T5-base fine-tuned on event2Mind for Intent Prediction ?

Google's T5 fine-tuned on event2Mind dataset for Intent Prediction .

Details of T5 ? ➡️ ?

The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in Here the abstract:

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

Details of the downstream task (Intent Prediction) - Dataset ?

Dataset ID: event2Mind from Huggingface/NLP

Events without intent were not used!

Check out more about this dataset and others in NLP Viewer

Model fine-tuning ?️‍

The training script is a slightly modified version of this awesome one by Suraj Patil .

Model in Action ?

# Tip: By now, install transformers from source

from transformers import AutoModelWithLMHead, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-e2m-intent")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-e2m-intent")

def get_intent(event, max_length=16):
  input_text = "%s </s>" % event
  features = tokenizer([input_text], return_tensors='pt')

  output = model.generate(input_ids=features['input_ids'], 
               attention_mask=features['attention_mask'],
               max_length=max_length)

  return tokenizer.decode(output[0])

event = "PersonX takes PersonY home"
get_intent(event)

# output: 'to be helpful'

Created by Manuel Romero/@mrm8488 | LinkedIn Made with ♥ in Spain