updated readme

README.md

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 license: mit
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+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:
+
+```bash
+!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`.
+
+```python
+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"])
+```