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mrm8488/t5-base-finetuned-e2m-intent mrm8488/t5-base-finetuned-e2m-intent
60 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
156 models

How to use this model directly from the πŸ€—/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-e2m-intent") model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-e2m-intent")

T5-base fine-tuned on event2Mind for Intent Prediction πŸ€”

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

Details of T5 πŸ“œ ➑️ πŸ“œ

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 β€œColossal 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.

model image

Details of the downstream task (Intent Prediction) - Dataset πŸ“š

Dataset ID: event2Mind from HugginFace/NLP

Dataset Split # samples
event2Mind train 46472
event2Mind valid 1960

Events without intent were not used!

Check out more about this dataset and others in NLP Viewer

Model fine-tuning πŸ‹οΈβ€

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

Model in Action πŸš€

# 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