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--- |
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tags: |
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- exbert |
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- question-answering |
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language: |
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- multilingual |
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- cs |
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- en |
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--- |
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# XLM RoBERTa for Czech+English Extractive Question Answering |
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This is the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model with a head for extractive question answering trained on a combination of [English SQuAD 1.1](https://huggingface.co/datasets/squad) and [Czech SQAD 3.0](https://lindat.cz/repository/xmlui/handle/11234/1-3069) Question Answering datasets. For the Czech SQAD 3.0, original contexts (=whole Wikipedia websites) were limited to fit the RoBERTa's context window, excluding ~3% of the samples. |
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## Intended uses & limitations |
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This model is purposed to extract a segment of a given context that contains an answer to a given question (Extractive Question Answering) in English and Czech. |
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Given the fine-tuning on two languages and a good reported zero-shot cross-lingual applicability of other fine-tuned XLM-RoBERTas, the model will likely work on other languages as well, with a decay in quality. |
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Note that despite its size, English SQuAD has a variety of reported biases (see, e.g. [L. Mikula (2022)](https://is.muni.cz/th/adh58/?lang=en), Chap. 4.1). |
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## Usage |
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Here is how to use this model to answer the question on a given context using 🤗 Transformers in PyTorch: |
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```python |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
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tokenizer = AutoTokenizer.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs") |
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model = AutoModelForQuestionAnswering.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs") |
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context = """ |
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Podle slovenského lidového podání byl Juro Jánošík obdařen magickými předměty (kouzelná valaška, čarovný opasek), |
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které mu dodávaly nadpřirozené schopnosti. Okrádal především šlechtice, |
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trestal panské dráby a ze svého lupu vyděloval část pro chudé, tedy bohatým bral a chudým dával. |
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""" |
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question = "Jaké schopnosti daly magické předměty Juro Jánošíkovi?" |
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inputs = tokenizer(question, context, return_tensors="pt") |
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outputs = model(**inputs) |
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start_position = outputs.start_logits[0].argmax() |
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end_position = outputs.end_logits[0].argmax() |
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answer_ids = tokenizer.decode(inputs["input_ids"][0][start_position:end_position]) |
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print("Answer:") |
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print(tokenizer.decode(answer_ids)) |
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``` |
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## Training |
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The model has been trained using [Adaptor library](https://github.com/gaussalgo/adaptor) v0.1.5, in parallel on both Czech and English data, with the following parameters: |
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```python |
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training_arguments = AdaptationArguments(output_dir="train_dir", |
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learning_rate=1e-5, |
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stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED, |
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do_train=True, |
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do_eval=True, |
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warmup_steps=1000, |
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max_steps=100000, |
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gradient_accumulation_steps=30, |
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eval_steps=100, |
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logging_steps=10, |
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save_steps=1000, |
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num_train_epochs=30, |
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evaluation_strategy="steps") |
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``` |
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You can find the full training script in [train_roberta_extractive_qa.py](train_roberta_extractive_qa.py), reproducible after a specific data preprocessing for Czech SQAD in [parse_czech_squad.py](parse_czech_squad.py) |
