
## Facebook/bart-large-cnn model

This model is intended to summarize political texts regarding climate change, the environment and energy. The model was fine-tuned on 7k political party press releases from 66 parties in 12 different countries and is intended to identify the primary issue of the press release, the position of the party on the primary issue, and a 1-2 sentence summary. 
Training Data primarily consists of GPT-4 responses asking for summaries of the press releases. Small modifications were also made to the summaries from GPT-4 when validating the responses. I also made all the training text summaries lower case by accident (oops!), so outputs are lowercase. 

**Note** The model is pretty good at identifying the primary issue of any text, but it'll refer to the author of the text as 'the party' and summarize the "position" of *the party* as such. 

**Countries included in Training Data** = ['Italy', 'Sweden', 'Switzerland', 'Netherlands', 'Germany', 'Denmark', 'Spain', 'UK', 'Austria', 'Poland', 'Ireland', 'France']


Citation: 

```
@online{dickson2023,
  title = {Bart-large CNN Climate Change Summarization},
  url = {https://huggingface.co/z-dickson/bart-large-cnn-climate-change-summarization},
  author = {Dickson, Zachary P},
  year = 2023,
  urldate = {\today},
}
```
{"language": ["en", "es", "da", "de", "it", "fr", "nl", "pl"], "license": "afl-3.0", "tags": ["politics", "summarization", "climate change", "political party"], "metrics": ["rouge"], "widget": [{"text": "In the light of the current spate of organised vandalism perpetrated in the names of Eco This or Stop Something Else, haven\u2019t we seen this kind of near mass-hysterical action before? With certain obvious exceptions, most of the activists appear to be in the teens to early twenties bracket, and of a comfortable so-called middle-class group. In any event, they have been persuaded that this business of \u2018climate change\u2019 which has steadily become some sort of cult, is about to destroy all life as we know it. In reality, the world\u2019s climate has been changing since the \u2018Big Bang\u2019 and will continue so to do until the whole thing eventually fizzles out. They have not yet cottoned on to the fact that by far the biggest threat to human existence is that of overpopulation. What is more disturbing, however, is the ease with which they have been recruited into behaving as they do \u2013 with no regard to everybody else\u2019s opinions and wishes. Whether by disrupting a Snooker Tournament, the Grand National, obstructing motorways or whatever else, it is clear that there is a core group of these \u2018eco\u2019 fanatics who can be directed to any place or event that somebody decides should be attacked, whenever and wherever they choose. For this to happen, there has to be a hierarchy at large, as opposed to and directing the cannon fodder who actually make the mischief. As we have seen on various other occasions, it is those \u2018useful idiots\u2019 who do the dirty work while the organisers stay safely away and laugh at those gullible enough to take it all in, regardless of the veracity of their cause\u2019 or the consequences of their mindless actions. This is not new by any means. The Nazis in pre-war Germany used similar tactics involving some sort of brainwashing and intimidation, which resulted in the emergence of Hitler Youth and we all know what a misguided bunch they eventually turned out to be. Of more concern these days is the potential for the organisers of these events to bring together at short notice a substantial gang of activists who can be easily manipulated into carrying out acts of serious civil disobedience against any stratum of society they decide needs their form of correction or treatment. This is a form of grooming  however you look at it. Of course, there will be a percentage who will duck out of any really serious civil disorder, but that would still leave a substantial number of organised troublemakers who will relish the thought of seizing some sort of power to affect political thought or action. This is generally accompanied by those seeking to maximise damage to public and private property. It is regrettable that the Courts have so far failed to acknowledge this current spate of ochlocracy. Meanwhile, we all have to put up with that troublesome element intent on testing the boundaries of a decent democratic society.", "example_title": "English (Political party: UKIP)"}, {"text": "Die Bek\u00e4mpfung illegaler Migration ist eine gemeinsame Priorit\u00e4t von \u00d6sterreich und Indien, gerade vor dem Hintergrund der dramatisch gestiegenen Ankunftszahlen illegaler Migrantinnen und Migranten aus Indien im vergangenen Jahr. Ausdruck dessen findet sich im Abkommen \u00fcber eine umfassende Migrations- und Mobilit\u00e4tspartnerschaft, die Au\u00dfenminister Alexander Schallenberg und sein indischer Amtskollege, Subrahmanyam Jaishankar, am Rande des EU-Indopazifik-Ministerforums in Stockholm unterzeichnet haben. Damit wurde erstmals eine vertragliche Grundlage f\u00fcr R\u00fcckf\u00fchrungen nach Indien geschaffen, die zusammen mit der erfolgten Abschaffung der visafreien Einreise aus Indien nach Serbien, f\u00fcr die sich Kanzler und Innenminister erfolgreich eingesetzt haben, zu einem noch deutlicheren R\u00fcckgang der illegalen Migration aus Indien f\u00fchren wird. Aber es geht dabei nicht nur um die Bek\u00e4mpfung von illegaler Migration, sondern auch viel mehr um die St\u00e4rkung legaler Migrationsm\u00f6glichkeiten, insbesondere f\u00fcr die Fachkr\u00e4fte, die \u00d6sterreich dringend ben\u00f6tigt. Hierbei soll zuk\u00fcnftig zuerst Kontakt zwischen Firmen und den potentiellen Arbeitskr\u00e4ften hergestellt werden, wobei die Zusammenarbeit zwischen staatlichen Agenturen indischen Staatsangeh\u00f6rigen erleichtern soll, einen geeigneten Arbeitgeber in \u00d6sterreich zu finden. Ein verbesserter Austausch von Studierenden und eine z\u00fcgige Visavergabe, vor allem f\u00fcr Journalistinnen und Journalisten sowie in der Wissenschaft ist ebenfalls vorgesehen. F\u00fcr junge Menschen wird dar\u00fcber hinaus die Chance geschaffen, durch ein Working Holiday Programm im Zielland kurze, befristete Arbeitsverh\u00e4ltnisse einzugehen oder Bildungseinrichtungen ohne Besch\u00e4ftigungsbewilligung zu nutzen. Au\u00dfenminister Alexander Schallenberg: \u201eDas Abkommen ist ein Meilenstein in unseren Beziehungen mit dem bev\u00f6lkerungsreichsten Land der Welt. Es schafft M\u00f6glichkeiten, indische Arbeitskr\u00e4fte beispielsweise im Rahmen der Rot-Wei\u00df-Rot Karte nach \u00d6sterreich zu bringen. Hochqualifizierte Inderinnen und Inder k\u00f6nnen nun dort L\u00fccken schlie\u00dfen, wo es in \u00d6sterreich an Arbeitskr\u00e4ften fehlt.\u201c", "example_title": "German (Political party: Austrian People's party)"}]}
transformers pytorch tensorboard safetensors bart text2text-generation politics summarization climate change political party en es da de it fr nl pl license:afl-3.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #politics #summarization #climate change #political party #en #es #da #de #it #fr #nl #pl #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


## Facebook/bart-large-cnn model

This model is intended to summarize political texts regarding climate change, the environment and energy. The model was fine-tuned on 7k political party press releases from 66 parties in 12 different countries and is intended to identify the primary issue of the press release, the position of the party on the primary issue, and a 1-2 sentence summary. 
Training Data primarily consists of GPT-4 responses asking for summaries of the press releases. Small modifications were also made to the summaries from GPT-4 when validating the responses. I also made all the training text summaries lower case by accident (oops!), so outputs are lowercase. 

Note The model is pretty good at identifying the primary issue of any text, but it'll refer to the author of the text as 'the party' and summarize the "position" of *the party* as such. 

Countries included in Training Data = ['Italy', 'Sweden', 'Switzerland', 'Netherlands', 'Germany', 'Denmark', 'Spain', 'UK', 'Austria', 'Poland', 'Ireland', 'France']


Citation: 


## Facebook/bart-large-cnn model

This model is intended to summarize political texts regarding climate change, the environment and energy. The model was fine-tuned on 7k political party press releases from 66 parties in 12 different countries and is intended to identify the primary issue of the press release, the position of the party on the primary issue, and a 1-2 sentence summary. 
Training Data primarily consists of GPT-4 responses asking for summaries of the press releases. Small modifications were also made to the summaries from GPT-4 when validating the responses. I also made all the training text summaries lower case by accident (oops!), so outputs are lowercase. 

Note The model is pretty good at identifying the primary issue of any text, but it'll refer to the author of the text as 'the party' and summarize the "position" of *the party* as such. 

Countries included in Training Data = ['Italy', 'Sweden', 'Switzerland', 'Netherlands', 'Germany', 'Denmark', 'Spain', 'UK', 'Austria', 'Poland', 'Ireland', 'France']


Citation:
TAGS
#transformers #pytorch #tensorboard #safetensors #bart #text2text-generation #politics #summarization #climate change #political party #en #es #da #de #it #fr #nl #pl #license-afl-3.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## Facebook/bart-large-cnn model

This model is intended to summarize political texts regarding climate change, the environment and energy. The model was fine-tuned on 7k political party press releases from 66 parties in 12 different countries and is intended to identify the primary issue of the press release, the position of the party on the primary issue, and a 1-2 sentence summary. 
Training Data primarily consists of GPT-4 responses asking for summaries of the press releases. Small modifications were also made to the summaries from GPT-4 when validating the responses. I also made all the training text summaries lower case by accident (oops!), so outputs are lowercase. 

Note The model is pretty good at identifying the primary issue of any text, but it'll refer to the author of the text as 'the party' and summarize the "position" of *the party* as such. 

Countries included in Training Data = ['Italy', 'Sweden', 'Switzerland', 'Netherlands', 'Germany', 'Denmark', 'Spain', 'UK', 'Austria', 'Poland', 'Ireland', 'France']


Citation: