Tokenizers 0.15.0