metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: bert-base-uncased
model-index:
  - name: bert-base-uncased-tajik-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wikiann
          type: wikiann
          config: tg
          split: train+test
          args: tg
        metrics:
          - type: precision
            value: 0.5042016806722689
            name: Precision
          - type: recall
            value: 0.5769230769230769
            name: Recall
          - type: f1
            value: 0.5381165919282511
            name: F1
          - type: accuracy
            value: 0.848129958443521
            name: Accuracy

bert-base-uncased-tajik-ner

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

Loss: 1.2137
Precision: 0.5042
Recall: 0.5769
F1: 0.5381
Accuracy: 0.8481

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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	50	0.9499	0.0450	0.0962	0.0613	0.6626
No log	4.0	100	0.7348	0.1549	0.2115	0.1789	0.7401
No log	6.0	150	0.6685	0.1916	0.3077	0.2362	0.8017
No log	8.0	200	0.7875	0.3923	0.4904	0.4359	0.8036
No log	10.0	250	0.7495	0.4225	0.5769	0.4878	0.8274
No log	12.0	300	0.8934	0.4198	0.5288	0.4681	0.8085
No log	14.0	350	0.9455	0.4758	0.5673	0.5175	0.8236
No log	16.0	400	0.9469	0.5893	0.6346	0.6111	0.8410
No log	18.0	450	0.9936	0.5333	0.6154	0.5714	0.8485
0.2726	20.0	500	0.9804	0.5	0.6058	0.5478	0.8519
0.2726	22.0	550	1.1035	0.5963	0.625	0.6103	0.8432
0.2726	24.0	600	1.0318	0.5856	0.625	0.6047	0.8576
0.2726	26.0	650	1.1820	0.4921	0.5962	0.5391	0.8221
0.2726	28.0	700	1.1204	0.4878	0.5769	0.5286	0.8311
0.2726	30.0	750	1.1911	0.5357	0.5769	0.5556	0.8376
0.2726	32.0	800	1.1747	0.5259	0.5865	0.5545	0.8394
0.2726	34.0	850	1.1403	0.5872	0.6154	0.6009	0.8542
0.2726	36.0	900	1.1824	0.5370	0.5577	0.5472	0.8330
0.2726	38.0	950	1.1467	0.5424	0.6154	0.5766	0.8440
0.003	40.0	1000	1.2148	0.5268	0.5673	0.5463	0.8360
0.003	42.0	1050	1.3478	0.5273	0.5577	0.5421	0.8266
0.003	44.0	1100	1.2137	0.5042	0.5769	0.5381	0.8481

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1