Update README.md

README.md

@@ -12,7 +12,40 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# ner-news-t5-large
+# NER using T5-large on small dataset
+
+Simple experimental model that was trained in 3 epochs on very small dataset (~100 examples)
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, NerPipeline
+
+model = AutoModelForTokenClassification.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("imvladikon/t5-english-ner", trust_remote_code=True)
+
+pipe = NerPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
+print(pipe("London is the capital city of England and the United Kingdom"))
+"""
+[{'entity_group': 'LOCATION',
+  'score': 0.84536326,
+  'word': 'London',
+  'start': 0,
+  'end': 6},
+ {'entity_group': 'LOCATION',
+  'score': 0.8957489,
+  'word': 'England',
+  'start': 30,
+  'end': 37},
+ {'entity_group': 'LOCATION',
+  'score': 0.73186326,
+  'word': 'UnitedKingdom',
+  'start': 46,
+  'end': 60}]
+"""
+```
+
+
 
 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the private(en) dataset.
 It achieves the following results on the evaluation set: