metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - token_classification_v2
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: favs_token_classification_v2_uncased
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: token_classification_v2
          type: token_classification_v2
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.6598639455782312
          - name: Recall
            type: recall
            value: 0.782258064516129
          - name: F1
            type: f1
            value: 0.7158671586715867
          - name: Accuracy
            type: accuracy
            value: 0.8546511627906976

favs_token_classification_v2_uncased

This model is a fine-tuned version of bert-base-uncased on the token_classification_v2 dataset. It achieves the following results on the evaluation set:

Loss: 0.5006
Precision: 0.6599
Recall: 0.7823
F1: 0.7159
Accuracy: 0.8547

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
2.2447	1.0	13	1.9089	0.2	0.0968	0.1304	0.3576
1.9589	2.0	26	1.5848	0.2734	0.2823	0.2778	0.4477
1.729	3.0	39	1.3636	0.3128	0.4516	0.3696	0.6134
1.4278	4.0	52	1.1854	0.4302	0.5968	0.5	0.7122
1.3046	5.0	65	1.0341	0.5183	0.6855	0.5903	0.7413
1.1599	6.0	78	0.9163	0.5188	0.6694	0.5845	0.75
0.9263	7.0	91	0.8235	0.5399	0.7097	0.6132	0.7645
0.8721	8.0	104	0.7627	0.5176	0.7097	0.5986	0.7733
0.7879	9.0	117	0.7070	0.5366	0.7097	0.6111	0.7849
0.6881	10.0	130	0.6575	0.5427	0.7177	0.6181	0.7936
0.6414	11.0	143	0.6076	0.5660	0.7258	0.6360	0.8110
0.6096	12.0	156	0.5804	0.6090	0.7661	0.6786	0.8285
0.5812	13.0	169	0.5661	0.6282	0.7903	0.7000	0.8343
0.5006	14.0	182	0.5503	0.6144	0.7581	0.6787	0.8285
0.5289	15.0	195	0.5366	0.6267	0.7581	0.6861	0.8372
0.4447	16.0	208	0.5222	0.6419	0.7661	0.6985	0.8459
0.435	17.0	221	0.5120	0.6599	0.7823	0.7159	0.8517
0.4454	18.0	234	0.5058	0.6667	0.7903	0.7232	0.8547
0.422	19.0	247	0.5013	0.6599	0.7823	0.7159	0.8547
0.4285	20.0	260	0.5006	0.6599	0.7823	0.7159	0.8547

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1