baseok

README.md

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 ---
+
+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)
+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"
+```

config.json

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+{
+  "_name_or_path": "t5-base",
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "relu",
+  "gradient_checkpointing": false,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "transformers_version": "4.6.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

pytorch_model.bin

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special_tokens_map.json

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spiece.model

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tokenizer_config.json

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