Update README.md

README.md

@@ -25,4 +25,54 @@ Evaluated on held-out test set from XQuAD
 "exact_match": 79.44756554307116,
 "f1": 89.79318021513376,
 "test_samples": 2307
-```
+```
+
+# Usage
+
+## In Transformers
+```python
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+
+model_name = "alon-albalak/xlm-roberta-base-xquad"
+
+# a) Get predictions
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+QA_input = {
+    'question': 'Why is model conversion important?',
+    'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
+}
+res = nlp(QA_input)
+
+# b) Load model & tokenizer
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+
+## In FARM
+```python
+from farm.modeling.adaptive_model import AdaptiveModel
+from farm.modeling.tokenization import Tokenizer
+from farm.infer import QAInferencer
+
+model_name = "alon-albalak/xlm-roberta-base-xquad"
+
+# a) Get predictions
+nlp = QAInferencer.load(model_name)
+QA_input = [{"questions": ["Why is model conversion important?"],
+             "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}]
+res = nlp.inference_from_dicts(dicts=QA_input, rest_api_schema=True)
+
+# b) Load model & tokenizer
+model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering")
+tokenizer = Tokenizer.load(model_name)
+```
+
+## In Haystack
+
+```python
+reader = FARMReader(model_name_or_path="alon-albalak/xlm-roberta-base-xquad")
+# or 
+reader = TransformersReader(model="alon-albalak/xlm-roberta-base-xquad",tokenizer="alon-albalak/xlm-roberta-base-xquad")
+```
+
+Usage instructions for FARM and Haystack were adopted from https://huggingface.co/deepset/xlm-roberta-large-squad2