| --- |
| license: cc-by-4.0 |
| language: |
| - multilingual |
| - en |
| - de |
| - fr |
| - pl |
| - sk |
| - cs |
| - hu |
| base_model: FacebookAI/xlm-roberta-large |
| pipeline_tag: text-classification |
| tags: |
| - text-classification |
| - pytorch |
| - emotion-classification |
| - political-communication |
| - computational-social-science |
| metrics: |
| - f1 |
| extra_gated_heading: "Request access to MORESPulse" |
| extra_gated_fields: |
| Country: country |
| Institution: text |
| Institution Email: text |
| Full Name: text |
| Please specify your academic project/use case you want to use the models for: text |
| I agree to cite the accompanying publication (Szabó et al 2026) in any work using this model: checkbox |
| extra_gated_prompt: 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] and 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, Patkós & Boda (2026), Journal of |
| Information Technology & Politics, https://doi.org/10.1080/19331681.2026.2716074. |
| extra_gated_button_content: "Request access" |
| --- |
| |
| # MORESPulse |
|
|
| ## Model description |
|
|
| An `xlm-roberta-large` model finetuned on sentence-level multilingual training data hand-annotated using the following labels: |
|
|
| - **0**: "Anger" |
| - **1**: "Fear" |
| - **2**: "Disgust" |
| - **3**: "Sadness" |
| - **4**: "Joy" |
| - **5**: "None of Them" |
|
|
| This model can also be used for sentiment classification with the following conversion: |
|
|
| - **Joy (4)** → Positive |
| - **None of Them (5)** → Neutral (or None of Them) |
| - **All Other Labels** → Negative |
|
|
| The training data we used was augmented using artificially generated examples and translated texts. It covers 7 languages (English, German, French, Polish, Slovak, Czech and Hungarian) with nearly identical shares. |
|
|
| ## How to use the model |
|
|
| ```python |
| from transformers import AutoTokenizer, pipeline |
| |
| tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") |
| |
| pipe = pipeline( |
| model="MORES-horizon/MORESPulse", |
| 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 language-specific test sets and demonstrated nearly identical performance across all languages: |
|
|
|  |
|
|
| ### Fine-tuning procedure |
|
|
| This model was fine-tuned with the following key hyperparameters: |
|
|
| - **Number of Training Epochs**: 10 |
| - **Batch Size**: 16 |
| - **Learning Rate**: 5e-06 |
| - **Early Stopping**: enabled with a patience of 2 epochs |
|
|
| ## Inference platform |
|
|
| This model is used by the [Babel Machine](https://babel.poltextlab.com), 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., Patkós, V., & Boda, Z. (2026). Political emotionalization in large text corpora: detecting affective cues with MORESPulse and its scientific uses. *Journal of Information Technology & Politics*, 1–19. https://doi.org/10.1080/19331681.2026.2716074 |
|
|
| ```bibtex |
| @article{szabo2026morespulse, |
| author = {Szab{\'o}, Gabriella and Ring, Orsolya and Patk{\'o}s, Veronika and Boda, Zsolt}, |
| title = {Political emotionalization in large text corpora: detecting affective cues with {MORESPulse} and its scientific uses}, |
| journal = {Journal of Information Technology \& Politics}, |
| year = {2026}, |
| pages = {1--19}, |
| doi = {10.1080/19331681.2026.2716074}, |
| url = {https://doi.org/10.1080/19331681.2026.2716074} |
| } |
| ``` |
|
|
| ## 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. |
|
|
