README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.6603139013452914
          - name: Recall
            type: recall
            value: 0.4682034976152623
          - name: F1
            type: f1
            value: 0.547906976744186
          - name: Accuracy
            type: accuracy
            value: 0.9355430668654662

Cybonto-distilbert-base-uncased-finetuned-ner-Wnut17

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

• Loss: 0.5062
• Precision: 0.6603
• Recall: 0.4682
• F1: 0.5479
• Accuracy: 0.9355

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.3396	0.6470	0.4269	0.5144	0.9330
No log	2.0	214	0.3475	0.5948	0.4539	0.5149	0.9335
No log	3.0	321	0.3793	0.6613	0.4253	0.5177	0.9332
No log	4.0	428	0.3598	0.6195	0.4944	0.5500	0.9354
0.0409	5.0	535	0.3702	0.5802	0.4571	0.5113	0.9308
0.0409	6.0	642	0.4192	0.6546	0.4459	0.5305	0.9344
0.0409	7.0	749	0.4039	0.6360	0.4610	0.5346	0.9354
0.0409	8.0	856	0.4104	0.6564	0.4587	0.5400	0.9353
0.0409	9.0	963	0.3839	0.6283	0.4944	0.5534	0.9361
0.0132	10.0	1070	0.4331	0.6197	0.4547	0.5245	0.9339
0.0132	11.0	1177	0.4152	0.6196	0.4817	0.5420	0.9355
0.0132	12.0	1284	0.4654	0.6923	0.4507	0.5460	0.9353
0.0132	13.0	1391	0.4869	0.6739	0.4436	0.5350	0.9350
0.0132	14.0	1498	0.4297	0.6424	0.4769	0.5474	0.9353
0.0061	15.0	1605	0.4507	0.6272	0.4626	0.5325	0.9340
0.0061	16.0	1712	0.4410	0.6066	0.4793	0.5355	0.9335
0.0061	17.0	1819	0.4851	0.6639	0.4523	0.5381	0.9351
0.0061	18.0	1926	0.4815	0.6553	0.4563	0.5380	0.9346
0.0035	19.0	2033	0.5188	0.6780	0.4420	0.5351	0.9350
0.0035	20.0	2140	0.4986	0.6770	0.4698	0.5547	0.9363
0.0035	21.0	2247	0.4834	0.6552	0.4714	0.5483	0.9355
0.0035	22.0	2354	0.5094	0.6784	0.4595	0.5479	0.9358
0.0035	23.0	2461	0.4954	0.6583	0.4579	0.5401	0.9354
0.0026	24.0	2568	0.5035	0.6667	0.4595	0.5440	0.9354
0.0026	25.0	2675	0.5000	0.6599	0.4658	0.5461	0.9355
0.0026	26.0	2782	0.4968	0.6697	0.4738	0.5549	0.9357
0.0026	27.0	2889	0.4991	0.6545	0.4714	0.5481	0.9352
0.0026	28.0	2996	0.4936	0.6508	0.4769	0.5505	0.9353
0.0021	29.0	3103	0.5005	0.6535	0.4722	0.5482	0.9353
0.0021	30.0	3210	0.5062	0.6603	0.4682	0.5479	0.9355

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.1.0
• Tokenizers 0.12.1