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--- |
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tags: |
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- token-classification |
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language: |
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- fi |
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widget: |
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- text: Asun Brysselissä, Euroopan pääkaupungissa. |
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datasets: |
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- drvenabili/autotrain-data-turku-ner |
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- turku_ner_corpus |
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co2_eq_emissions: |
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emissions: 0.2165403288824756 |
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license: apache-2.0 |
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pipeline_tag: token-classification |
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--- |
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# Info |
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This is a fine-tuned model on the NER task. The original model is Turku NLP's [bert-base-finnish-uncased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1), and the fine-tuning dataset is Turku NLP's [turku_ner_corpus](https://huggingface.co/datasets/turku_ner_corpus/). |
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The model is released under Apache 2.0. |
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Please mention the training dataset if you use this model: |
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```bibtex |
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@inproceedings{luoma-etal-2020-broad, |
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title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition", |
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author = {Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo}, |
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booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference", |
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year = "2020", |
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url = "https://www.aclweb.org/anthology/2020.lrec-1.567", |
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pages = "4615--4624", |
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} |
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``` |
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# Validation Metrics |
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- Loss: 0.075 |
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- Accuracy: 0.982 |
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- Precision: 0.879 |
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- Recall: 0.868 |
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- F1: 0.873 |
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# Test Metrics |
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### Overall Metrics |
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- Accuracy: 0.986 |
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- Precision: 0.857 |
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- Recall: 0.872 |
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- F1: 0.864 |
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### Per-entity metrics |
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```json |
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{ |
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"DATE": { |
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"precision": 0.925, |
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"recall": 0.9736842105263158, |
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"f1": 0.9487179487179489, |
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"number": "114" |
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}, |
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"EVENT": { |
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"precision": 0.3, |
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"recall": 0.42857142857142855, |
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"f1": 0.3529411764705882, |
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"number": "7" |
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}, |
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"LOC": { |
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"precision": 0.9057239057239057, |
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"recall": 0.9372822299651568, |
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"f1": 0.9212328767123287, |
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"number": "287" |
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}, |
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"ORG": { |
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"precision": 0.8274111675126904, |
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"recall": 0.7836538461538461, |
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"f1": 0.8049382716049382, |
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"number": "208" |
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}, |
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"PER": { |
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"precision": 0.88, |
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"recall": 0.9225806451612903, |
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"f1": 0.9007874015748031, |
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"number": "310" |
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}, |
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"PRO": { |
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"precision": 0.6081081081081081, |
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"recall": 0.569620253164557, |
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"f1": 0.5882352941176471, |
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"number": "79" |
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} |
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} |
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``` |
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## Usage |
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You can use cURL to access this model: |
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``` |
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$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "Asun Brysselissä, Euroopan pääkaupungissa."}' https://api-inference.huggingface.co/models/iguanodon-ai/bert-base-finnish-uncased-ner |
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``` |
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Or Python API: |
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``` |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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model = AutoModelForTokenClassification.from_pretrained("iguanodon-ai/bert-base-finnish-uncased-ner") |
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tokenizer = AutoTokenizer.from_pretrained("iguanodon-ai/bert-base-finnish-uncased-ner") |
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inputs = tokenizer("Asun Brysselissä, Euroopan pääkaupungissa.", return_tensors="pt") |
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outputs = model(**inputs) |
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``` |
