README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          args: en
        metrics:
          - name: Precision
            type: precision
            value: 0.819622641509434
          - name: Recall
            type: recall
            value: 0.8444790046656299
          - name: F1
            type: f1
            value: 0.8318651857525853
          - name: Accuracy
            type: accuracy
            value: 0.9269227060339613
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wikiann
          type: wikiann
          config: en
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8492771401033908
            verified: true
          - name: Precision
            type: precision
            value: 0.857294905524994
            verified: true
          - name: Recall
            type: recall
            value: 0.865900059186607
            verified: true
          - name: F1
            type: f1
            value: 0.8615759964905745
            verified: true
          - name: loss
            type: loss
            value: 1.054654836654663
            verified: true

bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.3217
• Precision: 0.8196
• Recall: 0.8445
• F1: 0.8319
• Accuracy: 0.9269

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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2821	1.0	2500	0.2906	0.7983	0.8227	0.8103	0.9193
0.2087	2.0	5000	0.2614	0.8030	0.8379	0.8201	0.9257
0.1404	3.0	7500	0.3217	0.8196	0.8445	0.8319	0.9269

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1