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Do you remember what was going on at this point? | [] | null | 1,364,660,190 | 2,145,526,795 | [
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You went outside, you realized there had been a bomb. | [] | null | -217,436,304 | -608,116,526 | [
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What did you see? | [] | null | 2,119,361,142 | 1,286,493,661 | [
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What were people doing? | [] | null | -346,591,761 | 487,798,403 | [
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Panic, immediately. | [] | null | 2,113,096,972 | 543,958,744 | [
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There was great panic in town. | [
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People did not know what to do, to stay in that little town or escape to Warsaw. | [
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My parents they went to Warsaw. | [
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All the six children and my mother. | [] | null | -2,030,619,445 | -1,405,329,295 | [
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My father was in the militia. | [] | null | -2,022,249,957 | 1,436,191,337 | [
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End of preview. Expand
in Dataset Viewer.
📚 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
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