metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-tiny-finetuned-wnut17-ner
    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
          - name: Recall
            type: recall
            value: 0
          - name: F1
            type: f1
            value: 0
          - name: Accuracy
            type: accuracy
            value: 0.8960890010322284

bert-tiny-finetuned-wnut17-ner

This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.6054
Precision: 0.0
Recall: 0.0
F1: 0.0
Accuracy: 0.8961

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: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	27	1.1060	0.8961
No log	2.0	54	0.9075	0.8961
No log	3.0	81	0.7978	0.8961
No log	4.0	108	0.7333	0.8961
No log	5.0	135	0.6929	0.8961
No log	6.0	162	0.6661	0.8961
No log	7.0	189	0.6477	0.8961
No log	8.0	216	0.6346	0.8961
No log	9.0	243	0.6251	0.8961
No log	10.0	270	0.6182	0.8961
No log	11.0	297	0.6132	0.8961
No log	12.0	324	0.6097	0.8961
No log	13.0	351	0.6073	0.8961
No log	14.0	378	0.6059	0.8961
No log	15.0	405	0.6054	0.8961

Framework versions

Transformers 4.21.0
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1