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--- |
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license: apache-2.0 |
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widget: |
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- text: "One day I will be a real teacher and I will try to do the best I can for the children." |
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example_title: "Classification (without context)" |
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--- |
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# Model Card for XLM-Roberta-large-reflective-conf4 |
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This is a reflectivity classification model trained to distinguish different types of reflectivity in the reports of teaching students. |
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It was evaluated in a cross-lingual settings and was found to work well also in languages outside English -- see the results in the referenced paper. |
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## Model Details |
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- **Repository:** https://github.com/EduMUNI/reflection-classification |
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- **Paper:** https://link.springer.com/article/10.1007/s10639-022-11254-7 |
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- **Developed by:** Michal Stefanik & Jan Nehyba, Masaryk University |
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- **Model type:** Roberta-large |
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- **Finetuned from model:** [XLM-R-large](https://huggingface.co/xlm-roberta-large) |
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## Usage |
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To match the training format, it is best to use the prepared wrapper that will format the classified sentence and its surrounding context in the expected format: |
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```python |
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from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer |
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LABELS = ["Other", "Belief", "Perspective", "Feeling", "Experience", |
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"Reflection", "Difficulty", "Intention", "Learning"] |
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class NeuralClassifier: |
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def __init__(self, model_path: str, uses_context: bool, device: str): |
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self.config = AutoConfig.from_pretrained(model_path) |
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self.device = device |
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self.model = AutoModelForSequenceClassification.from_pretrained(model_path, config=self.config).to(device) |
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self.tokenizer = AutoTokenizer.from_pretrained(model_path) |
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self.uses_context = uses_context |
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def predict_sentence(self, sentence: str, context: str = None): |
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if context is None and self.uses_context: |
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raise ValueError("You need to pass in context argument, including the sentence") |
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features = self.tokenizer(sentence, text_pair=context, |
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padding="max_length", truncation=True, return_tensors='pt') |
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outputs = self.model(**features.to(self.device), return_dict=True) |
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argmax = outputs.logits.argmax(dim=-1).detach().cpu().tolist()[0] |
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labels = LABELS[argmax] |
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return labels |
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``` |
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The wrapper can be used as follows: |
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```python |
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classifier = NeuralClassifier(model_path="MU-NLPC/XLM-R-large-reflective-conf4", |
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uses_context=False, |
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device="cpu") |
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test_sentences = ["And one day I will be a real teacher and I will try to do the best I can for the children.", |
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"I felt really well!", |
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"gfagdhj gjfdjgh dg"] |
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y_pred = [classifier.predict_sentence(sentence) for sentence in tqdm(test_sentences)] |
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print(y_pred) |
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>>> ['Intention', 'Feeling', 'Other'] |
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``` |
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### Training Data |
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The model was trained on a [CEReD dataset](http://hdl.handle.net/11372/LRT-3573) and aims for the best possible evaluation in cross-lingual settings (on unseen languages). |
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See the reproducible training script in the project directory: https://github.com/EduMUNI/reflection-classification |
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## Citation |
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If you use the model in scientific work, please acknowledge our work as follows. |
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```bibtex |
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@Article{Nehyba2022applications, |
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author={Nehyba, Jan and {\v{S}}tef{\'a}nik, Michal}, |
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title={Applications of deep language models for reflective writings}, |
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journal={Education and Information Technologies}, |
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year={2022}, |
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month={Sep}, |
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day={05}, |
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issn={1573-7608}, |
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doi={10.1007/s10639-022-11254-7}, |
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url={https://doi.org/10.1007/s10639-022-11254-7} |
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} |
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``` |
