metadata

library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: my_first_ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          config: wnut_17
          split: test
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.44532803180914515
          - name: Recall
            type: recall
            value: 0.20759962928637626
          - name: F1
            type: f1
            value: 0.28318584070796454
          - name: Accuracy
            type: accuracy
            value: 0.9365140438630243

my_first_ner

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

Loss: 0.2982
Precision: 0.4453
Recall: 0.2076
F1: 0.2832
Accuracy: 0.9365

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.3151	0.3618	0.0825	0.1343	0.9310
No log	2.0	214	0.2982	0.4453	0.2076	0.2832	0.9365

Framework versions

Transformers 4.47.1
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0