README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - favsbot
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-cased-NER-favsbot
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: favsbot
          type: favsbot
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.8571428571428571
          - name: Recall
            type: recall
            value: 0.96
          - name: F1
            type: f1
            value: 0.9056603773584904
          - name: Accuracy
            type: accuracy
            value: 0.9583333333333334

bert-base-cased-NER-favsbot

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

• Loss: 0.0992
• Precision: 0.8571
• Recall: 0.96
• F1: 0.9057
• Accuracy: 0.9583

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: 1.5e-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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	10	1.7643	0.0	0.0	0.0	0.5694
No log	2.0	20	1.1420	0.0	0.0	0.0	0.5833
No log	3.0	30	0.7946	0.9375	0.6	0.7317	0.8056
No log	4.0	40	0.5625	0.8182	0.72	0.7660	0.8611
No log	5.0	50	0.4217	0.8148	0.88	0.8462	0.9306
No log	6.0	60	0.3082	0.8519	0.92	0.8846	0.9444
No log	7.0	70	0.2386	0.8148	0.88	0.8462	0.9444
No log	8.0	80	0.1965	0.8148	0.88	0.8462	0.9444
No log	9.0	90	0.1626	0.8148	0.88	0.8462	0.9444
No log	10.0	100	0.1465	0.8571	0.96	0.9057	0.9583
No log	11.0	110	0.1314	0.8571	0.96	0.9057	0.9583
No log	12.0	120	0.1215	0.8571	0.96	0.9057	0.9583
No log	13.0	130	0.1160	0.8571	0.96	0.9057	0.9583
No log	14.0	140	0.1104	0.8571	0.96	0.9057	0.9583
No log	15.0	150	0.1050	0.8571	0.96	0.9057	0.9583
No log	16.0	160	0.1012	0.8571	0.96	0.9057	0.9583
No log	17.0	170	0.0997	0.8571	0.96	0.9057	0.9583
No log	18.0	180	0.0997	0.8571	0.96	0.9057	0.9583
No log	19.0	190	0.0995	0.8571	0.96	0.9057	0.9583
No log	20.0	200	0.0992	0.8571	0.96	0.9057	0.9583

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1
• Datasets 2.6.1
• Tokenizers 0.12.1