Analyze Ancient Greek text for syntax and named entities
ModernBERT for reasoning and zero-shot classification
Classify Turkish text into predefined categories
Demo emotion detection
Analyze content to detect triggers
Embedding Leaderboard
Explore BERT model interactions
Submit model predictions and view leaderboard results
Generate relation triplets from text
Display and filter LLM benchmark results
Easily visualize tokens for any diffusion model.
Extract relationships and entities from text
fake news detection using distilbert trained on liar dataset
Ancient_Greek_Spacy_Models is a specialized natural language processing (NLP) tool designed to analyze Ancient Greek text. Built on the spaCy framework, it provides advanced capabilities for syntax parsing and named entity recognition in Ancient Greek texts. This model is particularly useful for scholars, researchers, and anyone working with Ancient Greek literature or historical documents.
• Syntax Parsing: Analyzes sentence structure and grammatical relationships. • Named Entity Recognition: Identifies and categorizes named entities (e.g., people, places, organizations) in Ancient Greek texts. • Tokenization: Accurately splits text into individual words and tokens. • Part-of-Speech Tagging: Assigns grammatical categories to each word (e.g., noun, verb, adjective). • Dependency Parsing: Visualizes sentence structure using dependency grammar. • Lemmatization: Reduces words to their base or root forms.
pip install spacy.python -m spacy download [model_name].import spacy
nlp = spacy.load("[model_name]")
text = "ΑνtokenId javelινxcaesar(example)"
doc = nlp(text)
# Perform tasks like entity recognition, POS tagging, etc.
1. What is the primary purpose of Ancient_Greek_Spacy_Models?
It is designed to analyze Ancient Greek text for syntax and named entities, making it a valuable tool for scholarly research.
2. Does this model support Modern Greek?
No, it is specifically trained for Ancient Greek texts and may not perform well on Modern Greek.
3. How do I visualize the syntax analysis?
You can use spaCy's built-in visualization tools or libraries like spacy_displacy to visualize the dependency parses and entity recognitions.