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--- |
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license: apache-2.0 |
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language: |
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- bs |
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- hr |
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- sr |
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- sl |
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- sk |
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- cs |
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- en |
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tags: |
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- sentiment-analysis |
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- text-regression |
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- text-classification |
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- sentiment-regression |
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- sentiment-classification |
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- parliament |
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inference: false |
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--- |
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# Multilingual parliament sentiment regression model XLM-R-Parla-Sent |
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This model is based on [xlm-r-parla](https://huggingface.co/classla/xlm-r-parla), an XLM-R-large model additionally pre-trained on parliamentary proceedings. The model was fine-tuned on the [ParlaSent dataset](http://hdl.handle.net/11356/1868), a manually annotated selection of sentences of parliamentary proceedings from Bosnia and Herzegovina, Croatia, Czechia, Serbia, Slovakia, Slovenia, and the United Kingdom. |
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Both the additionally pre-trained model, as the training dataset are results of the [ParlaMint project](https://www.clarin.eu/parlamint). The details on the models and the dataset are described in the following publication (to be published soon): |
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Michal Mochtak, Peter Rupnik, Nikola Ljubešić: The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings. |
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## Annotation schema |
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The discrete labels, present in the original dataset, were mapped to integers as follows: |
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``` |
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"Negative": 0.0, |
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"M_Negative": 1.0, |
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"N_Neutral": 2.0, |
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"P_Neutral": 3.0, |
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"M_Positive": 4.0, |
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"Positive": 5.0, |
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``` |
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The model was then fine-tuned on numeric labels and set up as a regressor. |
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## Finetuning procedure |
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The fine-tuning procedure is described in the pending paper. Presumed optimal hyperparameters used are |
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``` |
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num_train_epochs=4, |
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train_batch_size=32, |
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learning_rate=8e-6, |
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regression=True |
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``` |
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## Results |
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Results reported were obtained from 5 fine-tuning runs. |
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test dataset | R^2 | MAE |
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--- | --- | --- |
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BCS | 0.6146 ± 0.0104 | 0.7050 ± 0.0089 |
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EN | 0.6722 ± 0.0100 | 0.6755 ± 0.0076 |
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## Usage Example |
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With `simpletransformers==0.64.3`. |
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```python |
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from simpletransformers.classification import ClassificationModel, ClassificationArgs |
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import torch |
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model_args = ClassificationArgs( |
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regression=True, |
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) |
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model = ClassificationModel(model_type="xlmroberta", model_name="classla/xlm-r-parlasent",use_cuda=torch.cuda.is_available(), num_labels=1,args=model_args) |
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model.predict(["I fully disagree with this argument.", "The ministers are entering the chamber.", "Things can always be improved in the future.", "These are great news."]) |
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``` |
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Output: |
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```python |
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( |
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array([0.11633301, 3.63671875, 4.203125 , 5.30859375]), |
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array([0.11633301, 3.63671875, 4.203125 , 5.30859375]) |
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) |
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``` |
