Update README.md

README.md

@@ -59,23 +59,24 @@ bibendum cursus. Nunc volutpat vitae neque ut bibendum.
 
 ## Model variations
 
-With the motivation to increase accuracy obtained with baseline implementation, we implemented a transfer learning 
+With the motivation to increase accuracy obtained with baseline implementation, was 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: 
+In this context, was considered two approaches: 
 
-   i) pre-training wordembeddings using similar datasets for text classification;
+   i) pre-training word embeddings 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:
+Os modelos que utilizam Word2Vec e Longformer também precisam ser carregados e seus pesos são os seguintes:
+
+Longformer: 10.88 GB
+
+Word2Vec: 56.1 MB
+
+Table 1:
 | Model                        | #params | Language |
 |------------------------------|:-------:|:--------:|
 | [`mcti-base-uncased`]        | 110M    | English  |
@@ -84,8 +85,8 @@ The detailed release history can be found on the [here](https://huggingface.co/u
 | [`mcti-large-cased`]         | 110M    | Chinese  |
 | [`-base-multilingual-cased`] | 110M    | Multiple |
 
-#### Table 2:
-| Dataset                              | Compatibility to base* |
+Table 2: Compatibility results (*base = labeled MCTI dataset entries)
+| Dataset                              |                        |
 |--------------------------------------|:----------------------:|
 | Labeled MCTI                         | 100%                   |
 | Full MCTI                            | 100%                   |
@@ -146,8 +147,7 @@ output = model(encoded_input)
 
 ### Limitations and bias
 
-This model is uncased: it does not make a difference between english
-and English.
+This model is uncased: it does not make a difference between english and English.
 
 Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
 predictions:
@@ -182,9 +182,9 @@ 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).
+The [inputted training](https://github.com/chap0lin/PPF-MCTI/tree/master/Datasets) data was obtained from scrapping techniques, over 30 different platforms e.g. The Royal Society, 
+Annenberg foundation, and contained 928 labeled entries (928 rows x 21 columns). Of the data gathered, was used only 
+the main text content (column u). Text content averages 800 tokens in length, but with high variance, up to 5,000 tokens.
 
 ## Training procedure
 
@@ -204,7 +204,7 @@ to implement the [pre-processing code](https://github.com/mcti-sefip/mcti-sefip-
 
 Several Python packages were used to develop the preprocessing code:
 
-#### Table 3: Python packages used
+Table 3: Python packages used
 |                         Objective                      |   Package    |
 |--------------------------------------------------------|--------------|
 | Resolve contractions and slang usage in text           | [contractions](https://pypi.org/project/contractions) |
@@ -221,7 +221,7 @@ Several Python packages were used to develop the preprocessing code:
 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
+Table 4: Preprocessing methods evaluated
 |  id    |                   Experiments                                          |
 |--------|------------------------------------------------------------------------|
 | Base   | Original Texts                                                         |
@@ -244,7 +244,7 @@ All eight bases were evaluated to classify the  eligibility of the opportunity,
 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
+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     |
@@ -282,7 +282,7 @@ data in a supervised manner. The new coupled model can be seen in Figure 5 under
 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
+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   |
@@ -308,7 +308,7 @@ models, we realized supervised training of the whole model. At this point, only
 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
+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   |