metadata

license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - indian_names
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_awesome_wnut_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: indian_names
          type: indian_names
          config: indian_names
          split: train
          args: indian_names
        metrics:
          - name: Precision
            type: precision
            value: 0.980007544322897
          - name: Recall
            type: recall
            value: 0.979145728643216
          - name: F1
            type: f1
            value: 0.9795764469301829
          - name: Accuracy
            type: accuracy
            value: 0.9962591162591162

my_awesome_wnut_model

This model is a fine-tuned version of distilbert-base-uncased on the indian_names dataset. It achieves the following results on the evaluation set:

Loss: 0.0148
Precision: 0.9800
Recall: 0.9791
F1: 0.9796
Accuracy: 0.9963

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: 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
No log	1.0	66	0.1644	0.1903	0.1692	0.1791	0.8817
No log	2.0	132	0.1154	0.9760	0.8181	0.8901	0.9703
No log	3.0	198	0.0921	0.9755	0.9046	0.9387	0.9795
No log	4.0	264	0.0586	0.9616	0.9193	0.9400	0.9849
No log	5.0	330	0.0465	0.9588	0.9219	0.9400	0.9861
No log	6.0	396	0.0346	0.9359	0.9460	0.9409	0.9902
No log	7.0	462	0.0227	0.9708	0.9678	0.9693	0.9941
0.1068	8.0	528	0.0199	0.9753	0.9734	0.9743	0.9946
0.1068	9.0	594	0.0155	0.9801	0.9784	0.9793	0.9961
0.1068	10.0	660	0.0148	0.9800	0.9791	0.9796	0.9963

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3