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--- |
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license: mit |
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language: |
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- multilingual |
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tags: |
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- zero-shot-classification |
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- text-classification |
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- pytorch |
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metrics: |
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- accuracy |
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- f1-score |
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extra_gated_prompt: 'Our models are intended for academic use only. If you are not |
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affiliated with an academic institution, please provide a rationale for using our |
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models. |
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If you use our models for your work or research, please cite this paper: Sebők, |
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M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large |
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Language Models for Multilingual Policy Topic Classification: The Babel Machine |
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Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434' |
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extra_gated_fields: |
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Name: text |
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Country: country |
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Institution: text |
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E-mail: text |
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Use case: text |
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--- |
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# xlm-roberta-large-spanish-party-cap-v3 |
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## Model description |
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An `xlm-roberta-large` model finetuned on multilingual training data containing texts of the `party` domain labelled with [major topic codes](https://www.comparativeagendas.net/pages/master-codebook) from the [Comparative Agendas Project](https://www.comparativeagendas.net/). |
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## How to use the model |
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```python |
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from transformers import AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") |
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pipe = pipeline( |
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model="poltextlab/xlm-roberta-large-spanish-party-cap-v3", |
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task="text-classification", |
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tokenizer=tokenizer, |
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use_fast=False, |
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token="<your_hf_read_only_token>" |
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) |
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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." |
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pipe(text) |
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``` |
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### Gated access |
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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. |
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## Model performance |
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The model was evaluated on a test set of 3041 examples (10% of the available data).<br> |
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Model accuracy is **0.75**. |
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| label | precision | recall | f1-score | support | |
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|:-------------|------------:|---------:|-----------:|----------:| |
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| 0 | 0.64 | 0.67 | 0.65 | 183 | |
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| 1 | 0.7 | 0.59 | 0.64 | 126 | |
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| 2 | 0.86 | 0.79 | 0.83 | 150 | |
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| 3 | 0.86 | 0.78 | 0.82 | 88 | |
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| 4 | 0.77 | 0.75 | 0.76 | 235 | |
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| 5 | 0.81 | 0.83 | 0.82 | 234 | |
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| 6 | 0.67 | 0.77 | 0.72 | 88 | |
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| 7 | 0.71 | 0.85 | 0.78 | 53 | |
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| 8 | 0.4 | 0.64 | 0.49 | 59 | |
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| 9 | 0.88 | 0.85 | 0.87 | 117 | |
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| 10 | 0.85 | 0.82 | 0.83 | 219 | |
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| 11 | 0.68 | 0.76 | 0.71 | 225 | |
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| 12 | 0.79 | 0.77 | 0.78 | 77 | |
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| 13 | 0.64 | 0.74 | 0.68 | 211 | |
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| 14 | 0.81 | 0.76 | 0.79 | 84 | |
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| 15 | 0.75 | 0.71 | 0.73 | 154 | |
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| 16 | 0.77 | 0.49 | 0.6 | 49 | |
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| 17 | 0.83 | 0.77 | 0.8 | 249 | |
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| 18 | 0.78 | 0.73 | 0.75 | 298 | |
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| 19 | 0.67 | 0.5 | 0.57 | 32 | |
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| 20 | 0.83 | 0.82 | 0.82 | 110 | |
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| macro avg | 0.75 | 0.73 | 0.73 | 3041 | |
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| weighted avg | 0.76 | 0.75 | 0.75 | 3041 | |
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## Inference platform |
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This model is used by the [CAP 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. |
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## Cooperation |
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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](https://babel.poltextlab.com). |
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## Debugging and issues |
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This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually. |
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If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue. |
