README.md

README.md

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+# iPBL – Literary Genre Classification (HerBERT)
+
+## Overview
+
+This model implements the **literary genre classification** component of the iPBL (Bibliography of Polish Digital Culture) system developed at the Institute of Literary Research of the Polish Academy of Sciences.
+
+It assigns domain-specific literary form categories to Polish web-based cultural texts.  
+The model supports structured bibliographic description within a historically established classificatory framework derived from the Polish Literary Bibliography (PBL).
+
+Unlike general-purpose genre classifiers, this model operates within a discipline-specific bibliographic regime.
+
+---
+
+## Task
+
+Single-label multi-class text classification.
+
+Each document is assigned one dominant literary genre category.
+
+### Genres
+
+- artykuł  
+- esej  
+- felieton  
+- kult  
+- nota  
+- opowiadanie  
+- proza  
+- recenzja  
+- wiersz  
+- wpis blogowy  
+- wspomnienie  
+- wywiad  
+- zgon  
+
+---
+
+## Base Model
+
+`allegro/herbert-base-cased`
+
+Architecture: `BertForSequenceClassification`
+
+---
+
+## Training Data
+
+The model was trained on curated bibliographic records produced in everyday bibliographic practice within the iPBL project.
+
+Raw samples: 17,731  
+Final samples (after frequency filtering ≥ 100): 17,486  
+
+Data split:
+
+- 70% Training  
+- 10% Validation  
+- 20% Test  
+
+The dataset reflects real-world class imbalance typical of web-native literary discourse.
+
+---
+
+## Performance (Test Set)
+
+- Accuracy: **85.13%**
+- Weighted F1-score: **0.85**
+
+### High-performing genres
+
+| Genre      | F1-score |
+|------------|----------|
+| wiersz     | 0.94 |
+| wywiad     | 0.94 |
+| recenzja   | 0.92 |
+| artykuł    | 0.85 |
+
+Lower performance is observed for structurally hybrid and low-resource genres (e.g., esej, nota).
+
+---
+
+## How to Use
+
+### Standard Transformers Usage
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="darekpe79/Literary_Genre_Classification",
+    tokenizer="darekpe79/Literary_Genre_Classification"
+)
+
+text = "Przykładowy tekst artykułu literackiego..."
+classifier(text)