uvegesistvan
/

huBERTPlain_v2

@@ -1,199 +1,73 @@
 ---
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: cc-by-nc-4.0
+language:
+- hu
+metrics:
+- accuracy
+model-index:
+- name: huBERTPlain
+  results:
+  - task:
+      type: text-classification
+    metrics:
+      - type: accuracy
+        value: 0.74
+widget:
+- text: "Az egységes gyakorlati alkalmazás érdekében, illetve abból a célból, hogy a független kisüzemi termelői státuszt valamennyi tagállamban könnyebben elismerjék a Bizottság 2022. január 1-jével kezdődően uniós végrehajtási rendeletben határozta meg: egységes űrlap rendszeresítésével a tanúsítvány formáját, tartalmát és a kiállítására vonatkozó részlet szabályokat; a tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete szerinti szállításához szükséges adminisztratív okmányban, azaz az Adminisztratív kísérőokmányon (NAV_VP_IE815 jelű nyomtatvány) történő szerepeltetését; a tanúsítvány meghatározott adatainak 2008/118/EK irányelv V. fejezete szerinti szállításához szükséges adminisztratív okmányban, azaz az Egyszerűsített Kísérő Okmányon (NAV_VP_HU815e jelű nyomtatvány) történő szerepeltetését."
+  example_title: "Incomprehensible"
+- text: "Az AEO-engedély birtokosainak listáján – keresésre – megjelenő információk: az engedélyes neve, az engedélyt kibocsátó ország, az engedély típusa."
+  exmaple_title: "Comprehensible"
+---
+## Model description
+Cased fine-tuned BERT model for Hungarian, trained on a dataset provided by National Tax and Customs Administration - Hungary (NAV): Public Accessibilty Programme.
+Refined version of the huBERTPlain ('uvegesistvan/huBERTPlain') model.
+Trainig data cleaned further:
+	- Minor corrections in sentence segmentation results.
+	- Train data filtered: sentence pairs (original - rephrased) filtered out in each document, where two sentences' Levenstein distance was less then 3. These assumed to be spelling corrections, therefore not helping Plain Language classification.
+## Intended uses & limitations
+The model can be used as any other (cased) BERT model. It has been tested recognizing "accessible" and "original" sentences, where:
+* "accessible" - "Label_0": sentence, that can be considered as comprehensible (regarding to Plain Language directives)
+* "original" - "Label_1": sentence, that needs to rephrased in order to follow Plain Language Guidelines.
+## Training
+Fine-tuned version of the original huBERT model (`SZTAKI-HLT/hubert-base-cc`), trained on information materials provided by NAV linguistic experts.
+## Eval results
+| Class | Precision | Recall | F-Score |
+|-----|------------|------------|------|
+| **Accessible / Label_0** | **0.75** | **0.72** | **0.73**|
+| **Original / Label_1** | **0.74** | **0.77** | **0.75**|
+| **accuracy** | | | **0.74**|
+| **macro avg** | **0.74** | **0.74** | **0.74**|
+| **weighted avg** | **0.74** | **0.74** | **0.74**|
+## Usage
+```py
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/huBERTPlain")
+model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/huBERTPlain")
+```
+### BibTeX entry and citation info
+If you use the model, please cite the following dissertation (to be submitted for workshop discussion):
+Bibtex:
+```bibtex
+@PhDThesis{ Uveges:2024,
+  author = {{"U}veges, Istv{\'a}n},
+  title  = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
+  year   = {2024},
+  school = {Szegedi Tudom{\'a}nyegyetem}
+}
+```