README.md

metadata

license: bigscience-openrail-m
widget:
  - text: >-
      We will restore funding to the Global Environment Facility and the
      Intergovernmental Panel on Climate Change.

Model description

An xlm-roberta-large model fine-tuned on ~1,6 million annotated statements contained in the Manifesto Corpus (version 2023a). The model can be used to categorize any type of text into 56 different political topics according to the Manifesto Project's coding scheme (Handbook 4). It works for all languages the xlm-roberta model is pretrained on (overview), just note that it will perform best for the 38 languages contained in the Manifesto Corpus:

armenian	bosnian	bulgarian	catalan	croatian
czech	danish	dutch	english	estonian
finnish	french	galician	georgian	german
greek	hebrew	hungarian	icelandic	italian
japanese	korean	latvian	lithuanian	macedonian
montenegrin	norwegian	polish	portuguese	romanian
russian	serbian	slovak	slovenian	spanish
swedish	turkish	ukrainian

How to use

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("manifesto-project/manifestoberta-xlm-roberta-56policy-topics-sentence-2023-1-1")
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")

sentence = "We will restore funding to the Global Environment Facility and the Intergovernmental Panel on Climate Change, to support critical climate science research around the world"

inputs = tokenizer(sentence,
                   return_tensors="pt",
                   max_length=200,  #we limited the input to 200 tokens during finetuning
                   padding="max_length",
                   truncation=True
                   )

logits = model(**inputs).logits

probabilities = torch.softmax(logits, dim=1).tolist()[0]
probabilities = {model.config.id2label[index]: round(probability * 100, 2) for index, probability in enumerate(probabilities)}
probabilities = dict(sorted(probabilities.items(), key=lambda item: item[1], reverse=True))
print(probabilities)
# {'501 - Environmental Protection: Positive': 67.28, '411 - Technology and Infrastructure': 15.19, '107 - Internationalism: Positive': 13.63, '416 - Anti-Growth Economy: Positive': 2.02...

predicted_class = model.config.id2label[logits.argmax().item()]
print(predicted_class)
# 501 - Environmental Protection: Positive

Model Performance

The model was evaluated on a test set of 199,046 annotated manifesto statements.

Overall

	Accuracy	Top2_Acc	Top3_Acc	Precision	Recall	F1_Macro	MCC	Cross-Entropy
Sentence Model	0.57	0.73	0.81	0.49	0.43	0.45	0.55	1.5
Context Model	0.64	0.81	0.88	0.54	0.52	0.53	0.62	1.15