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xlm-roberta-large-manifesto

Model description

An xlm-roberta-large model finetuned on multilingual training data labeled using the Manifesto Project's coding scheme.

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-manifesto",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)

Gated access

Due to the gated access, you must pass the token parameter when loading the model. In earlier versions of the Transformers package, you may need to use the use_auth_token parameter instead.

Model performance

The model was evaluated on a test set of 305141 examples, which were split in a stratified manner, where for every label, 20% of all occurences were randomly selected.
Metrics (precision, recall and F1-score are weighted macro averages):

Precision	Recall	F1-Score	Accuracy	Top3_Acc	Top5_Acc
0.6495	0.6547	0.6507	0.6547	0.8505	0.9073

Language-specific metrics:

Debugging and issues

This architecture uses the sentencepiece tokenizer. In order to run the model before transformers==4.27 you need to install it manually.

poltextlab
/

xlm-roberta-large-manifesto