Create README.md

README.md

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+---
+metrics:
+- accuracy
+- bleu
+widget:
+  - text: 19, asbury place,mason city, iowa, 50401, us
+    example_title: Adress 1
+  - text: 1429, birch drive, mason city, iowa, 50401, us
+    example_title: Adress 2
+---
+
+# Address Standardization and Correction Model
+
+This model is [t5-base](https://huggingface.co/t5-base) fine-tuned to transform incorrect and non-standard addresses into standardized addresses.
+
+
+## How to use the model
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Hnabil/t5-address-standardizer")
+tokenizer = AutoTokenizer.from_pretrained("Hnabil/t5-address-standardizer")
+
+inputs = tokenizer("220, soyth rhodeisland aveune, mason city, iowa, 50401, us", return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+
+# ['220, s rhode island ave, mason city, ia, 50401, us']
+```
+
+## Training data
+
+The model has been trained on data from [openaddresses.io](https://openaddresses.io/). 
+