Create README.md

README.md

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+---
+tags:
+- generation
+language:
+- multilingual
+- cs
+- en
+---
+
+# Mt5-large for Prime 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, 
+given a small set of 2-5 demonstrations (i.e. primes).
+
+## Priming
+
+Note that **this is a priming model** that expects a **set of demonstrations** of your task of interest, 
+similarly to GPT-3. 
+Rather than performing well on the conventional question answering, it aims to learn to extrapolate the pattern of given demonstrations
+to novel tasks, such as Named Entity Recognition or Keywords Extraction from a given pattern.
+
+## Data & Training
+
+This model was trained on a combination of [AdversarialQA](https://adversarialqa.github.io) 
+and [Czech SQAD 3.0](https://lindat.cz/repository/xmlui/handle/11234/1-3069) 
+Question Answering datasets.
+
+To train the model to use the demonstrations, we've **clustered** the samples by the question-word(s)
+in English AdversarialQA and by the category in the Czech SQAD and used the examples of the same cluster as the demonstrations
+of the task in training. 
+
+We find that the specific algorithm of selection of these demonstrations makes a big difference in the model's ability to extrapolate 
+to new tasks and will be shared in the following article; stay tuned!
+
+For the Czech SQAD 3.0, original contexts (=whole Wikipedia websites) were limited to a maximum of 8000 characters
+per a sequence of prime demonstrations.
+Pre-processing script for Czech SQAD is available [here](https://huggingface.co/gaussalgo/xlm-roberta-large_extractive-QA_en-cs/blob/main/parse_czech_squad.py).
+
+
+For training the model (and hence intended also for the inference), we've used the following patterns of 2-7 demonstrations:
+
+For English samples:
+
+*input*: 
+```
+Question: {Q1} Context: {C1} Answer: {A1}, 
+Question: {Q2} Context: {C2} Answer: {A2}, 
+[...possibly more demonstrations...] 
+
+Question: {Q} Context: {C} Answer:`
+```
+=> *target*: 
+```
+{A}
+```
+
+For Czech samples:
+
+*input*: 
+```
+Otázka: {Q1} Kontext: {C1} Odpověď: {A1}, 
+Otázka: {Q2} Kontext: {C2} Odpověď: {A2}, 
+[...possibly more demonstrations...] 
+
+Otázka: {Q} Kontext: {C} Odpověď:`
+```
+=> *target*: 
+```
+{A}
+```
+
+
+The best checkpoint was picked to maximize the model's zero-shot performance on unseen Named Entity Recognition 
+from the out-of-distribution domain of texts and labels.
+
+## Intended uses & limitations
+
+This model is purposed for a few-shot application on any text extraction task in English and Czech, where the prompt can be stated
+as a natural question. E.g. to use this model for extracting the entities of customer names from the text, 
+prompt it with demonstrations in the following format: 
+ 
+```python
+input_text = """
+    Question: What is the customer's name? 
+    Context: Origin: Barrack Obama, Customer id: Bill Moe. 
+    Answer: Bill Moe, 
+    Question: What is the customer's name? 
+    Context: Customer id: Barrack Obama, if not deliverable, return to Bill Clinton. 
+    Answer:"""
+```
+
+Note that despite its size, English AdversarialQA 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-priming-QA_en-cs")
+model = AutoModelForSeq2SeqLM.from_pretrained("gaussalgo/mt5-base-priming-QA_en-cs")
+
+# For the expected format of input_text, see Intended use above
+inputs = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**inputs)
+
+print("Answer:")
+print(tokenizer.decode(outputs))
+```