Xlm Roberta Large Twitter Cap Minor

🚀 xlm-roberta-large-twitter-cap-minor

An xlm-roberta-large model fine - tuned on multilingual data for text classification.

🚀 Quick Start

An xlm-roberta-large model is fine - tuned on multilingual (English, Danish, Hungarian) training data. The data is labeled with minor topic codes from the Comparative Agendas Project.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-twitter-cap-minor",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    truncation=True,
    max_length=512,
    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)

Advanced Usage

⚠️ Important Note

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.

📚 Documentation

Model Performance

The model was evaluated on a test set of 15 349 English examples (20% of the English data).

• Accuracy: 0.67.
• Weighted Average F1 - score: 0.61

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.

📄 License

This project is under the MIT license.