đ small-e-czech-finetuned-ner-wikiann
This model is a fine - tuned version of [Seznam/small - e - czech](https://huggingface.co/Seznam/small - e - czech) on the wikiann dataset, achieving high performance in token classification tasks.
đ Quick Start
This model is a fine - tuned version of [Seznam/small - e - czech](https://huggingface.co/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
đ Documentation
Model Information
Property |
Details |
Model Type |
small - e - czech - finetuned - ner - wikiann |
Training Datasets |
wikiann |
Metrics |
Precision, Recall, F1, Accuracy |
Model Results
Task |
Dataset |
Metrics |
Value |
Token Classification |
wikiann (cs) |
Precision |
0.8713322894683097 |
Token Classification |
wikiann (cs) |
Recall |
0.8970423324922905 |
Token Classification |
wikiann (cs) |
F1 |
0.8840004144075699 |
Token Classification |
wikiann (cs) |
Accuracy |
0.9557089381093997 |
đ§ Technical Details
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
đ License
This model is released under the CC - BY - 4.0 license.