README.md

metadata

license: cc-by-4.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: small-e-czech-finetuned-ner-wikiann
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          args: cs
        metrics:
          - name: Precision
            type: precision
            value: 0.8713322894683097
          - name: Recall
            type: recall
            value: 0.8970423324922905
          - name: F1
            type: f1
            value: 0.8840004144075699
          - name: Accuracy
            type: accuracy
            value: 0.9557089381093997

small-e-czech-finetuned-ner-wikiann

This model is a fine-tuned version of Seznam/small-e-czech on the wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.2547
• Precision: 0.8713
• Recall: 0.8970
• F1: 0.8840
• Accuracy: 0.9557

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2924	1.0	2500	0.2449	0.7686	0.8088	0.7882	0.9320
0.2042	2.0	5000	0.2137	0.8050	0.8398	0.8220	0.9400
0.1699	3.0	7500	0.1912	0.8236	0.8593	0.8411	0.9466
0.1419	4.0	10000	0.1931	0.8349	0.8671	0.8507	0.9488
0.1316	5.0	12500	0.1892	0.8470	0.8776	0.8620	0.9519
0.1042	6.0	15000	0.2058	0.8433	0.8811	0.8618	0.9508
0.0884	7.0	17500	0.2020	0.8602	0.8849	0.8724	0.9531
0.0902	8.0	20000	0.2118	0.8551	0.8837	0.8692	0.9528
0.0669	9.0	22500	0.2171	0.8634	0.8906	0.8768	0.9550
0.0529	10.0	25000	0.2228	0.8638	0.8912	0.8773	0.9545
0.0613	11.0	27500	0.2293	0.8626	0.8898	0.8760	0.9544
0.0549	12.0	30000	0.2276	0.8694	0.8958	0.8824	0.9554
0.0516	13.0	32500	0.2384	0.8717	0.8940	0.8827	0.9552
0.0412	14.0	35000	0.2443	0.8701	0.8931	0.8815	0.9554
0.0345	15.0	37500	0.2464	0.8723	0.8958	0.8839	0.9557
0.0412	16.0	40000	0.2477	0.8705	0.8948	0.8825	0.9552
0.0363	17.0	42500	0.2525	0.8742	0.8973	0.8856	0.9559
0.0341	18.0	45000	0.2529	0.8727	0.8962	0.8843	0.9561
0.0194	19.0	47500	0.2533	0.8699	0.8966	0.8830	0.9557
0.0247	20.0	50000	0.2547	0.8713	0.8970	0.8840	0.9557

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 1.18.4
• Tokenizers 0.11.6