metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: distilbert-base-uncased
model-index:
  - name: distilbert-base-uncased_ner_wnut_17
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wnut_17
          type: wnut_17
          args: wnut_17
        metrics:
          - type: precision
            value: 0.6700879765395894
            name: Precision
          - type: recall
            value: 0.5466507177033493
            name: Recall
          - type: f1
            value: 0.6021080368906456
            name: F1
          - type: accuracy
            value: 0.9559412550066756
            name: Accuracy

distilbert-base-uncased_ner_wnut_17

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: 0.2400
Precision: 0.6701
Recall: 0.5467
F1: 0.6021
Accuracy: 0.9559

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2367	0.6879	0.4270	0.5269	0.9455
No log	2.0	426	0.2272	0.6913	0.4928	0.5754	0.9533
0.173	3.0	639	0.2393	0.6788	0.5132	0.5845	0.9553
0.173	4.0	852	0.2338	0.6541	0.5610	0.6040	0.9557
0.0489	5.0	1065	0.2400	0.6701	0.5467	0.6021	0.9559

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1