| --- |
| license: cc-by-4.0 |
| language: |
| - multilingual |
| tags: |
| - text-classification |
| - pytorch |
| metrics: |
| - f1-score |
| 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 |
| 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] poltextlab [dot] com 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. |
| --- |
| # xlm-roberta-large-pooled-MORES |
| ## 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="poltextlab/xlm-roberta-large-pooled-MORES", |
| 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. |
|
|
| ## Cooperation |
| Model performance can be significantly improved by extending our training sets. We appreciate every submission of coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [Babel Machine](https://babel.poltextlab.com). |
|
|
| ## 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. |
|
|
| ## Funding |
|
|
| The research was funded by European Union’s Horizon 2020 research and innovation program, “MORES” project (Grant No.: 101132601). |
