metadata

license: cc-by-4.0
language:
  - pl
  - en
datasets:
  - Curlicat
pipeline_tag: text-classification
tags:
  - keywords-generation
  - text-classifiation
  - other
widget:
  - text: >-
      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: Keywords generation (English)
  - text: >-
      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: Keywords generation (Polish)
  - text: >-
      El Padrão real (traducible al español como Patrón real) era una obra
      cartográfica de origen portugués producida secretamente y mantenida por la
      organización de la corte real en el siglo XVI. La obra estaba disponible
      para la élite científica de la época, siendo expuesta en la Casa da Índia
      (Casa de la India). En el Padrão real se añadieron constantemente los
      nuevos descubrimientos de los portugueses. El primer Padrão real fue
      producido en la época de Enrique el Navegante, antes de la existencia de
      la Casa de la India. 
    example_title: Keywords generation (Spanish)

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 model's input is text preceded by a prefix, and the output is the target text, where the prefix defines the type of task: e.g. "Translate from Polish to English:". 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.

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.

Corpus

The model was trained on a curlicat corpus

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 HerBERT implementation, the training dataset was tokenized into subwords using a character level byte-pair encoding (CharBPETokenizer) with a vocabulary size of 50k tokens. The tokenizer itself was trained with a tokenizers library.

We kindly encourage you to use the Fast version of the tokenizer, namely HerbertTokenizerFast.

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)

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
plT5kw	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
plT5kw	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.