placingholocaust/spacy-project

📚 Placing the Holocaust Weasel (spacy) Project

This is the official spaCy project for the Placing the Holocaust Project. This project houses our data and our Python scripts for converting data, serializing it, training 4 different spaCy models with it, and evaluating those models. It also contains all the metrics from v. 0.0.1.

For this project, we are using spaCy v. 3.7.4.

Project Overview

Studying experiences of the Holocaust should not be limited to what happened in identifiable, or familiar, named places such as camps or ghettos, cities or villages. Many of the most important events of the Holocaust occurred in unnamed places. For most, physical and temporal disorientation were a real part of what it meant to be a victim of Nazi violence. Our approach to analyzing testimony transcripts recognizes the importance of the unnamed street corner, fence, farm, or hill in both survivor testimonies and conceptualizations of Holocaust landscapes generally.

As a part of the University of Maine’s Placing the Holocaust project, we created a taxonomy of nine place categories to capture this wide array of both unnamed and named places. We were able to train a model to annotate 977 post-war testimony transcripts from the United States Holocaust Memorial Museum (USHMM). The final outcome of the project includes creating an open access site with both a search engine of the transcripts and a mapping tool (forthcoming summer 2024). In releasing our data, we hope that others can build from our methodology to implement their own place-based approach to analyzing their corpus, Holocaust-related or not, and develop their own methods to analyzing testimony transcripts. Please share your work with us!

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, CONTINENT, OR LARGER	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
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
IMAGINARY OR OTHER	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
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
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
OBJECTS	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
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

📋 project.yml

The project.yml defines the data assets required by the project, as well as the available commands and workflows. For details, see the Weasel documentation.

⏯ Commands

The following commands are defined by the project. They can be executed using weasel run [name]. Commands are only re-run if their inputs have changed.

Command	Description
download-lg	Download a large spaCy model with pretrained vectors
download-md	Download a medium spaCy model with pretrained vectors
convert	Convert the data to spaCy's binary format
convert-sents	Convert the data to sentences before converting to spaCy's binary format
split	Split data into train/dev/test sets
create-config-sm	Create a new config with a spancat pipeline component for small models
train-sm	Train the spancat model with a small configuration
train-md	Train the spancat model with a medium configuration
train-lg	Train the spancat model with a large configuration
train-trf	Train the spancat model with a transformer configuration
evaluate-sm	Evaluate the small model and export metrics
evaluate-md	Evaluate the medium model and export metrics
evaluate-lg	Evaluate the large model and export metrics
evaluate-trf	Evaluate the transformer model and export metrics
build-table	Build a table from the metrics for README.md
readme	Build a table from the metrics for README.md
package	Package the trained model as a pip package

⏭ Workflows

The following workflows are defined by the project. They can be executed using weasel run [name] and will run the specified commands in order. Commands are only re-run if their inputs have changed.

Workflow	Steps
all-sm-sents	convert-sents → split → create-config-sm → train-sm → evaluate-sm

🗂 Assets

The following assets are defined by the project. They can be fetched by running weasel assets in the project directory.

File	Source	Description
assets/train.jsonl	Local	Training data. Chunked into sentences.
assets/dev.jsonl	Local	Validation data. Chunked into sentences.
assets/test.jsonl	Local	Testing data. Chunked into sentences.
assets/annotated_data.json/	Local	All data, including negative examples.
assets/annotated_data_spans.jsonl	Local	Data with examples of span annotations.
corpus/train.spacy	Local	Training data in serialized format.
corpus/dev.spacy	Local	Validation data in serialized format.
corpus/test.spacy	Local	Testing data in serialized format.
gold-training-data/*	Local	Original outputs from Prodigy.
notebooks/*	Local	Notebooks for testing project features.
configs/*	Local	Config files for training spaCy models.

Model Metrics

Overall Model Performance

Model	Precision	Recall	F-Score
Small	94.1	89.2	91.6
Medium	94	90.5	92.2
Large	94.1	91.7	92.9
Transformer	93.6	91.6	92.6

Performance per Label

Model	Label	Precision	Recall	F-Score
Small	BUILDING	94.7	90.2	92.4
Medium	BUILDING	95.2	92.8	94
Large	BUILDING	94.8	93.2	94
Transformer	BUILDING	94.3	94.2	94.3
Small	COUNTRY	97.6	94.6	96.1
Medium	COUNTRY	96.5	96.3	96.4
Large	COUNTRY	97.7	96.8	97.2
Transformer	COUNTRY	96.6	96.8	96.7
Small	DLF	92.4	86.4	89.3
Medium	DLF	95	84.1	89.2
Large	DLF	93.5	88.4	90.9
Transformer	DLF	94.1	90.4	92.2
Small	ENV_FEATURES	86.6	81.2	83.8
Medium	ENV_FEATURES	86.3	79.1	82.5
Large	ENV_FEATURES	77.5	90.1	83.3
Transformer	ENV_FEATURES	85.1	86.9	86
Small	INT_SPACE	93.8	85.9	89.6
Medium	INT_SPACE	93.9	91.3	92.6
Large	INT_SPACE	92.4	93.8	93.1
Transformer	INT_SPACE	94.6	91.8	93.2
Small	NPIP	92.7	86.4	89.4
Medium	NPIP	94.5	82.4	88
Large	NPIP	92.7	86.6	89.6
Transformer	NPIP	94.8	83	88.5
Small	POPULATED_PLACE	94	90.6	92.3
Medium	POPULATED_PLACE	93	91.2	92.1
Large	POPULATED_PLACE	95.2	90.4	92.7
Transformer	POPULATED_PLACE	92.1	91.3	91.7
Small	REGION	84.4	68.4	75.6
Medium	REGION	81.4	75.8	78.5
Large	REGION	83	76.8	79.8
Transformer	REGION	81.2	68.4	74.3
Small	SPATIAL_OBJ	96	90	92.9
Medium	SPATIAL_OBJ	95.2	93.8	94.5
Large	SPATIAL_OBJ	95.3	95.5	95.4
Transformer	SPATIAL_OBJ	96.3	92.8	94.5

Acknowledgements

This project, based in the University of Maine’s History Department (Anne Kelly Knowles, PI), has been funded by National Endowment for the Humanities Digital Humanities Advancement grant no. HAA-287827-22; a Collaborative Research Seed Grant, Center for the Humanities, Washington University in St. Louis; the Clement and Linda McGillicuddy Humanities Center, University of Maine; and the Dale Benson Gift Fund, University of Maine.

Dataset Team

Christine Liu, William Mattingly, Gregory Gaines