README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-small-finetuned-xglue-ner-longer50
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          config: wnut_17
          split: train
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.6182136602451839
          - name: Recall
            type: recall
            value: 0.4222488038277512
          - name: F1
            type: f1
            value: 0.5017768301350392
          - name: Accuracy
            type: accuracy
            value: 0.9252207821997935

bert-small-finetuned-xglue-ner-longer50

This model is a fine-tuned version of muhtasham/bert-small-finetuned-xglue-ner-longer20 on the wnut_17 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7236
• Precision: 0.6182
• Recall: 0.4222
• F1: 0.5018
• Accuracy: 0.9252

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: 8
• eval_batch_size: 8
• 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	425	0.5693	0.5232	0.4581	0.4885	0.9268
0.0032	2.0	850	0.6191	0.5281	0.4498	0.4858	0.9260
0.0035	3.0	1275	0.7045	0.6011	0.4055	0.4843	0.9241
0.0056	4.0	1700	0.6715	0.5571	0.4438	0.4940	0.9261
0.004	5.0	2125	0.6537	0.5645	0.4294	0.4878	0.9256
0.0063	6.0	2550	0.6646	0.5659	0.4211	0.4829	0.9255
0.0063	7.0	2975	0.6269	0.5306	0.4354	0.4783	0.9238
0.003	8.0	3400	0.7235	0.5921	0.3959	0.4746	0.9238
0.0051	9.0	3825	0.6334	0.5330	0.4450	0.4850	0.9237
0.0047	10.0	4250	0.6408	0.5893	0.4462	0.5078	0.9271
0.004	11.0	4675	0.6721	0.5840	0.4282	0.4941	0.9255
0.0051	12.0	5100	0.6853	0.5795	0.4318	0.4949	0.9258
0.0038	13.0	5525	0.6870	0.5789	0.4211	0.4875	0.9249
0.0038	14.0	5950	0.6931	0.6032	0.4091	0.4875	0.9241
0.0033	15.0	6375	0.6502	0.5965	0.4510	0.5136	0.9266
0.0032	16.0	6800	0.6941	0.6126	0.4426	0.5139	0.9267
0.0042	17.0	7225	0.6603	0.5856	0.4462	0.5064	0.9266
0.0016	18.0	7650	0.6870	0.6121	0.4474	0.5169	0.9273
0.0028	19.0	8075	0.6922	0.5906	0.4366	0.5021	0.9250
0.0023	20.0	8500	0.7096	0.6089	0.4246	0.5004	0.9250
0.0023	21.0	8925	0.6763	0.5772	0.4426	0.5010	0.9261
0.0025	22.0	9350	0.6880	0.5696	0.4258	0.4873	0.9241
0.0018	23.0	9775	0.6759	0.5836	0.4426	0.5034	0.9259
0.0017	24.0	10200	0.7044	0.6198	0.4270	0.5057	0.9262
0.0018	25.0	10625	0.6948	0.6040	0.4306	0.5028	0.9245
0.0018	26.0	11050	0.6930	0.5948	0.4354	0.5028	0.9255
0.0018	27.0	11475	0.7077	0.6048	0.4246	0.4989	0.9250
0.0023	28.0	11900	0.7127	0.6103	0.4270	0.5025	0.9252
0.0013	29.0	12325	0.7253	0.6243	0.4234	0.5046	0.9254
0.0015	30.0	12750	0.7236	0.6182	0.4222	0.5018	0.9252

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1