Update README.md

README.md

@@ -1,7 +1,49 @@
-Use in Transformers:
+---
 
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+datasets:
 
-tokenizer = AutoTokenizer.from_pretrained("Suva/uptag-url-model")
+- arxiv
 
-model = AutoModelForSeq2SeqLM.from_pretrained("Suva/uptag-url-model")
+
+widget:
+
+- text: "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production 
+machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and 
+handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. 
+In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, 
+Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. 
+In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 
+1.7-2.9 times versus production systems."
+            
+license: mit
+---
+
+# T5 One Line Summary
+A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using [simpleT5] library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 models
+
+## Usage:
+```python
+abstract = """We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production 
+machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and 
+handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a 
+set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. 
+In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, 
+Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, 
+Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
+"""
+```
+### Using Transformers🤗
+```python
+model_name = "Suva/uptag-url-model"
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+input_ids = tokenizer.encode("summarize: " + abstract, return_tensors="pt", add_special_tokens=True)
+generated_ids = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=3)
+preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
+print(preds)
+# output
+["Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers",
+ "Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems",
+ "Overton: Building, Monitoring, and Improving Production Machine Learning Systems"]
+ ```