README.md

---
language: en
tags:
- Clsssification
license: apache-2.0
datasets:
- tensorflow
- numpy
- keras
- pandas
- openpyxl
- gensin
- contractions
- nltk
- spacy
thumbnail: https://github.com/Marcosdib/S2Query/Classification_Architecture_model.png
---

![MCTIimg](https://antigo.mctic.gov.br/mctic/export/sites/institucional/institucional/entidadesVinculadas/conselhos/pag-old/RODAPE_MCTI.png)


# MCTI Text Classification Task (uncased) DRAFT

Disclaimer: 

## According to the abstract,

Text classification is a traditional problem in Natural Language Processing (NLP). Most of the state-of-the-art implementations
require high-quality, voluminous, labeled data. Pre- trained models on large corpora have shown beneficial for text classification
and other NLP tasks, but they can only take a limited amount of symbols as input. This is a real case study that explores 
different machine learning strategies to classify a small amount of long, unstructured, and uneven data to find a proper method
with good performance. The collected data includes texts of financing opportunities the international R&D funding organizations
provided on theirwebsites. The main goal is to find international R&D funding eligible for Brazilian researchers, sponsored by
the Ministry of Science, Technology and Innovation. We use pre-training and word embedding solutions to learn the relationship
of the words from other datasets with considerable similarity and larger scale. Then, using the acquired features, based on the
available dataset from MCTI, we apply transfer learning plus deep learning models to improve the comprehension of each sentence.
Compared to the baseline accuracy rate of 81%, based on the available datasets, and the 85% accuracy rate achieved through a 
Transformer-based approach, the Word2Vec-based approach improved the accuracy rate to 88%. The research results serve as 
asuccessful case of artificial intelligence in a federal government application.

This model focus on a more specific problem, creating a Research Financing Products Portfolio (FPP) outside ofthe Union budget,
supported by the Brazilian Ministry of Science, Technology, and Innovation (MCTI). It was introduced in ["Using transfer learning to classify long unstructured texts with small amounts of labeled data"](https://www.scitepress.org/Link.aspx?doi=10.5220/0011527700003318) and first released in
[this repository](https://huggingface.co/unb-lamfo-nlp-mcti). This model is uncased: it does not make a difference between english
and English.

## Model description

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nisi nec pulvinar suscipit, dolor nulla sagittis massa, et vehicula ante felis quis nibh. Lorem ipsum dolor sit amet,
consectetur adipiscing elit. Maecenas viverra tempus risus non ornare. Donec in vehicula est. Pellentesque vulputate 
bibendum cursus. Nunc volutpat vitae neque ut bibendum:

- Nullam congue hendrerit turpis et facilisis. Cras accumsan ante mi, eu hendrerit nulla finibus at. Donec imperdiet, 
  nisi nec pulvinar suscipit, dolor nulla sagittis massa, et vehicula ante felis quis nibh. Lorem ipsum dolor sit amet,
  consectetur adipiscing elit.
- Nullam congue hendrerit turpis et facilisis. Cras accumsan ante mi, eu hendrerit nulla finibus at. Donec imperdiet, 
  nisi nec pulvinar suscipit, dolor nulla sagittis massa, et vehicula ante felis quis nibh. Lorem ipsum dolor sit amet,
  consectetur adipiscing elit.

Nullam congue hendrerit turpis et facilisis. Cras accumsan ante mi, eu hendrerit nulla finibus at. Donec imperdiet, 
nisi nec pulvinar suscipit, dolor nulla sagittis massa, et vehicula ante felis quis nibh. Lorem ipsum dolor sit amet,
consectetur adipiscing elit. Maecenas viverra tempus risus non ornare. Donec in vehicula est. Pellentesque vulputate 
bibendum cursus. Nunc volutpat vitae neque ut bibendum.

![architeru](https://github.com/marcosdib/S2Query/Classification_Architecture_model.png)

## Model variations

With the motivation to increase accuracy obtained with baseline implementation, we implemented a transfer learning 
strategy under the assumption that small data available for training was insufficient for adequate embedding training. 
In this context, we considered two approaches: 

   i) pre-training wordembeddings using similar datasets for text classification;
   ii) using transformers and attention mechanisms (Longformer) to create contextualized embeddings.

XXXX has originally been released in base and large variations, for cased and uncased input text. The uncased models 
also strips out an accent markers. Chinese and multilingual uncased and cased versions followed shortly after.  
Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of 
two models.  

Other 24 smaller models are released afterward.  

The detailed release history can be found on the [here](https://huggingface.co/unb-lamfo-nlp-mcti) on github.

#### Table 1:
| Model                        | #params | Language |
|------------------------------|:-------:|:--------:|
| [`mcti-base-uncased`]        | 110M    | English  |
| [`mcti-large-uncased`]       | 340M    | English  | 
| [`mcti-base-cased`]          | 110M    | English  |
| [`mcti-large-cased`]         | 110M    | Chinese  |
| [`-base-multilingual-cased`] | 110M    | Multiple |

#### Table 2:
| Dataset                              | Compatibility to base* |
|--------------------------------------|:----------------------:|
| Labeled MCTI                         | 100%                   |
| Full MCTI                            | 100%                   |
| BBC News Articles                    | 56.77%                 |
| New unlabeled MCTI                   | 75.26%                 |


## Intended uses

You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://www.google.com) to look for
fine-tuned versions of a task that interests you.

Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like XXX.

### How to use

You can use this model directly with a pipeline for masked language modeling:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")

[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
  'score': 0.1073106899857521,
  'token': 4827,
  'token_str': 'fashion'},
 {'sequence': "[CLS] hello i'm a fine model. [SEP]",
  'score': 0.027095865458250046,
  'token': 2986,
  'token_str': 'fine'}]
```

Here is how to use this model to get the features of a given text in PyTorch:

```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```

and in TensorFlow:

```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```

### Limitations and bias

Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:

```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")

[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
  'score': 0.09747550636529922,
  'token': 10533,
  'token_str': 'carpenter'},
 {'sequence': '[CLS] the man worked as a salesman. [SEP]',
  'score': 0.037680890411138535,
  'token': 18968,
  'token_str': 'salesman'}]

>>> unmasker("The woman worked as a [MASK].")

[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
  'score': 0.21981462836265564,
  'token': 6821,
  'token_str': 'nurse'},
 {'sequence': '[CLS] the woman worked as a cook. [SEP]',
  'score': 0.03042375110089779,
  'token': 5660,
  'token_str': 'cook'}]
```

This bias will also affect all fine-tuned versions of this model.

## Training data

The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).


## Training procedure

### Preprocessing

Pre-processing was used to standardize the texts for the English language, reduce the number of insignificant tokens and
optimize the training of the models.

The following assumptions were considered:
- The Data Entry base is obtained from the result of goal 4.
- Labeling (Goal 4) is considered true for accuracy measurement purposes;
- Preprocessing experiments compare accuracy in a shallow neural network (SNN);
- Pre-processing was investigated for the classification goal.

From the Database obtained in Meta 4, stored in the project's [GitHub](https://github.com/mcti-sefip/mcti-sefip-ppfcd2020/blob/scraps-desenvolvimento/Rotulagem/db_PPF_validacao_para%20UNB_%20FINAL.xlsx), a Notebook was developed in [Google Colab](https://colab.research.google.com) 
to implement the [pre-processing code](https://github.com/mcti-sefip/mcti-sefip-ppfcd2020/blob/pre-processamento/Pre_Processamento/MCTI_PPF_Pr%C3%A9_processamento.ipynb), which also can be found on the project's GitHub.

Several Python packages were used to develop the preprocessing code:

#### Table 3: Python packages used
|                         Objective                      |   Package    |
|--------------------------------------------------------|--------------|
| Resolve contractions and slang usage in text           | [contractions](https://pypi.org/project/contractions) |
| Natural Language Processing                            | [nltk](https://pypi.org/project/nltk)         |
| Others data manipulations and calculations included in Python 3.10: io, json, math, re (regular expressions), shutil, time, unicodedata;    | [numpy](https://pypi.org/project/numpy)        |
| Data manipulation and analysis                         | [pandas](https://pypi.org/project/pandas)       |
| http library                                           | [requests](https://pypi.org/project/requests)     |
| Training model                                         | [scikit-learn](https://pypi.org/project/scikit-learn) |
| Machine learning                                       | [tensorflow](https://pypi.org/project/tensorflow)   |
| Machine learning                                       | [keras](https://keras.io/)        |
| Translation from multiple languages to English         | [translators](https://pypi.org/project/translators)  |


As detailed in the notebook on [GitHub](https://github.com/mcti-sefip/mcti-sefip-ppfcd2020/blob/pre-processamento/Pre_Processamento/MCTI_PPF_Pr%C3%A9_processamento), in the pre-processing, code was created to build and evaluate 8 (eight) different 
bases, derived from the base of goal 4, with the application of the methods shown in Figure 2.

#### Table 4: Preprocessing methods evaluated
|  id    |                   Experiments                                          |
|--------|------------------------------------------------------------------------|
| Base   | Original Texts                                                         |
| xp1    | Expand Contractions                                                    |
| xp2    | Expand Contractions + Convert text to lowercase                        |
| xp3    | Expand Contractions + Remove Punctuation                               |
| xp4    | Expand Contractions + Remove Punctuation + Convert text to lowercase   |
| xp5    | xp4 + Stemming                                                         |
| xp6    | xp4 + Lemmatization                                                    |
| xp7    | xp4 + Stemming + Stopwords Removal                                     |
| xp8    | ap4 + Lemmatization + Stopwords Removal                                |

First, the treatment of punctuation and  capitalization was evaluated. This phase  resulted in the construction and  
evaluation  of the first four bases (xp1, xp2, xp3, xp4).

Then, the content simplification was evaluated, from the xp4 base, considering stemming (xp5),  stemming (xp6), 
stemming + stopwords removal (xp7), and stemming + stopwords removal (xp8).

All eight bases were evaluated to classify the  eligibility of the opportunity, through the  training of a shallow 
neural network  (SNN – Shallow Neural Network).  The metrics for the eight bases were evaluated. The results are        
shown in Table 5.

#### Table 5: Results obtained in Preprocessing
|  id    |                   Experiment                                           | acurácia | f1-score | recall | precision | Média(s) | N_tokens | max_lenght |
|--------|------------------------------------------------------------------------|----------|----------|--------|-----------|----------|----------|------------|
| Base   | Original Texts                                                         |  89,78%  |  84,20%  | 79,09% |   90,95%  |  417,772 |   23788  |   5636     |
| xp1    | Expand Contractions                                                    |  88,71%  |  81,59%  | 71,54% |   97,33%  |  414,715 |   23768  |   5636     |
| xp2    | Expand Contractions + Convert text to lowercase                        |  90,32%  |  85,64%  | 77,19% |   97,44%  |  368,375 |   20322  |   5629     |
| xp3    | Expand Contractions + Remove Punctuation                               |  91,94%  |  87,73%  | 79,66% |   98,72%  |  386,650 |   22121  |   4950     |
| xp4    | Expand Contractions + Remove Punctuation + Convert text to lowercase   |  90,86%  |  86,61%  | 80,85% |   94,25%  |  326,830 |   18616  |   4950     |
| xp5    | xp4 + Stemming                                                         |  91,94%  |  87,68%  | 78,47% |  100,00%  |  257,960 |   14319  |   4950     |
| xp6    | xp4 + Lemmatization                                                    |  89,78%  |  85,06%  | 79,66% |   91,87%  |  282,645 |   16194  |   4950     |
| xp7    | xp4 + Stemming + Stopwords Removal                                     |  92,47%  |  88,46%  | 79,66% |  100,00%  |  210,320 |   14212  |   2817     |
| xp8    | ap4 + Lemmatization + Stopwords Removal                                |  92,47%  |  88,46%  | 79,66% |  100,00%  |  225,580 |   16081  |   2726     |

Even so, between these two excellent options, one can judge which one to choose. XP7: It has less training time, 
less number of unique tokens. XP8: It has smaller maximum sizes. In this case, the criterion used for the choice 
was the computational cost required to train the vector representation models (word-embedding, sentence-embeddings, 
document-embedding). The training time is so close that it did not have such a large weight for the analysis.

As a last step, a spreadsheet was generated for the model (xp8) with the fields opo_pre and opo_pre_tkn, containing the preprocessed text in sentence format and tokens, respectively. This [database](https://github.com/mcti-sefip/mcti-sefip-ppfcd2020/blob/pre-processamento/Pre_Processamento/oportunidades_final_pre_processado.xlsx) was made 
available on the project's GitHub with the inclusion of columns opo_pre (text) and opo_pre_tkn (tokenized). 

### Pretraining

The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.

## Evaluation results

### Model training with Word2Vec embeddings

Now we have a pre-trained model of word2vec embeddings that has already learned relevant meaningsfor our classification problem.
We can couple it to our classification models (Fig. 4), realizing transferlearning and then training the model with the labeled
data in a supervised manner. The new coupled model can be seen in Figure 5 under word2vec model training. The Table 3 shows the
obtained results with related metrics. With this implementation, we achieved new levels of accuracy with 86% for the CNN
architecture and 88% for the LSTM architecture.

#### Table 6: Results from Pre-trained WE + ML models
| ML Model |  Accuracy | F1 Score  | Precision |   Recall  |
|:--------:|:---------:|:---------:|:---------:|:---------:|
| NN       |  0.8269   |  0.8545   |  0.8392   |  0.8712   |
| DNN      |  0.7115   |  0.7794   |  0.7255   |  0.8485   |
| CNN      |  0.8654   |  0.9083   |  0.8486   |  0.9773   |
| LSTM     |  0.8846   |  0.9139   |  0.9056   |  0.9318   |

### Transformer-based implementation

Another way we used pre-trained vector representations was by use of a Longformer (Beltagy et al., 2020). We chose it because
of the limitation of the first generation of transformers and BERT-based architectures involving the size of the sentences:
the maximum of 512 tokens. The reason behind that limitation is that the self-attention mechanism scale quadratically with the
input sequence length O(n2) (Beltagy et al., 2020). The Longformer allowed the processing sequences of a thousand characters
without facing the memory bottleneck of BERT-like architectures and achieved SOTA in several benchmarks.

For our text length distribution in Figure 3, if we used a Bert-based architecture with a maximum length of 512, 99 sentences
would have to be truncated and probably miss some critical information. By comparison, with the Longformer, with a maximum 
length of 4096, only eight sentences will have their information shortened.

To apply the Longformer, we used the pre-trained base (available on the link) that was previously trained with a combination
of vast datasets as input to the model, as shown in figure 5 under Longformer model training. After coupling to our classification
models, we realized supervised training of the whole model. At this point, only transfer learning was applied since more 
computational power was needed to realize the fine-tuning of the weights. The results with related metrics can be viewed in table 4.
This approach achieved adequate accuracy scores, above 82% in all implementation architectures.

#### Table 7: Results from Pre-trained Longformer + ML models
| ML Model |  Accuracy | F1 Score  | Precision |   Recall  |
|:--------:|:---------:|:---------:|:---------:|:---------:|
| NN       |  0.8269   |  0.8754   |0.7950     |  0.9773   |
| DNN      |  0.8462   |  0.8776   |0.8474     |  0.9123   |
| CNN      |  0.8462   |  0.8776   |0.8474     |  0.9123   |
| LSTM     |  0.8269   |  0.8801   |0.8571     |  0.9091   | 


## Checkpoints
- Examples
- Implementation Notes
- Usage Example
- >>>
- >>> ...


## Config

## Tokenizer

## Training data

## Training procedure

## Preprocessing

## Pretraining

## Evaluation results
## Benchmarks

### BibTeX entry and citation info

```bibtex
@conference{webist22,
author       ={Carlos Rocha. and Marcos Dib. and Li Weigang. and Andrea Nunes. and Allan Faria. and Daniel Cajueiro.
               and Maísa {Kely de Melo}. and Victor Celestino.},
title        ={Using Transfer Learning To Classify Long Unstructured Texts with Small Amounts of Labeled Data},
booktitle    ={Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST,},
year         ={2022},
pages        ={201-213},
publisher    ={SciTePress},
organization ={INSTICC},
doi          ={10.5220/0011527700003318},
isbn         ={978-989-758-613-2},
issn         ={2184-3252},
}
```

<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>