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I need you to start off by telling me your name, place of birth, and date of birth?
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| 1,669,136,555 |
My name David Kochalski.
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I was born in a small town called , and I was born May 5, 1928.
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Tell me a little bit about your family life before the war?
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Well, we were very hard working, six children, father and mother and we had a small mill, flour, buckwheat.
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We were not prosperous but comfortable.
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| 1,669,136,578 |
Did you go to a public school, a private school?
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I went to two schools.
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One was a public school in the morning.
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| 1,669,136,633 |
In the afternoon I went to a religious school until almost late at night.
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| 1,669,136,642 |
So your family was fairly religious?
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| 1,669,136,643 |
Yes.
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| 1,669,136,644 |
And Judaism was important to you?
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| 1,669,136,644 |
Well, I raised in the spirit of Judaism.
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| 1,669,136,645 |
Now, when you went to the public school you had a lot of friends who were not Jewish?
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spans_manual
|
accept
| 1,669,136,648 |
No, the school itself, in this little city, was segregated between Catholics and Jews.
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spans_manual
|
accept
| 1,669,136,656 |
Mind you, it was a small town, and I would say the majority of the people in that small town were Jewish people.
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spans_manual
|
accept
| 1,669,136,666 |
Inside the town, somehow, I don't know why, but they separated us Jewish children and Catholic children.
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spans_manual
|
accept
| 1,669,136,670 |
As you know, most of the people in Poland were Catholic.
|
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spans_manual
|
accept
| 1,669,136,673 |
Did you have friends who were Catholics?
|
[] | null | -1,259,775,102 | -327,833,614 |
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spans_manual
|
accept
| 1,669,136,676 |
Yes, I used to have friends.
|
[] | null | -983,660,034 | -262,651,215 |
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spans_manual
|
accept
| 1,669,136,677 |
Did you feel any anti-semitism growing up?
|
[] | null | -847,902,391 | 939,948,254 |
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spans_manual
|
accept
| 1,669,136,679 |
Yes, I did.
|
[] | null | 1,388,049,161 | 1,907,691,129 |
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spans_manual
|
accept
| 1,669,136,680 |
I felt it, maybe not personally, but I knew of a lot of incidents whereby either they were small little -- I would call 1t -- we were separated, in other words, but hardly got together, and there were incidents.
|
[] | null | -1,066,588,585 | 1,476,572,377 |
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spans_manual
|
accept
| 1,669,136,683 |
Incidents, not pleasant incidents, because we were called in from the house, people regardless of how religious we were, did not believe that we were really religious people.
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Were there any incidents with your brothers or sisters that you remember?
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No, other than we were a lovely family.
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Were there a lot of cultural or social organizations, political organizations in the town?
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accept
| 1,669,136,694 |
Yes there were Zionists in the city.
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accept
| 1,669,136,697 |
There were Socialists, there were Communists, even though at that time Communists had to go underground, and religious institutions.
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Were your parents active in the Zionist movement?
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My parents were, I would say, Zionists, and very religious.
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accept
| 1,669,136,708 |
What did you know about Hitler and the rise of Nazism before the war?
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accept
| 1,669,136,709 |
I was aware what was going on even though I was a very young youngster.
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My father used to subscribe to many newspapers, Jewish in origin and non-Jewish papers, papers that had nothing to do with us.
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In fact, I remember when they were chasing out Jews from Germany, who were of Polish origin, my father took in some of them, a couple of them, to stay with them because they came out of there with nothing.
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| 1,669,136,720 |
They really went from city to city amongst Jews.
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There were so many of them that people had to help.
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| 1,669,136,725 |
So, I was aware, until the war, very much aware, what was going on.
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| 1,669,136,727 |
They told stories of what was going on in Germany’?
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| 1,669,136,728 |
Definitely, yes.
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In a public school, since it was more or less a Jewish school, in other words, we did not speak Jewish there, it was Polish, history, everything was Polish, and the guy who was --what do you call it?
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spans_manual
|
accept
| 1,669,136,736 |
He was not Polish, the guy who was running the school, he was not Jewish, he was Polish.
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spans_manual
|
accept
| 1,669,136,741 |
In other words, he was a Catholic, so we were aware amongst us kids, because sometimes we were not greeted in the city very well.
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spans_manual
|
accept
| 1,669,136,749 |
Did you have any idea what was happening in Germany might come to your town?
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spans_manual
|
accept
| 1,669,136,752 |
Yes, because people were aware that eventually Hitler will overrun Poland.
|
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spans_manual
|
accept
| 1,669,136,754 |
It was known between people that he's not going to stop at that time in Czechoslovakia.
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spans_manual
|
accept
| 1,669,136,759 |
He had great -- in other words, he had great expansion ideas, and he asked the Polish government to give him almost everything they needed, or wanted.
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spans_manual
|
accept
| 1,669,136,762 |
So, we knew it was going to be sooner or later.
|
[] | null | 1,476,402,001 | -559,553,823 |
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spans_manual
|
accept
| 1,669,136,763 |
A few days before the war they already had black outs in Poland, because at that time, I would say the Russians and the Poles signed an agreement.
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It was about a week before the war, and everyone was aware that there will be a war.
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We did not know the particulars of what they signed.
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The Germans, the USSR, we anticipated there would be a war.
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accept
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Was there anything that the community did in preparation for this?
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I don't think so.
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accept
| 1,669,136,772 |
The Jewish people naturally were very anxious.
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accept
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They already knew what happened to the people.
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accept
| 1,669,136,775 |
They were thrown out.
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spans_manual
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accept
| 1,669,136,775 |
They already heard of the burnings of the synagogues and everything, but they really thought it will pass if the Germans come in.
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spans_manual
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accept
| 1,669,136,779 |
It will pass, and Jewish people really believed that Germany was going to be defeated.
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spans_manual
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accept
| 1,669,136,781 |
We were aware and the preparation was made in a way.
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spans_manual
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accept
| 1,669,136,782 |
People used to hoard food and everything, because they knew he was going to come.
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spans_manual
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accept
| 1,669,136,783 |
They had certain memories, Jewish people had certain memories of World War I where it really was benign.
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They occupied Poland or part of Poland and it was benign.
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| 1,669,136,786 |
In other words, the occupation, they were not harsh on Jews.
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| 1,669,136,790 |
On the other end, Jews were very friendly because our language, the Jewish language resembled German, so there were a lot of Germans in that town.
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accept
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They used to speak Jewish too, or Jews spoke German.
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spans_manual
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So, the Jews kind of figured it will go over.
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spans_manual
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accept
| 1,669,136,802 |
Maybe the war will take a little longer, but it's not going to be that bad.
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spans_manual
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accept
| 1,669,136,804 |
They did not expect anything very bad.
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spans_manual
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accept
| 1,669,136,805 |
Do you remember what the Catholic population was thinking?
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spans_manual
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accept
| 1,669,136,807 |
Were they eager for Germany to come in?
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spans_manual
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accept
| 1,669,136,808 |
Were they prepared to fight them?
|
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spans_manual
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accept
| 1,669,136,808 |
At that time, it was more or less, I would say a partnership between Jews and Catholics, because they had a history, the catholics had a bad history of German occupations.
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spans_manual
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accept
| 1,669,136,811 |
The Jews already knew what was going to happen, that they are putting Jews in concentration camps and so on.
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spans_manual
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accept
| 1,669,136,816 |
That they are really against Jews, the Germans.
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spans_manual
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Do you remember the day that was occupied by Germany?
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spans_manual
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accept
| 1,669,136,819 |
What I remember is one morning, I think it was on a Friday, we were all sleeping and all of a sudden, a great bang in that little town.
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spans_manual
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accept
| 1,669,136,830 |
A bomb was thrown.
|
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spans_manual
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accept
| 1,669,136,831 |
We did not know it was a bomb.
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accept
| 1,669,136,833 |
We all ran out and we saw planes going over.
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spans_manual
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accept
| 1,669,136,840 |
There was near the town, not far from the town were German garrison (ph).
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spans_manual
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accept
| 1,669,136,898 |
They tried to bomb the garrison but they missed.
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spans_manual
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accept
| 1,669,136,899 |
They bombed the city.
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spans_manual
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accept
| 1,669,136,902 |
At that time, radios whatever you could hear, they announced already that the war had begun.
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spans_manual
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accept
| 1,669,136,904 |
They tried to bomb a German garrison?
|
[] | null | -122,388,725 | 1,772,654,588 |
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spans_manual
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accept
| 1,669,136,906 |
No, it was a Polish Army garrison, which from what I guess, they wanted to bomb, but they probably missed.
|
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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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spans_manual
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accept
| 1,669,136,909 |
You went outside, you realized there had been a bomb.
|
[] | null | -217,436,304 | -608,116,526 |
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spans_manual
|
accept
| 1,669,136,931 |
What did you see?
|
[] | null | 2,119,361,142 | 1,286,493,661 |
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spans_manual
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accept
| 1,669,136,932 |
What were people doing?
|
[] | null | -346,591,761 | 487,798,403 |
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spans_manual
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accept
| 1,669,136,933 |
Panic, immediately.
|
[] | null | 2,113,096,972 | 543,958,744 |
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spans_manual
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accept
| 1,669,136,934 |
There was great panic in town.
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spans_manual
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accept
| 1,669,136,936 |
People did not know what to do, to stay in that little town or escape to Warsaw.
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spans_manual
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accept
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My parents they went to Warsaw.
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accept
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All the six children and my mother.
|
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My father was in the militia.
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accept
| 1,669,136,942 |
End of preview. Expand
in Data Studio
📚 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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19,632