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--- |
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--- |
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language: |
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- da |
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tags: |
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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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--- |
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# xlm-roberta-large-danish-parlspeech-cap-v3 |
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## Model description |
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An `xlm-roberta-large` model fine-tuned on danish training data containing parliamentary speeches (oral questions, interpellations, bill debates, other plenary speeches, urgent questions) labeled 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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This snippet prints the three most probable labels and their corresponding softmax scores: |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("poltextlab/xlm-roberta-large-danish-parlspeech-cap-v3") |
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tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") |
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sentence = "This is an example." |
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inputs = tokenizer(sentence, |
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return_tensors="pt", |
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max_length=512, |
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padding="do_not_pad", |
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truncation=True |
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) |
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logits = model(**inputs).logits |
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probs = torch.softmax(logits, dim=1).tolist()[0] |
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probs = {model.config.id2label[index]: round(probability, 2) for index, probability in enumerate(probs)} |
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top3_probs = dict(sorted(probs.items(), key=lambda item: item[1], reverse=True)[:3]) |
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print(top3_probs) |
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``` |
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## Model performance |
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The model was evaluated on a test set of 44159 examples.<br> |
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Model accuracy is **0.94**. |
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| label | precision | recall | f1-score | support | |
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|:-------------|------------:|---------:|-----------:|----------:| |
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| 0 | 0.91 | 0.92 | 0.92 | 2310 | |
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| 1 | 0.9 | 0.9 | 0.9 | 1285 | |
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| 2 | 0.98 | 0.96 | 0.97 | 3400 | |
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| 3 | 0.95 | 0.95 | 0.95 | 1972 | |
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| 4 | 0.92 | 0.93 | 0.93 | 2679 | |
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| 5 | 0.96 | 0.96 | 0.96 | 2778 | |
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| 6 | 0.94 | 0.94 | 0.94 | 2458 | |
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| 7 | 0.96 | 0.94 | 0.95 | 1173 | |
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| 8 | 0.95 | 0.96 | 0.96 | 1948 | |
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| 9 | 0.95 | 0.97 | 0.96 | 3276 | |
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| 10 | 0.94 | 0.95 | 0.94 | 3224 | |
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| 11 | 0.92 | 0.93 | 0.93 | 2270 | |
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| 12 | 0.94 | 0.93 | 0.93 | 1510 | |
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| 13 | 0.89 | 0.89 | 0.89 | 1759 | |
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| 14 | 0.96 | 0.95 | 0.95 | 1941 | |
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| 15 | 0.95 | 0.93 | 0.94 | 1343 | |
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| 16 | 0.89 | 0.9 | 0.9 | 402 | |
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| 17 | 0.95 | 0.94 | 0.95 | 3337 | |
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| 18 | 0.92 | 0.92 | 0.92 | 3484 | |
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| 19 | 0.95 | 0.95 | 0.95 | 834 | |
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| 20 | 0.93 | 0.91 | 0.92 | 776 | |
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| macro avg | 0.94 | 0.93 | 0.94 | 44159 | |
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| weighted avg | 0.94 | 0.94 | 0.94 | 44159 | |
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### Fine-tuning procedure |
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This model was fine-tuned with the following key hyperparameters: |
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- **Number of Training Epochs**: 10 |
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- **Batch Size**: 8 |
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- **Learning Rate**: 5e-06 |
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- **Early Stopping**: enabled with a patience of 2 epochs |
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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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## Reference |
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Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434 |
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## Debugging and issues |
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This architecture uses the `sentencepiece` tokenizer. In order to use 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. |
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