poltextlab / xlm-roberta-large-pooled-cap-media2-v5

xlm-roberta-large-pooled-cap-media2-v5

Model description

An xlm-roberta-large model finetuned on english training data labelled with major topic codes from the Comparative Agendas Project . Furthermore we used the following 18 media codes:

• State and Local Government Administration (24)
• Weather (25)
• Fires, emergencies and natural disasters (26)
• Crime and trials (27)
• Arts, culture, entertainment and history (28)
• Style and fashion (29)
• Food (30)
• Travel (31)
• Wellbeing and learning (32)
• Personal finance and real estate (33)
• Personal technology and popular science (34)
• Churches and Religion (35)
• Celebrities and human interest (36)
• Obituaries and death notices (37)
• Sports (38)
• Crosswords, puzzles, comics (39)
• Media production/internal, letters (40)
• Advertisements (41)

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-media2-v2",
    task="text-classification",
    tokenizer=tokenizer,
    token="<your_hf_read_only_token>"
)

text = "Tapenade, when combined with chopped tomatoes and basil, makes for one of the best simple summer dishes in existence."
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.

Classification Report

Overall Performance:

• Accuracy: 80%
• Macro Avg: Precision: 0.76, Recall: 0.77, F1-score: 0.76
• Weighted Avg: Precision: 0.80, Recall: 0.80, F1-score: 0.80

Per-Class Metrics:

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.