poltextlab / xlm-roberta-large-pooled-cap-media

xlm-roberta-large-pooled-cap-media

Model description

An xlm-roberta-large model finetuned on multilingual (english, german, hungarian, spanish, slovakian) training data labelled with major topic codes from the Comparative Agendas Project . Furthermore we used the follwoing 7 media codes:

• State and Local Government Administration (24)
• Weather and Natural Disaster (26)
• Fires(27)
• Sports and Recreation (29)
• Death Notices (30)
• Churches and Religion (31)
• Other, Miscellaneous and Human Interest (99)

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-pooled-cap-media",
    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 8874 english examples.

• Precision: 0.80 .
• Recall: 0.79
• Weighted Average F1-score: 0.79

Inference platform

This model is used by the CAP Babel Machine , an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.

Cooperation

Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the CAP Babel Machine .

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.

If you encounter a RuntimeError when loading the model using the from_pretrained() method, adding ignore_mismatched_sizes=True should solve the issue.