placingholocaust/gliner_small-v2.1-holocaust

This is the Placing the Holocaust's finetuned GliNER small model. GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.

Links

• Original GliNER model: https://huggingface.co/urchade/gliner_small-v2.1
• Finetuned with this data: https://huggingface.co/datasets/placingholocaust/spacy-project
• GliNER paper: https://arxiv.org/abs/2311.08526
• GliNER Repository: https://github.com/urchade/GLiNER

Labels

Category	Definition	Examples
building	Includes references to physical structures and places of labor or employment like factories. Institutions such as the "Judenrat" or "Red Cross" are also included.	school, home, house, hospital, factory, station, office, store, synagogue, barracks
country	Mostly country names, also includes "earth," "country," and "world." Distinguished from Region and Environmental feature based on context.	germany, poland, states, israel, united, country, america, england, france, russia
dlf (distinct landscape feature)	Places not large enough to be a geographic or populated region but too large to be an Object, includes parts of buildings like "roof" or "chimney."	street, door, border, line, farm, window, streets, road, wall, field
env feature (environmental feature)	Any named or unnamed environmental feature, including bodies of water and landforms. General references like "nature" and "water" are included.	woods, forest, river, mountains, ground, trees, water, tree, mountain, sea
interior space	References to distinct rooms within a building, or large place features of a building like a "factory floor."	room, apartment, floor, kitchen, rooms, gas, basement, bathroom, chambers, bunker
imaginary	Difficult terms that are context-dependent like "inside," "outside," or "side." Also includes unspecified locations like "community," and conceptual places like "hell" or "heaven."	place, outside, places, side, inside, hiding, hell, heaven, part, spot
populated place	Includes cities, towns, villages, and hamlets or crossroads settlements. Names of places can be the same as a ghetto, camp, city, or district.	camp, ghetto, town, city, auschwitz, camps, new, york, concentration, village
region	Sub-national regions, states, provinces, or islands. Includes references to sides of a geopolitical border or military zone.	area, side, land, siberia, new, zone, jersey, california, russian, eastern
spatial object	Objects of conveyance and movable objects like furniture. In specific contexts, refers to transportation vehicles or items like "ovens," where the common use case of the term prevails.	train, car, ship, boat, bed, truck, trains, cars, trucks

Installation

To use this model, you must install the GLiNER Python library:

!pip install gliner

Usage

Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using GLiNER.from_pretrained and predict entities with predict_entities.

from gliner import GLiNER

model = GLiNER.from_pretrained("placingholocaust/gliner_small-v2.1-holocaust")

text = """
Okay. So now it's spring of '44? A: ‘4, And she says, You're going to go to Brzezinka. I said, What is Brzezinka? She said, It's a crematorium and the gas chamber. They have a half a million Hungarian Jews are coming in. That's when the time they -- and they need people to select. We do not select the people to -- who die or not. The women fold the clothes and look for jewelry and make packages to send it to Germany.
"""

labels = ["dlf", "populated place", "country", "region", "interior space", "env feature", "building", "spatial object"]

entities = model.predict_entities(text, labels)

for entity in entities:
    print(entity["text"], "=>", entity["label"])