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README.md

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+---
+tags:
+- exbert
+- question-answering
+language:
+- multilingual
+- cs
+- en
+---
+
+# XLM RoBERTa for Czech+English Extractive Question Answering
+
+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.
+
+## Intended uses & limitations
+
+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.
+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.
+
+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).
+
+## Usage
+
+Here is how to use this model to answer the question on a given context using 🤗 Transformers in PyTorch:
+
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs")
+model = AutoModelForQuestionAnswering.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs")
+
+context = """
+Podle slovenského lidového podání byl Juro Jánošík obdařen magickými předměty (kouzelná valaška, čarovný opasek),
+které mu dodávaly nadpřirozené schopnosti. Okrádal především šlechtice,
+trestal panské dráby a ze svého lupu vyděloval část pro chudé, tedy bohatým bral a chudým dával.
+"""
+question = "Jaké schopnosti daly magické předměty Juro Jánošíkovi?"
+
+inputs = tokenizer(question, context, return_tensors="pt")
+outputs = model(**inputs)
+start_position = outputs.start_logits[0].argmax()
+end_position = outputs.end_logits[0].argmax()
+answer_ids = tokenizer.decode(inputs["input_ids"][0][start_position:end_position])
+
+print("Answer:")
+print(tokenizer.decode(answer_ids))
+
+```
+
+## Training
+
+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:
+
+```python
+training_arguments = AdaptationArguments(output_dir="train_dir",
+learning_rate=1e-5,
+stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
+do_train=True,
+do_eval=True,
+warmup_steps=1000,
+max_steps=100000,
+gradient_accumulation_steps=30,
+eval_steps=100,
+logging_steps=10,
+save_steps=1000,
+num_train_epochs=30,
+evaluation_strategy="steps")
+```
+
+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)