librarian-bot's picture
Librarian Bot: Add base_model information to model
d6b8845
|
raw
history blame
2.2 kB
metadata
license: gpl-3.0
tags:
  - generated_from_trainer
datasets:
  - mim_gold_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: vesteinn/IceBERT
model-index:
  - name: IceBERT-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: mim_gold_ner
          type: mim_gold_ner
          args: mim-gold-ner
        metrics:
          - type: precision
            value: 0.8920083733530353
            name: Precision
          - type: recall
            value: 0.8655753375552635
            name: Recall
          - type: f1
            value: 0.8785930867192238
            name: F1
          - type: accuracy
            value: 0.9855436530476731
            name: Accuracy

IceBERT-finetuned-ner

This model is a fine-tuned version of vesteinn/IceBERT on the mim_gold_ner dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0772
  • Precision: 0.8920
  • Recall: 0.8656
  • F1: 0.8786
  • Accuracy: 0.9855

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: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0519 1.0 2904 0.0731 0.8700 0.8564 0.8631 0.9832
0.026 2.0 5808 0.0749 0.8771 0.8540 0.8654 0.9840
0.0159 3.0 8712 0.0772 0.8920 0.8656 0.8786 0.9855

Framework versions

  • Transformers 4.11.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.12.1
  • Tokenizers 0.10.3