metadata

tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: twitter-roberta-base-WNUT
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.7045454545454546
          - name: Recall
            type: recall
            value: 0.6303827751196173
          - name: F1
            type: f1
            value: 0.6654040404040403
          - name: Accuracy
            type: accuracy
            value: 0.9639611008707811

twitter-roberta-base-WNUT

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.1938
Precision: 0.7045
Recall: 0.6304
F1: 0.6654
Accuracy: 0.9640

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: 64
eval_batch_size: 1024
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	0.46	25	0.3912	0.0	0.0	0.0	0.9205
No log	0.93	50	0.2847	0.25	0.0024	0.0047	0.9209
No log	1.39	75	0.2449	0.5451	0.3469	0.4240	0.9426
No log	1.85	100	0.1946	0.6517	0.4856	0.5565	0.9492
No log	2.31	125	0.1851	0.6921	0.5646	0.6219	0.9581
No log	2.78	150	0.1672	0.6867	0.5873	0.6331	0.9594
No log	3.24	175	0.1675	0.6787	0.5837	0.6277	0.9615
No log	3.7	200	0.1644	0.6765	0.6328	0.6539	0.9638
No log	4.17	225	0.1672	0.6997	0.6495	0.6737	0.9640
No log	4.63	250	0.1652	0.6915	0.6435	0.6667	0.9649
No log	5.09	275	0.1882	0.7067	0.6053	0.6521	0.9629
No log	5.56	300	0.1783	0.7128	0.6352	0.6717	0.9645
No log	6.02	325	0.1813	0.7011	0.6172	0.6565	0.9639
No log	6.48	350	0.1804	0.7139	0.6447	0.6776	0.9647
No log	6.94	375	0.1902	0.7218	0.6268	0.6709	0.9641
No log	7.41	400	0.1883	0.7106	0.6316	0.6688	0.9641
No log	7.87	425	0.1862	0.7067	0.6340	0.6683	0.9643
No log	8.33	450	0.1882	0.7053	0.6328	0.6671	0.9639
No log	8.8	475	0.1919	0.7055	0.6304	0.6658	0.9638
0.1175	9.26	500	0.1938	0.7045	0.6304	0.6654	0.9640
0.1175	9.72	525	0.1880	0.7025	0.6411	0.6704	0.9646

Framework versions

Transformers 4.20.1
Pytorch 1.12.0
Datasets 2.3.2
Tokenizers 0.12.1