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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. Please allow us a few business days to manually review subscriptions. |
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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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Institution Email: text |
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Please specify your academic use case: text |
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--- |
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# xlm-roberta-large-pooled-cap-minor-v3 |
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## Model description |
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An `xlm-roberta-large` model finetuned on multilingual (english, danish, hungarian) training data labelled with [minor 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-pooled-cap-minor-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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truncation=True, |
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max_length=512, |
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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 15 349 english examples (20% of the english data).<br> |
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* Accuracy: **0.87**. |
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* Weighted Average F1-score: **0.86** |
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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. |
