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Our models are intended for academic projects and academic research only. If you are not affiliated with an academic institution, please reach out to us at huggingface [at] com or mores-horizon [dot] eu for further inquiry. If we cannot clearly determine your academic affiliation and use case based on your form data, your request may be rejected. Please allow us a few business days to manually review subscriptions. If you use this model, please cite Szabó, Ring, Lipiński & Beichelt (2026), Frontiers in Political Science, https://doi.org/10.3389/fpos.2026.1828880.

MORES_emotions9

Model description

An xlm-roberta-large model finetuned on sentence-level multilingual training data hand-annotated using a nine-category emotion codebook.

Codebook

The model assigns the following labels:

0: "Anger"
1: "Fear"
2: "Disgust"
3: "Sadness"
4: "Joy"
5: "None of Them"
6: "Enthusiasm"
7: "Hope"
8: "Pride"

The training data we used was augmented with translated texts. It covers 7 languages (English, German, French, Polish, Slovak, Czech and Hungarian) with nearly identical shares.

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")

pipe = pipeline(
    model="MORES-horizon/MORES_emotions9",
    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

Overall performance

Accuracy: 0.72
Macro avg: Precision 0.72, Recall 0.73, F1-score 0.72
Weighted avg: Precision 0.72, Recall 0.72, F1-score 0.72

Per-class metrics

Label	Precision	Recall	F1-score	Support
Anger	0.55	0.54	0.55	5439
Fear	0.76	0.80	0.78	5432
Disgust	0.95	0.92	0.94	5432
Sadness	0.84	0.84	0.84	5425
Joy	0.80	0.81	0.80	5152
None of Them	0.69	0.65	0.67	11158
Enthusiasm	0.64	0.60	0.62	5432
Hope	0.50	0.59	0.54	5439
Pride	0.78	0.77	0.78	5432

Inference platform

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

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.

Citation

If you use this model in your research, please cite:

Szabó, G., Ring, O., Lipiński, A., & Beichelt, T. (2026). Protecting collective worth: pride in European parliament campaigns in Germany, Hungary, and Poland. Frontiers in Political Science, 8, 1828880. https://doi.org/10.3389/fpos.2026.1828880

@article{szabo2026pride,
  author  = {Szab{\'o}, Gabriella and Ring, Orsolya and Lipi{\'n}ski, Artur and Beichelt, Timm},
  title   = {Protecting collective worth: pride in European parliament campaigns in {Germany}, {Hungary}, and {Poland}},
  journal = {Frontiers in Political Science},
  year    = {2026},
  volume  = {8},
  pages   = {1828880},
  doi     = {10.3389/fpos.2026.1828880},
  url     = {https://doi.org/10.3389/fpos.2026.1828880}
}

Funding

This project was funded by MORES (Moral Emotions in Politics. How they Divide, how they Unite) which received funding from the European Union under grant agreement No 101132601. However, views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

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Safetensors

Model size

0.6B params

Tensor type

F32

Model tree for MORES-horizon/MORES_emotions9

Base model

FacebookAI/xlm-roberta-large

Finetuned

(1003)

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