|
--- |
|
language: |
|
- sk |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- wikiann |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
inference: false |
|
widget: |
|
- text: Zuzana Čaputová sa narodila 21. júna 1973 v Bratislave. |
|
example_title: Named Entity Recognition |
|
base_model: gerulata/slovakbert |
|
model-index: |
|
- name: slovakbert-ner |
|
results: |
|
- task: |
|
type: token-classification |
|
name: Token Classification |
|
dataset: |
|
name: wikiann |
|
type: wikiann |
|
args: sk |
|
metrics: |
|
- type: precision |
|
value: 0.9327115256495669 |
|
name: Precision |
|
- type: recall |
|
value: 0.9470124013528749 |
|
name: Recall |
|
- type: f1 |
|
value: 0.9398075632132469 |
|
name: F1 |
|
- type: accuracy |
|
value: 0.9785228256835333 |
|
name: Accuracy |
|
--- |
|
|
|
# Named Entity Recognition based on SlovakBERT |
|
|
|
This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the Slovak wikiann dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1600 |
|
- Precision: 0.9327 |
|
- Recall: 0.9470 |
|
- F1: 0.9398 |
|
- Accuracy: 0.9785 |
|
|
|
## Intended uses & limitations |
|
|
|
Supported classes: LOCATION, PERSON, ORGANIZATION |
|
|
|
``` |
|
from transformers import pipeline |
|
|
|
|
|
ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner') |
|
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké." |
|
classifications = ner_pipeline(input_sentence) |
|
``` |
|
|
|
with `displaCy`: |
|
|
|
``` |
|
import spacy |
|
from spacy import displacy |
|
|
|
|
|
ner_map = {0: '0', 1: 'B-OSOBA', 2: 'I-OSOBA', 3: 'B-ORGANIZÁCIA', 4: 'I-ORGANIZÁCIA', 5: 'B-LOKALITA', 6: 'I-LOKALITA'} |
|
|
|
entities = [] |
|
for i in range(len(classifications)): |
|
if classifications[i]['entity'] != 0: |
|
if ner_map[classifications[i]['entity']][0] == 'B': |
|
j = i + 1 |
|
while j < len(classifications) and ner_map[classifications[j]['entity']][0] == 'I': |
|
j += 1 |
|
entities.append((ner_map[classifications[i]['entity']].split('-')[1], classifications[i]['start'], |
|
classifications[j - 1]['end'])) |
|
|
|
nlp = spacy.blank("en") # it should work with any language |
|
|
|
doc = nlp(input_sentence) |
|
|
|
ents = [] |
|
for ee in entities: |
|
ents.append(doc.char_span(ee[1], ee[2], ee[0])) |
|
|
|
doc.ents = ents |
|
|
|
options = {"ents": ["OSOBA", "ORGANIZÁCIA", "LOKALITA"], |
|
"colors": {"OSOBA": "lightblue", "ORGANIZÁCIA": "lightcoral", "LOKALITA": "lightgreen"}} |
|
displacy_html = displacy.render(doc, style="ent", options=options) |
|
|
|
``` |
|
|
|
<div class="entities" style="line-height: 2.5; direction: ltr">Minister financií a líder mandátovo najsilnejšieho hnutia |
|
<mark class="entity" style="background: lightcoral; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
|
OĽaNO |
|
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORGANIZÁCIA</span> |
|
</mark> |
|
|
|
<mark class="entity" style="background: lightblue; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
|
Igor Matovič |
|
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">OSOBA</span> |
|
</mark> |
|
upozorňuje, že následky tretej vlny budú na |
|
<mark class="entity" style="background: lightgreen; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> |
|
Slovensku |
|
<span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOKALITA</span> |
|
</mark> |
|
veľmi veľké.</div> |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 15.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2342 | 1.0 | 625 | 0.1233 | 0.8891 | 0.9076 | 0.8982 | 0.9667 | |
|
| 0.1114 | 2.0 | 1250 | 0.1079 | 0.9118 | 0.9269 | 0.9193 | 0.9725 | |
|
| 0.0817 | 3.0 | 1875 | 0.1093 | 0.9173 | 0.9315 | 0.9243 | 0.9747 | |
|
| 0.0438 | 4.0 | 2500 | 0.1076 | 0.9188 | 0.9353 | 0.9270 | 0.9743 | |
|
| 0.028 | 5.0 | 3125 | 0.1230 | 0.9143 | 0.9387 | 0.9264 | 0.9744 | |
|
| 0.0256 | 6.0 | 3750 | 0.1204 | 0.9246 | 0.9423 | 0.9334 | 0.9765 | |
|
| 0.018 | 7.0 | 4375 | 0.1332 | 0.9292 | 0.9416 | 0.9353 | 0.9770 | |
|
| 0.0107 | 8.0 | 5000 | 0.1339 | 0.9280 | 0.9427 | 0.9353 | 0.9769 | |
|
| 0.0079 | 9.0 | 5625 | 0.1368 | 0.9326 | 0.9442 | 0.9383 | 0.9785 | |
|
| 0.0065 | 10.0 | 6250 | 0.1490 | 0.9284 | 0.9445 | 0.9364 | 0.9772 | |
|
| 0.0061 | 11.0 | 6875 | 0.1566 | 0.9328 | 0.9433 | 0.9380 | 0.9778 | |
|
| 0.0031 | 12.0 | 7500 | 0.1555 | 0.9339 | 0.9473 | 0.9406 | 0.9787 | |
|
| 0.0024 | 13.0 | 8125 | 0.1548 | 0.9349 | 0.9462 | 0.9405 | 0.9787 | |
|
| 0.0015 | 14.0 | 8750 | 0.1562 | 0.9330 | 0.9469 | 0.9399 | 0.9788 | |
|
| 0.0013 | 15.0 | 9375 | 0.1600 | 0.9327 | 0.9470 | 0.9398 | 0.9785 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.13.0.dev0 |
|
- Pytorch 1.10.0+cu113 |
|
- Datasets 1.15.1 |
|
- Tokenizers 0.10.3 |
|
|
