added T5-One-line-summary

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Shivanand Roy 2021-06-19 01:13:40 +05:30
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# 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
```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)
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.
"""
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']
```

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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
}

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