metadata

license: mit
base_model: Gladiator/microsoft-deberta-v3-large_ner_conll2003
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_tag_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        metrics:
          - name: Precision
            type: precision
            value: 0.8568714588197879
          - name: Recall
            type: recall
            value: 0.8550538245045557
          - name: F1
            type: f1
            value: 0.8559616767268047
          - name: Accuracy
            type: accuracy
            value: 0.9150941588185013]
language:
  - en

ner_tag_model

This model is a fine-tuned version of Gladiator/microsoft-deberta-v3-large_ner_conll2003 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1712
Precision: 0.8569
Recall: 0.8551
F1: 0.8560
Accuracy: 0.9151

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2322	1.0	2495	0.1925	0.7990	0.7924	0.7957	0.8969
0.1674	2.0	4990	0.1488	0.8218	0.8316	0.8267	0.9116
0.1381	3.0	7485	0.1438	0.8204	0.8350	0.8276	0.9130
0.1284	4.0	9980	0.1381	0.8419	0.8405	0.8412	0.9148
0.1198	5.0	12475	0.1400	0.8280	0.8410	0.8345	0.9148
0.1155	6.0	14970	0.1395	0.8379	0.8467	0.8423	0.9154
0.1125	7.0	17465	0.1496	0.8438	0.8487	0.8462	0.9151
0.1068	8.0	19960	0.1510	0.8518	0.8529	0.8523	0.9156
0.1002	9.0	22455	0.1616	0.8536	0.8539	0.8537	0.9150
0.0964	10.0	24950	0.1712	0.8569	0.8551	0.8560	0.9151

Framework versions

Transformers 4.33.1
Pytorch 1.13.1+cu116
Datasets 2.14.5
Tokenizers 0.13.3