Update README.md

README.md

@@ -82,6 +82,14 @@ Evaluated on German [XQuAD: xquad.de.json](https://github.com/deepmind/xquad)
 
 ## Usage
 
+### In Haystack
+For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
+```python
+reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2")
+# or 
+reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2")
+```
+
 ### In Transformers
 ```python
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
@@ -101,35 +109,6 @@ 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 = "deepset/xlm-roberta-large-squad2"
-
-# 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
-For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
-```python
-reader = FARMReader(model_name_or_path="deepset/xlm-roberta-large-squad2")
-# or 
-reader = TransformersReader(model="deepset/xlm-roberta-large-squad2",tokenizer="deepset/xlm-roberta-large-squad2")
-```
-
-
 ## Authors
 **Branden Chan:** branden.chan@deepset.ai    
 **Timo Möller:** timo.moeller@deepset.ai