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-*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,63 +1,55 @@
----
+# 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
 
-datasets:
+## Usage
-
-- 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)
+
-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)
+abstract = """We describe a system called Overton, whose main design goal is to 
-preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
+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.
+"""
+
+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"]
- ```
-### Using simpleT5⚡️
-```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',
-"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']
  ```