Create README.md

README.md

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+---
+language: 
+- en
+- es
+- eu
+datasets:
+- squad
+---
+
+# Description
+
+This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD version 1.1, that is able to answer basic factual questions in English, Spanish and Basque. It extracts the span of text in which the answer is found.
+
+
+### Outputs
+
+The model predicts a span of text from the context and a score for the probability for that span to be the correct answer:
+
+* Toxic: the tweet has at least some degree of toxicity.
+* Very Toxic: the tweet has a strong degree of toxicity.
+
+
+### How to use
+
+The model can be used directly with a *question-answering* pipeline:
+
+```python
+>>> from transformers import pipeline
+>>> context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
+>>> question = "When was Florence Nightingale born?"
+>>> qa = pipeline("question-answering", model="MarcBrun/ixambert-finetuned-squad")
+>>> qa(question=question,context=context)
+{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
+```
+
+%### Training procedure
+%The pre-trained model was fine-tuned for question answering using the following hyperparameters, which were selected from a validation set:
+
+%* Batch size = 32
+%* Learning rate = 2e-5
+%* Epochs = 3
+
+%The optimizer used was AdamW and the loss optimized was binary cross-entropy with class weights proportional to the class imbalance.