xlm-roberta-large-french-legislative-cap-v3

Model description

An xlm-roberta-large model fine-tuned on french training data containing legislative documents (bills, laws, motions, legislative decrees, hearings, resolutions) labeled with major topic codes from the Comparative Agendas Project.

We follow the master codebook of the Comparative Agendas Project, and all of our models use the same major topic codes.

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-french-legislative-cap-v3",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    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)

The translation table from the model results to CAP codes is the following:

CAP_NUM_DICT = {
    0: 1,
    1: 2,
    2: 3,
    3: 4,
    4: 5,
    5: 6,
    6: 7,
    7: 8,
    8: 9,
    9: 10,
    10: 12,
    11: 13,
    12: 14,
    13: 15,
    14: 16,
    15: 17,
    16: 18,
    17: 19,
    18: 20,
    19: 21,
    20: 23,
    21: 999,
}

We have included a 999 label because our models are fine-tuned on training data containing the label 'None' in addition to the 21 CAP major policy topic codes, indicating that the given text contains no relevant policy content. We use the label 999 for these cases.

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 1211 examples.
Model accuracy is 0.83.

Fine-tuning procedure

This model was fine-tuned with the following key hyperparameters:

• Number of Training Epochs: 10
• Batch Size: 8
• Learning Rate: 5e-06
• Early Stopping: enabled with a patience of 2 epochs

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.

Reference

Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434

Debugging and issues

This architecture uses the sentencepiece tokenizer. In order to use 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.