Create README.md

README.md

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+---
+tags:
+- question-answering
+language:
+- multilingual
+- cs
+- en
+---
+
+# Mt5-base for Czech+English Generative Question Answering
+
+This is the [mt5-base](https://huggingface.co/google/mt5-base) model with an LM head for a generation of extractive answers. In contrary to our [mt5-base-priming](https://huggingface.co/gaussalgo/mt5-base-priming-QA_en-cs/edit/main/README.md), this is a traditional sequence2sequence model without priming, though can also be used on other Text extraction tasks, such as Named Entity Recognition in zero-shot settings (with a significant decay in quality, compared to priming).
+
+## Intended uses & limitations
+
+This model is purposed to *generate* 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 multilingual large language models, the model will likely also work on other languages as well, with a specific decay in quality.
+
+Note that despite its size, English SQuAD has a variety of reported biases, 
+conditioned by the relative position or type of the answer in the context that can affect the model's performance on new data 
+(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, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("gaussalgo/mt5-base-generative-QA_en-cs")
+model = AutoModelForSeq2SeqLM.from_pretrained("gaussalgo/mt5-base-generative-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.generate(**inputs)
+
+print("Answer:")
+print(tokenizer.decode(outputs))
+
+```
+
+## 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=5e-5,
+                                         stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
+                                         do_train=True,
+                                         do_eval=True,
+                                         warmup_steps=1000,
+                                         max_steps=100000,
+                                         gradient_accumulation_steps=4,
+                                         eval_steps=100,
+                                         logging_steps=10,
+                                         save_steps=1000,
+                                         num_train_epochs=50,
+                                         evaluation_strategy="steps",
+                                         remove_unused_columns=False)
+
+```
+
+You can find the full training script in [train_mt5_qa_en+cs.py](https://huggingface.co/gaussalgo/mt5-base-generative-QA_en-cs/blob/main/train_mt5_qa_en%2Bcs.py), reproducible after a specific data preprocessing for Czech SQAD in [parse_czech_squad.py](parse_czech_squad.py)