README.md

---
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 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: "Spanish"
metrics:
- f1
- precision
- recall

---
<img src="https://public.3.basecamp.com/p/rs5XqmAuF1iEuW6U7nMHcZeY/upload/download/VL-NLP-short.png" alt="logo voicelab nlp" style="width:300px;"/>

# 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](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
- **Language model:** [t5-base](https://huggingface.co/t5-base)   
- **Language:** pl, en (but works relatively well with others)
- **Training data:** POSMAC
- **Online Demo:** [https://nlp-demo-1.voicelab.ai/](https://nlp-demo-1.voicelab.ai/)
- **Paper:** [Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer, ACIIDS 2022](TBA)

# 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 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

```python
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](https://voicelab.ai/contact/).