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vlt5-base-keywords

Text2Text Generation
Transformers
PyTorch
Safetensors
Polish
English
t5
keywords-generation
text-classifiation
other
text-generation-inference
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vlt5-base-keywords / README.md
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metadata
license: cc-by-4.0
language:
  - pl
  - en
datasets:
  - posmac
pipeline_tag: text2text-generation
tags:
  - keywords-generation
  - text-classifiation
  - other
widget:
  - text: >-
      Keywords: Our vlT5 model is a keyword generation model based on
      encoder-decoder architecture using Transformer blocks presented by google
      (https://huggingface.co/t5-base). The vlT5 was trained on scientific
      articles corpus to predict a given set of keyphrases based on the
      concatenation of the article’s abstract and title. It generates precise,
      yet not always complete keyphrases that describe the content of the
      article based only on the abstract.
    example_title: English 1
  - text: >-
      Keywords: Decays the learning rate of each parameter group by gamma every
      step_size epochs. Notice that such decay can happen simultaneously with
      other changes to the learning rate from outside this scheduler. When
      last_epoch=-1, sets initial lr as lr.
    example_title: English 2
  - text: >-
      Keywords: Przełomem w dziedzinie sztucznej inteligencji i maszynowego
      uczenia się było powstanie systemu eksperckiego Dendral na Uniwersytecie
      Stanforda w 1965. System ten powstał w celu zautomatyzowania analizy i
      identyfikacji molekuł związków organicznych, które dotychczas nie były
      znane chemikom. Wyniki badań otrzymane dzięki systemowi Dendral były
      pierwszym w historii odkryciem dokonanym przez komputer, które zostały
      opublikowane w prasie specjalistycznej.
    example_title: Polish
  - text: >-
      Keywords: El análisis de un economista calcula que, a pesar del aumento
      del gasto general, la Navidad es una pérdida de peso muerto según la
      teoría microeconómica ortodoxa, debido al efecto de dar regalos. Esta
      pérdida se calcula como la diferencia entre lo que el donante gastó en el
      artículo y lo que el receptor del regalo habría pagado por el artículo. Se
      estima que en 2001, Navidad resultó en una pérdida de peso muerto de $ 4
      mil millones solo en los EE. UU.1​ Debido a factores de complicación, este
      análisis se utiliza a veces para discutir posibles fallas en la teoría
      microeconómica actual. Otras pérdidas de peso muerto incluyen los efectos
      de la Navidad en el medio ambiente y el hecho de que los regalos
      materiales a menudo se perciben como elefantes blancos, lo que impone
      costos de mantenimiento y almacenamiento y contribuye al desorden.
    example_title: Spanish
metrics:
  - f1
  - precision
  - recall
logo voicelab nlp

Keyword Extraction from Short Texts with T5

Our vlT5 model is a keyword generation model based on encoder-decoder architecture using Transformer blocks presented by Google (https://huggingface.co/t5-base). The vlT5 was trained on scientific articles corpus to predict a given set of keyphrases based on the concatenation of the article’s abstract and title. It generates precise, yet not always complete keyphrases that describe the content of the article based only on the abstract.

Keywords generated with vlT5-base-keywords: encoder-decoder architecture, keyword generation

vlT5

The biggest advantage is the transferability of the vlT5 model, as it works well on all domains and types of text. The downside is that the text length and the number of keywords are similar to the training data: the text piece of an abstract length generates approximately 3 to 5 keywords. It works both extractive and abstractively. Longer pieces of text must be split into smaller chunks, and then propagated to the model.

Overview

Corpus

The model was trained on a POSMAC corpus. Polish Open Science Metadata Corpus (POSMAC) is a collection of 216,214 abstracts of scientific publications compiled in the CURLICAT project.

Domains Documents With keywords
Engineering and technical sciences 58 974 57 165
Social sciences 58 166 41 799
Agricultural sciences 29 811 15 492
Humanities 22 755 11 497
Exact and natural sciences 13 579 9 185
Humanities, Social sciences 12 809 7 063
Medical and health sciences 6 030 3 913
Medical and health sciences, Social sciences 828 571
Humanities, Medical and health sciences, Social sciences 601 455
Engineering and technical sciences, Humanities 312 312

Tokenizer

As in the original plT5 implementation, the training dataset was tokenized into subwords using a sentencepiece unigram model with vocabulary size of 50k tokens.

Usage 

from transformers import T5Tokenizer, T5ForConditionalGeneration

vlt5 = T5ForConditionalGeneration.from_pretrained("Voicelab/t5-base-keywords")
tokenizer = T5Tokenizer.from_pretrained("Voicelab/t5-base-keywords")

task_prefix = "Keywords: "
inputs = ["Christina Katrakis, who spoke to the BBC from Vorokhta in western Ukraine, relays the account of one family, who say Russian soldiers shot at their vehicles while they were leaving their village near Chernobyl in northern Ukraine. She says the cars had white flags and signs saying they were carrying children.",
     "Decays the learning rate of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.",
    "Hello, I'd like to order a pizza with salami topping."]

for sample in inputs:
           input_sequences = [task_prefix + sample]
        input_ids = tokenizer(input_sequences, return_tensors='pt', truncation=True).input_ids
        output = model.generate(input_ids, no_repeat_ngram_size=3, num_beams=4)
        predicted = tokenizer.decode(output[0], skip_special_tokens=True)
        print(sample, "\n --->", predicted)

Inference

Our results showed that the best generation results were achieved with no_repeat_ngram_size=3, num_beams=4

Results

Method Rank Micro Macro
P R F1 P R F1
extremeText 1 0.175 0.038 0.063 0.007 0.004 0.005
3 0.117 0.077 0.093 0.011 0.011 0.011
5 0.090 0.099 0.094 0.013 0.016 0.015
10 0.060 0.131 0.082 0.015 0.025 0.019
vlT5kw 1 0.345 0.076 0.124 0.054 0.047 0.050
3 0.328 0.212 0.257 0.133 0.127 0.129
5 0.318 0.237 0.271 0.143 0.140 0.141
KeyBERT 1 0.030 0.007 0.011 0.004 0.003 0.003
3 0.015 0.010 0.012 0.006 0.004 0.005
5 0.011 0.012 0.011 0.006 0.005 0.005
TermoPL 1 0.118 0.026 0.043 0.004 0.003 0.003
3 0.070 0.046 0.056 0.006 0.005 0.006
5 0.051 0.056 0.053 0.007 0.007 0.007
all 0.025 0.339 0.047 0.017 0.030 0.022
extremeText 1 0.210 0.077 0.112 0.037 0.017 0.023
3 0.139 0.152 0.145 0.045 0.042 0.043
5 0.107 0.196 0.139 0.049 0.063 0.055
10 0.072 0.262 0.112 0.041 0.098 0.058
vlT5kw 1 0.377 0.138 0.202 0.119 0.071 0.089
3 0.361 0.301 0.328 0.185 0.147 0.164
5 0.357 0.316 0.335 0.188 0.153 0.169
KeyBERT 1 0.018 0.007 0.010 0.003 0.001 0.001
3 0.009 0.010 0.009 0.004 0.001 0.002
5 0.007 0.012 0.009 0.004 0.001 0.002
TermoPL 1 0.076 0.028 0.041 0.002 0.001 0.001
3 0.046 0.051 0.048 0.003 0.001 0.002
5 0.033 0.061 0.043 0.003 0.001 0.002
all 0.021 0.457 0.040 0.004 0.008 0.005

License

CC BY 4.0

Citation

If you use this model, please cite the following paper:

Piotr Pęzik, Agnieszka Mikołajczyk-Bareła, Adam Wawrzyński, Bartłomiej Nitoń, Maciej Ogrodniczuk, Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer, ACIIDS 2022

Authors

The model was trained by NLP Research Team at Voicelab.ai.

You can contact us here.