metadata

license: mit
language:
  - multilingual
tags:
  - zero-shot-classification
  - text-classification
  - pytorch
metrics:
  - accuracy
  - f1-score
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  Our models are intended for academic use only. If you are not affiliated with
  an academic institution, please provide a rationale for using our models.
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  If you use our models for your work or research, please cite this paper:
  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
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  Use case: text

xlm-roberta-large-german-party-cap-v3

Model description

An xlm-roberta-large model finetuned on multilingual training data containing texts of the party domain labelled with major topic codes from the Comparative Agendas Project.

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-german-party-cap-v3",
    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 13065 examples (10% of the available data).
Model accuracy is 0.71.

label	precision	recall	f1-score	support
0	0.63	0.7	0.66	1417
1	0.69	0.64	0.67	935
2	0.86	0.77	0.81	567
3	0.76	0.7	0.73	321
4	0.67	0.65	0.66	923
5	0.79	0.82	0.8	823
6	0.72	0.74	0.73	673
7	0.83	0.81	0.82	455
8	0.74	0.76	0.75	294
9	0.81	0.83	0.82	376
10	0.68	0.67	0.68	740
11	0.67	0.68	0.68	1137
12	0.76	0.74	0.75	455
13	0.62	0.7	0.66	676
14	0.81	0.69	0.74	506
15	0.71	0.69	0.7	378
16	0.5	0.57	0.54	160
17	0.76	0.71	0.74	1219
18	0.61	0.65	0.63	737
19	0.67	0.26	0.37	47
20	0.77	0.73	0.75	225
21	0	0	0	1
macro avg	0.68	0.66	0.67	13065
weighted avg	0.71	0.71	0.71	13065

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.