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 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,55 +1,63 @@
-# 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
 
-## Usage
+datasets:
+
+- arxiv
+
+
+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](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 models
+
+## Usage:[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HrfT8IKLXvZzPFpl1EhZ3s_iiXG3O2VY?usp=sharing)
+```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 = "snrspeaks/t5-one-line-summary"
 
 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)
-abstract = """We describe a system called Overton, whose main design goal is to 
+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)
-support engineers in building, monitoring, and improving production machine learning systems. 
+preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
-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.
-"""
-
-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: 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"]
- 'Overton: A System for Building, Monitoring, and Improving Production Machine Learning Systems',
+ ```
-
+### Using simpleT5⚡️
- 'Overton: Building, Monitoring, and Improving Production Machine Learning Systems']
+```python
+# pip install --upgrade simplet5
+from simplet5 import SimpleT5
+model = SimpleT5()
+model.load_model("t5","snrspeaks/t5-one-line-summary")
+model.predict(abstract)
+
+# output
+"Overton: Building, Deploying, and Monitoring Machine Learning Systems for Engineers"
 ```