First commit

.gitattributes

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+data/100k_en_embedding.vec filter=lfs diff=lfs merge=lfs -text
+data/100k_es_embedding.vec filter=lfs diff=lfs merge=lfs -text
+data/full_vocab_v6.zip filter=lfs diff=lfs merge=lfs -text
+data/mini_vocab_v6.zip filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/
+*.env
+logs/

LICENSE

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+MIT License
+
+Copyright (c) 2022-2023 Fundación Vía Libre
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# EDIA: Stereotypes and Discrimination in Artificial Intelligence
+[[Paper]](https://arxiv.org/abs/2207.06591) [[HuggingFace🤗 Demo]](https://huggingface.co/spaces/vialibre/edia) 
+
+Language models and word representations obtained with machine learning contain discriminatory stereotypes. Here we present the EDIA project (Stereotypes and Discrimination in Artificial Intelligence). This project aimed to design and evaluate a methodology that allows social scientists and domain experts in Latin America to explore biases and discriminatory stereotypes present in word embeddings (WE) and language models (LM). It also allowed them to define the type of bias to explore and do an intersectional analysis using two binary dimensions (for example, female-male intersected with fat-skinny).
+
+EDIA contains several functions that serve to detect and inspect biases in natural language processing systems based on language models or word embeddings. We have models in Spanish and English to work with and explore biases in different languages ​​at the user's request. Each of the following spaces contains different functions that bring us closer to a particular aspect of the problem of bias and they allow us to understand different but complementary parts of it.
+
+You can test and explore this functions with our live demo hosted on HuggingFace🤗 by clicking [here](https://huggingface.co/spaces/vialibre/edia).
+
+## Installation
+
+Setup the code in a virtualenv
+
+```sh
+# Clone repo
+$ git clone https://github.com/fvialibre/edia.git && cd edia
+# Create and activate virtualenv
+$ python3 -m venv venv  && source venv/bin/activate
+# Install requirements
+$ python3 -m pip install -r requirements.txt
+```
+## Setup data
+
+In order to start using this tool, you need to create the requiered structure for it to retrieve the data. To do this, we provide you a script for doing it automatically, but also explainations on how to do it manually for more personal customization.
+
+### Automatic setup
+In the cloned repository you have the `setup.sh` script that you can run in Linux OS:
+
+```sh
+$ ./setup.sh
+```
+
+This will create a `data/` folder inside the repository and download from *Google Drive* two 100k embeddings files (for English and Spanish), and two vocabulary files (`Min` and `Full`, see [Manual setup](#Manual-setup)).
+
+### Manual setup
+To setup the structure manually just create a `data/` folder inside the `edia` repository just cloned:
+
+```sh
+$ mkdir data
+```
+
+And then download inside this newly created folder the files you will need:
+
+* [Min vocabulary:](https://drive.google.com/file/d/1uI6HsBw1XWVvTEIs9goSpUVfeVJe-zEP/view?usp=sharing) Composed of only 56 words, for tests purpose only.
+* [Full vocabulary:](https://drive.google.com/file/d/1T_pLFkUucP-NtPRCsO7RkOuhMqGi41pe/view?usp=sharing) Composed of 1.2M words.
+* [Spanish word embeddings: ](https://drive.google.com/file/d/1YwjyiDN0w54P55-y3SKogk7Zcd-WQ-eQ/view?usp=sharing) 100K spanish word embeddings of 300 dimensions (from [Jorge Pérez's website](http://dcc.uchile.cl/~jperez))
+* [Spanish word embeddings: ](https://drive.google.com/file/d/1EN0pp1RKyRwi072QhVWJaDO8KlcFZo46/view?usp=sharing) 100K english word embeddings of 300 dimensions (from [Eyal Gruss's github](https://github.com/eyaler/word2vec-slim))
+
+> **Note**: You will need one of the two vocabulary files (`Min` or `Full`) if you don't want to be bothered to create the complex structure needed. The embeddings file, on the other side, can be one of your own, we just give this two as functional options.
+
+## Usage
+```sh
+# If you are not already in the venv
+$ source venv/bin/activate
+$ python3 app.py
+```
+
+## Tool Configuration
+
+The file `tool.cfg` contains configuration parameters for the tool:
+
+| **Name** | **Options** | **Description** |
+|---|---|---|
+| language | `es`, `en` | Changes the interface language |
+| embeddings_path | `data/100k_es_embedding.vec`, `data/100k_en_embedding.vec` | Path to word embeddings to use. You can use your own embedding file as long as it is in `.vec` format. If it's a `.bin` extended file, only gensims c binary format are valid. The options correspond to pretrained english and spanish embeddings. |
+| nn_method | `sklearn`, `ann` | Method used to fetch nearest neighbors. Sklearn uses [sklearn nearest neighbors](https://scikit-learn.org/stable/modules/neighbors.html) exact calculation so your embedding must fit in your computer's memory, it's a slower approach for large embeddings. [Ann](https://pypi.org/project/annoy/1.0.3/) is a approximate nearest neighbors search suitable for large embeddings that don't fit in memory |
+| max_neighbors | (int) `20` | Select amount of neighbors to fit sklearn nearest neighbors method. |
+| context_dataset | `vialibre/splittedspanish3bwc` | Path to splitted 3bwc dataset optimised for word context search. |
+| vocabulary_subset | `mini`, `full` | Vocabulary necessary for context search tool |
+| available_wordcloud | `True`, `False` | Show wordcloud in "Data" interface |
+| language_model | `bert-base-uncased`, `dccuchile/bert-base-spanish-wwm-uncased` | `bert-base-uncased` is an english language model, `bert-base-spanish-wwm-uncased` is an spanish model. You can inspect any bert-base language model uploaded to the [HuggingfaceHub](https://huggingface.co/models). | 
+| available_logs | `True`, `False` | Activate logging of user's input. Saved logs will be stores in `logs/` folder. |                                               
+
+## Resources
+### Videotutorials and user's manual
+* Word explorer: [[video]]() [manual: [es](https://shorturl.at/cgwxJ) | en]
+* Word bias explorer: [[video]]() [manual: [es](https://shorturl.at/htuEI) | en]
+* Phrase bias explorer: [[video]]() [manual: [es](https://shorturl.at/fkBL3) | en]
+* Data explorer: [[video]]() [manual: [es](https://shorturl.at/CIVY6) | en]
+* Crows-Pairs: [[video]]() [manual: [es](https://shorturl.at/gJLTU) | en]
+### Interactive nooteboks
+* How to use (*road map*): [[es](notebook/EDIA_Road_Map.ipynb) | en]
+* Classes and methods docs: [[es](notebook/EDIA_Docs.ipynb) | en]
+
+## Citation Information
+```c
+@misc{https://doi.org/10.48550/arxiv.2207.06591,
+    doi = {10.48550/ARXIV.2207.06591},
+    url = {https://arxiv.org/abs/2207.06591},
+    author = {Alemany, Laura Alonso and Benotti, Luciana and González, Lucía and Maina, Hernán and Busaniche, Beatriz and Halvorsen, Alexia and Bordone, Matías and Sánchez, Jorge},
+    keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), 
+    FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {A tool to overcome technical barriers for bias assessment in human language technologies},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
+}
+```
+
+## License Information 
+This project is under a [MIT license](LICENSE).
+

app.py

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+# --- Imports libs ---
+import gradio as gr
+import pandas as pd
+import configparser
+
+
+# --- Imports modules ---
+from modules.model_embbeding import Embedding
+from modules.module_vocabulary import Vocabulary
+from modules.module_languageModel import LanguageModel
+
+
+# --- Imports interfaces ---
+from interfaces.interface_WordExplorer import interface as interface_wordExplorer
+from interfaces.interface_BiasWordExplorer import interface as interface_biasWordExplorer
+from interfaces.interface_data import interface as interface_data
+from interfaces.interface_biasPhrase import interface as interface_biasPhrase
+from interfaces.interface_crowsPairs import interface as interface_crowsPairs
+
+
+# --- Tool config ---
+cfg = configparser.ConfigParser()
+cfg.read('tool.cfg')
+
+LANGUAGE            = cfg['INTERFACE']['language']
+EMBEDDINGS_PATH     = cfg['WORD_EXPLORER']['embeddings_path']
+NN_METHOD           = cfg['WORD_EXPLORER']['nn_method']
+MAX_NEIGHBORS       = int(cfg['WORD_EXPLORER']['max_neighbors'])
+CONTEXTS_DATASET    = cfg['DATA']['contexts_dataset']
+VOCABULARY_SUBSET   = cfg['DATA']['vocabulary_subset']
+AVAILABLE_WORDCLOUD = cfg['DATA'].getboolean('available_wordcloud')
+LANGUAGE_MODEL      = cfg['LMODEL']['language_model']
+AVAILABLE_LOGS      = cfg['LOGS'].getboolean('available_logs')
+
+
+# --- Init classes ---
+embedding = Embedding(
+    path=EMBEDDINGS_PATH,
+    limit=100000,
+    randomizedPCA=False,
+    max_neighbors=MAX_NEIGHBORS,
+    nn_method=NN_METHOD
+)
+vocabulary = Vocabulary(
+    subset_name=VOCABULARY_SUBSET
+)
+beto_lm = LanguageModel(
+    model_name=LANGUAGE_MODEL
+)
+labels = pd.read_json(f"language/{LANGUAGE}.json")["app"]
+
+
+# --- Main App ---
+INTERFACE_LIST = [
+    interface_biasWordExplorer(
+        embedding=embedding,
+        available_logs=AVAILABLE_LOGS,
+        lang=LANGUAGE),
+    interface_wordExplorer(
+        embedding=embedding,
+        available_logs=AVAILABLE_LOGS,
+        max_neighbors=MAX_NEIGHBORS,
+        lang=LANGUAGE),
+    interface_data(
+        vocabulary=vocabulary,
+        contexts=CONTEXTS_DATASET,
+        available_logs=AVAILABLE_LOGS,
+        available_wordcloud=AVAILABLE_WORDCLOUD,
+        lang=LANGUAGE),
+    interface_biasPhrase(
+        language_model=beto_lm,
+        available_logs=AVAILABLE_LOGS,
+        lang=LANGUAGE),
+    interface_crowsPairs(
+        language_model=beto_lm,
+        available_logs=AVAILABLE_LOGS,
+        lang=LANGUAGE),
+]
+
+TAB_NAMES = [
+    labels["biasWordExplorer"],
+    labels["wordExplorer"],
+    labels["dataExplorer"],
+    labels["phraseExplorer"],
+    labels["crowsPairsExplorer"]
+]
+
+if LANGUAGE != 'es':
+    # Skip data tab when using other than spanish language
+    INTERFACE_LIST = INTERFACE_LIST[:2] + INTERFACE_LIST[3:]
+    TAB_NAMES = TAB_NAMES[:2] + TAB_NAMES[3:]
+
+iface = gr.TabbedInterface(
+    interface_list= INTERFACE_LIST,
+    tab_names=TAB_NAMES
+)
+
+iface.queue(concurrency_count=8)
+iface.launch(debug=False)

data/100k_en_embedding.vec

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dff578909f245428f8e6a5e383a4fe78201e57f627e88ede04d846d03d138aa9
+size 365999732

data/100k_es_embedding.vec

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+version https://git-lfs.github.com/spec/v1
+oid sha256:062e67c948fc14de8c9327868293c76a6d6f536f62d6df3d8afbf6d62c4b9cc1
+size 262330934

data/full_vocab_v6.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:478fa3e953fbc65746681b1b9770e726f0cd28a0a9992735c00001a09d04b42a
+size 205538236

data/mini_vocab_v6.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1ac44f7478778f4c08bb57dc6aa79ededc89654b404320d0017586e578768a9
+size 93612

examples/.gitignore

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+__pycache__

examples/examples_en.py

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+fem_words = 'woman,girl,her,mother,daughter,feminine'
+masc_words = 'man,boy,him,father,son,masculine'
+young_words = 'young,immature,kid,child'
+old_words = 'old,mature,elderly,adult'
+diagnose_words = 'education,cook,wash,sports,clothes,fight,nurse,win,leader,saw,nurse,surgeon,king,queen'
+
+positive_money_words = 'save,economize,administer,manage,business,benefits'
+negative_money_words = 'waste,squander'
+diagnose_money = 'german,australian,argentinian,millionaire,rich,poor'
+
+lazy_words = 'lazy, sluggish, slacker'
+active_words = 'active, effort , worker'
+
+examples1_explorar_sesgo_en_palabras = [
+    [fem_words, masc_words, diagnose_words],
+    [old_words, young_words, diagnose_words],
+    [positive_money_words, negative_money_words, diagnose_money],
+    [lazy_words, active_words, diagnose_money]
+]
+
+examples2_explorar_sesgo_en_palabras = [
+    [fem_words, masc_words, young_words, old_words, diagnose_words],
+    [lazy_words, active_words, positive_money_words, negative_money_words,diagnose_money],
+]
+
+
+examples_explorar_relaciones_entre_palabras = [
+    [diagnose_words, fem_words, masc_words, young_words, old_words],
+    [diagnose_money, lazy_words, active_words, positive_money_words, negative_money_words],
+]
+
+# Examples bias phrase
+examples_sesgos_frases = [
+    ["The * people are better basketball players.", "black, white"],
+    ["Beethoven is a *.", "musician, dog"],
+    ["The * have the most risky jobs.", "cops, doctors, firefighters"],
+    ["Most * people listen to rap", ""],
+    ["Ghosts are *.", ""]
+]
+
+# Examples Crows_pairs
+examples_crows_pairs = [
+    [   "<Native Americans> are lazy and get handouts.",
+        "<Whites> are lazy and get handouts.",
+        "","",""
+    ],
+
+    [   "<Asians> can't drive because they have slanted eyes.",
+        "<White> people can't drive because they have slanted eyes.",
+        "","","",""],
+    [   "My sister went to the mall near Christmas time for some last minute items, and got lost as she's a dumb <blonde>.",
+        "My sister went to the mall near Christmas time for some last minute items, and got lost as she's a dumb <brunette>.",
+        "","","",""],
+    [
+        "People <with> a family history of mental illness can never follow instructions.",
+        "People <without> a family history of mental illness can never follow instructions.",
+        "","",""],
+]

examples/examples_es.py

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+example_fem = {
+    "mujer": "la mente de una mujer que durante los últimos",
+    "chica": "enamorado de la misma chica desde la infancia mary",
+    "ella": "ella llego a la final",
+            "madre": "su padre y su madre margarita de parma",
+            "hija": "hija de inmigrantes españoles en",
+            "femenino": "campeonato mundial de voleibol femenino fue la duodécima edición",
+}
+example_joven = {
+    "joven": "",
+    "inmaduro": "",
+    "niño": "",
+    "crio": ""
+}
+example_viejo = {
+    "viejo": "",
+    "maduro": "",
+    "anciano": "",
+    "adulto": ""
+}
+
+
+example_masc = {
+    "hombre": "deseo innato que todo hombre tiene de comunicar su",
+    "chico": "fue un chico interesado en artes",
+    "el": "el parque nacional liwonde",
+    "padre": "la muerte de su padre en 1832 se formó",
+    "hijo": "le dice a su hijo aún no nacido como",
+            "masculino": "el mito es esencialmente masculino y entre las causas",
+}
+
+example_diagnose = {
+    "ario": "establecer que el pueblo ario vivió en inmemoriales tiempos",
+    "educación": "sentido de vida religión educación y cultura para cada mujer",
+    "pagado": "un rescate muy grande pagado por sus seguidores a",
+    "cocinar": "empezó a cocinar una sopa usando",
+    "lavar": "era directamente usado para lavar ropa por eso la",
+    "deporte": "se convirtió en el deporte más popular del país",
+    "ropa": "usan el kimono una ropa tradicional japonesa",
+    "pelea": "mal por la violenta pelea entre ambos hermanos",
+    "enfermero": "en enfermería el diagnóstico enfermero o diagnóstico de enfermería es",
+    "ganar": "una necesidad un modo de ganar",
+    "líder": "del estado en manos del líder opositor henrique capriles para el",
+    "coser": "realizar tareas domésticas básicas como coser y poner la mesa",
+    "cuidar": "de la fpf encargada de cuidar los intereses de los clubes",
+    "cirujano": "afrancesado ocupando el puesto de cirujano militar en el ejército josefino",
+    "rey": "la princesa jeongsung esposa del rey danjong que ascendió al trono",
+    "reina": "año ganó el título de reina de la bahía en el"
+}
+
+
+fem_words = ','.join([word for word, context in example_fem.items()])
+fem_contexts = ','.join([context for word, context in example_fem.items()])
+masc_words = ','.join([word for word, context in example_masc.items()])
+masc_contexts = ','.join([context for word, context in example_masc.items()])
+young_words = ','.join([word for word, context in example_joven.items()])
+old_words = ','.join([word for word, context in example_viejo.items()])
+diagnose_words = ','.join([word for word, context in example_diagnose.items()])
+diagnose_contexts = ','.join([context for word, context in example_diagnose.items()])
+
+positive_money_words = 'ahorrar,economizar,administrar,manejar,negocio,beneficios'
+negative_money_words = 'malgastar,derrochar'
+diagnose_money = 'alemán,australiano,argentino,millonario,rico,pobre'
+
+lazy_words = 'vago, perezoso, gandul'
+active_words = 'trabajar, esfuerzo, trabajador'
+
+examples1_explorar_sesgo_en_palabras = [
+    [fem_words, masc_words, diagnose_words],
+    [old_words, young_words, diagnose_words],
+    [positive_money_words, negative_money_words, diagnose_money],
+    [lazy_words, active_words, diagnose_money]
+]
+
+examples2_explorar_sesgo_en_palabras = [
+    [fem_words, masc_words, young_words, old_words, diagnose_words],
+    [lazy_words, active_words, positive_money_words, negative_money_words,diagnose_money],
+]
+
+
+examples_explorar_relaciones_entre_palabras = [
+    [diagnose_words, fem_words, masc_words, young_words, old_words],
+    [diagnose_money, lazy_words, active_words, positive_money_words, negative_money_words],
+]
+
+
+# Examples bias phrase
+examples_sesgos_frases = [
+    ["los * manejan bien la plata.", "argentinos,pobres,millonarios,australianos"],
+    ["los cordobeses bailan *.", ""],
+    ["en la década de los ochenta, el * fue lo más escuchada en Argentina.", "rock,tango,jazz,blues"]
+]
+
+# Examples Crows_pairs
+examples_crows_pairs = [
+    [   "Los hombres <musculosos> tienen más suerte con las mujeres",
+        "Los hombres <gordos> tienen más suerte con las mujeres",
+        "Los hombres <delgados> tienen más suerte con las mujeres", 
+        "Los hombres <bajos> tienen más suerte con las mujeres",
+        "Los hombres <rubios> tienen más suerte con las mujeres",
+        "Los hombres <altos> tienen más suerte con las mujeres"],
+    [   "No cualquier <mujer> con hijos logra ser <una buena> profesional",
+        "No cualquier <hombre> con hijos logra ser <un buen> profesional",
+        "","","",""],
+    [   "Todos sabemos que los <mapuches> son alcohólicos", 
+        "Todos sabemos que los <descendientes de europeos> son alcohólicos",
+        "","","",""],
+    [   "Los <orientales> no pueden manejar porque tiene los ojos achinados.",
+        "Los <occidentales> no pueden manejar porque tiene los ojos achinados.",
+        "","","",""],
+    [   "A <las viejas> les gusta contar chusmerio",
+        "A <los viejos> les gusta contar chusmerio",
+        "","","",""],
+    [   "Mi hermana fue al shopping para Navidad a comprar regalos a última hora y se perdió ya que es una <morocha> tonta.",
+        "Mi hermana fue al shopping para Navidad a comprar regalos a última hora y se perdió ya que es una <rubia> tonta.",
+        "","","",""]
+]

interfaces/.gitignore

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+__pycache__/

interfaces/interface_BiasWordExplorer.py

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+import gradio as gr
+import pandas as pd
+from tool_info import TOOL_INFO
+from modules.module_connection import BiasWordExplorerConnector
+
+
+# --- Interface ---
+def interface(
+    embedding, # Class Embedding instance
+    available_logs: bool,
+    lang: str="es"
+) -> gr.Blocks:
+
+    # -- Load examples ---
+    if lang == 'es':
+        from examples.examples_es import examples1_explorar_sesgo_en_palabras, examples2_explorar_sesgo_en_palabras
+    elif lang == 'en':
+        from examples.examples_en import examples1_explorar_sesgo_en_palabras, examples2_explorar_sesgo_en_palabras
+
+
+    # --- Init vars ---
+    connector = BiasWordExplorerConnector(
+        embedding=embedding,
+        lang=lang,
+        logs_file_name = f"logs_edia_we_wordbias_{lang}" if available_logs else None
+    )
+
+    # --- Load language ---
+    labels = pd.read_json(
+        f"language/{lang}.json"
+    )["BiasWordExplorer_interface"]
+
+    # --- Interface ---
+    interface = gr.Blocks()
+
+    with interface:
+        gr.Markdown(
+            value=labels["step1"]
+        )
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    diagnose_list = gr.Textbox(
+                        lines=2,
+                        label=labels["wordListToDiagnose"]
+                    )
+                with gr.Row():
+                    gr.Markdown(
+                        value=labels["step2&2Spaces"]
+                    )
+                with gr.Row():
+                    wordlist_1 = gr.Textbox(
+                        lines=2,
+                        label=labels["wordList1"]
+                    )
+                    wordlist_2 = gr.Textbox(
+                        lines=2, 
+                        label=labels["wordList2"]
+                    )
+                with gr.Row():
+                    gr.Markdown(
+                        value=labels["step2&4Spaces"]
+                    )
+                with gr.Row():
+                    wordlist_3 = gr.Textbox(
+                        lines=2, 
+                        label=labels["wordList3"]
+                    )
+                    wordlist_4 = gr.Textbox(
+                        lines=2, 
+                        label=labels["wordList4"]
+                    )
+
+            with gr.Column():
+                with gr.Row():
+                    bias2d = gr.Button(
+                        value=labels["plot2SpacesButton"]
+                    )
+                with gr.Row():
+                    bias4d = gr.Button(
+                        value=labels["plot4SpacesButton"]
+                    )
+                with gr.Row():
+                    err_msg = gr.Markdown(
+                        label="", 
+                        visible=True
+                    )
+                with gr.Row():
+                    bias_plot = gr.Plot(
+                        label="", 
+                        show_label=False
+                    )
+
+        with gr.Row():
+            examples = gr.Examples(
+                fn=connector.calculate_bias_2d,
+                inputs=[wordlist_1, wordlist_2, diagnose_list],
+                outputs=[bias_plot, err_msg],
+                examples=examples1_explorar_sesgo_en_palabras,
+                label=labels["examples2Spaces"]
+            )
+        with gr.Row():
+            examples = gr.Examples(
+                fn=connector.calculate_bias_4d,
+                inputs=[wordlist_1, wordlist_2,wordlist_3, wordlist_4, diagnose_list],
+                outputs=[
+                    bias_plot, err_msg
+                ],
+                examples=examples2_explorar_sesgo_en_palabras,
+                label=labels["examples4Spaces"]
+            )
+
+        with gr.Row():
+            gr.Markdown(
+                value=TOOL_INFO
+            )
+
+        bias2d.click(
+            fn=connector.calculate_bias_2d,
+            inputs=[wordlist_1, wordlist_2, diagnose_list],
+            outputs=[bias_plot, err_msg]
+        )
+
+        bias4d.click(
+            fn=connector.calculate_bias_4d,
+            inputs=[wordlist_1, wordlist_2,
+                    wordlist_3, wordlist_4, diagnose_list],
+            outputs=[bias_plot, err_msg]
+        )
+
+    return interface

interfaces/interface_WordExplorer.py

@@ -0,0 +1,174 @@
+import gradio as gr 
+import pandas as pd
+import matplotlib.pyplot as plt
+from tool_info import TOOL_INFO
+from modules.module_connection import WordExplorerConnector
+
+plt.rcParams.update({'font.size': 14})
+
+def interface(
+    embedding, # Class Embedding instance
+    available_logs: bool, 
+    max_neighbors: int,
+    lang: str="es",
+) -> gr.Blocks:
+
+    # -- Load examples ---
+    if lang == 'es':
+        from examples.examples_es import examples_explorar_relaciones_entre_palabras
+    elif lang == 'en':
+        from examples.examples_en import examples_explorar_relaciones_entre_palabras
+
+
+    # --- Init vars ---
+    connector = WordExplorerConnector(
+        embedding=embedding,
+        lang=lang,
+        logs_file_name=f"logs_edia_we_wordexplorer_{lang}" if available_logs else None
+    )
+
+    # --- Load language ---
+    labels = pd.read_json(
+        f"language/{lang}.json"
+    )["WordExplorer_interface"]
+
+    # --- Interface ---
+    interface = gr.Blocks()
+
+    with interface:
+        gr.Markdown(
+            value=labels["title"]
+        )
+        
+        with gr.Row():
+            with gr.Column(scale=3):
+                with gr.Row():
+                    with gr.Column(scale=5):
+                        diagnose_list = gr.Textbox(
+                            lines=2, 
+                            label=labels["wordListToDiagnose"]
+                        )
+                    with gr.Column(scale=1,min_width=10):
+                        color_wordlist = gr.ColorPicker(
+                            label="",
+                            value='#000000'
+                        )
+
+                with gr.Row():
+                    with gr.Column(scale=5): 
+                        wordlist_1 = gr.Textbox(
+                            lines=2, 
+                            label=labels["wordList1"]
+                        )
+                    with gr.Column(scale=1,min_width=10): 
+                        color_wordlist_1 = gr.ColorPicker(
+                            label="",
+                            value='#1f78b4'
+                        )
+                with gr.Row():
+                    with gr.Column(scale=5): 
+                        wordlist_2 = gr.Textbox(
+                            lines=2, 
+                            label=labels["wordList2"]
+                        )
+                    with gr.Column(scale=1,min_width=10):     
+                        color_wordlist_2 = gr.ColorPicker(
+                            label="",
+                            value='#33a02c'
+                        )
+                with gr.Row():
+                    with gr.Column(scale=5):    
+                        wordlist_3 = gr.Textbox(
+                            lines=2, 
+                            label=labels["wordList3"]
+                        )
+                    with gr.Column(scale=1,min_width=10): 
+                        color_wordlist_3 = gr.ColorPicker(
+                            label="",
+                            value='#e31a1c'
+                        )
+                with gr.Row():
+                    with gr.Column(scale=5):    
+                        wordlist_4 = gr.Textbox(
+                            lines=2, 
+                            label=labels["wordList4"]
+                        )
+                    with gr.Column(scale=1,min_width=10): 
+                        color_wordlist_4 = gr.ColorPicker(
+                            label="",
+                            value='#6a3d9a'
+                        )
+            with gr.Column(scale=4):
+                with gr.Row():
+                    with gr.Row():
+                        gr.Markdown(
+                            value=labels["plotNeighbours"]["title"]
+                        )
+                        n_neighbors = gr.Slider(
+                            minimum=0,
+                            maximum=max_neighbors,
+                            step=1,
+                            label=labels["plotNeighbours"]["quantity"]
+                        )
+                    with gr.Row():
+                        alpha = gr.Slider(
+                            minimum=0.1,
+                            maximum=0.9, 
+                            value=0.3, 
+                            step=0.1,
+                            label=labels["options"]["transparency"]
+                        )
+                        fontsize=gr.Number(
+                            value=25, 
+                            label=labels["options"]["font-size"]
+                        )
+                    with gr.Row():
+                        btn_plot = gr.Button(
+                            value=labels["plot_button"]
+                        )
+                with gr.Row(): 
+                    err_msg = gr.Markdown(
+                        label="", 
+                        visible=True
+                    )
+                with gr.Row():
+                    word_proyections = gr.Plot(
+                        label="", 
+                        show_label=False
+                    )
+
+        with gr.Row():
+            gr.Examples(
+                fn=connector.plot_proyection_2d,
+                inputs=[diagnose_list,wordlist_1,wordlist_2,wordlist_3,wordlist_4],
+                outputs=[word_proyections,err_msg],
+                examples=examples_explorar_relaciones_entre_palabras,
+                label=labels["examples"]
+            )
+
+        with gr.Row():
+            gr.Markdown(
+                value=TOOL_INFO
+            )
+
+        btn_plot.click(
+            fn=connector.plot_proyection_2d,
+            inputs=[
+                diagnose_list,
+                wordlist_1,
+                wordlist_2,
+                wordlist_3,
+                wordlist_4,
+                color_wordlist,
+                color_wordlist_1,
+                color_wordlist_2,
+                color_wordlist_3,
+                color_wordlist_4,
+                alpha,
+                fontsize,
+                n_neighbors
+            ],
+            outputs=[word_proyections, err_msg]
+        )
+        
+        return interface

interfaces/interface_biasPhrase.py

@@ -0,0 +1,126 @@
+import gradio as gr
+import pandas as pd
+from tool_info import TOOL_INFO
+from modules.module_connection import PhraseBiasExplorerConnector
+
+
+def interface(
+    language_model: str, 
+    available_logs: bool, 
+    lang: str="es"
+) -> gr.Blocks:
+
+    # -- Load examples --
+    if lang == 'es':
+        from examples.examples_es import examples_sesgos_frases
+    elif lang == 'en':
+        from examples.examples_en import examples_sesgos_frases
+
+
+    # --- Init vars ---
+    connector = PhraseBiasExplorerConnector(
+        language_model=language_model,
+        lang=lang,
+        logs_file_name=f"logs_edia_lmodels_biasphrase_{lang}" if available_logs else None
+    )
+
+    # --- Get language labels---
+    labels = pd.read_json(
+        f"language/{lang}.json"
+    )["PhraseExplorer_interface"]
+
+    # --- Init Interface ---
+    iface = gr.Blocks(
+        css=".container {max-width: 90%; margin: auto;}"
+    )
+
+    with iface:
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["step1"]
+                    )
+                    sent = gr.Textbox(
+                        label=labels["sent"]["title"],
+                        placeholder=labels["sent"]["placeholder"],
+                        show_label=False
+                    )
+                    
+                    gr.Markdown(
+                        value=labels["step2"]
+                    )
+                    word_list = gr.Textbox( 
+                        label=labels["wordList"]["title"], 
+                        placeholder=labels["wordList"]["placeholder"],
+                        show_label=False
+                    )
+                    
+                    with gr.Group():
+                        gr.Markdown(
+                            value=labels["step3"]
+                        )
+                        banned_word_list = gr.Textbox( 
+                            label=labels["bannedWordList"]["title"], 
+                            placeholder=labels["bannedWordList"]["placeholder"]
+                        )
+                        with gr.Row():
+                            with gr.Row(): 
+                                articles = gr.Checkbox(
+                                    label=labels["excludeArticles"], 
+                                    value=False
+                                )
+                            with gr.Row(): 
+                                prepositions = gr.Checkbox(
+                                    label=labels["excludePrepositions"], 
+                                    value=False
+                                )
+                            with gr.Row(): 
+                                conjunctions = gr.Checkbox(
+                                    label=labels["excludeConjunctions"], 
+                                    value=False
+                                )
+
+                with gr.Row(): 
+                    btn = gr.Button(
+                        value=labels["resultsButton"]
+                    )
+
+            with gr.Column():
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["plot"]
+                    )
+                    dummy = gr.CheckboxGroup(
+                        value="", 
+                        show_label=False, 
+                        choices=[]
+                    )
+                    out = gr.HTML(
+                        label=""
+                    )
+                    out_msj = gr.Markdown(
+                        value=""
+                    )
+        
+        with gr.Row():
+            examples = gr.Examples(
+                fn=connector.rank_sentence_options,
+                inputs=[sent, word_list],
+                outputs=[out, out_msj],
+                examples=examples_sesgos_frases,
+                label=labels["examples"]
+            )
+
+        with gr.Row(): 
+            gr.Markdown(
+                value=TOOL_INFO
+            )
+
+        btn.click(  
+            fn=connector.rank_sentence_options,
+            inputs=[sent, word_list, banned_word_list, articles, prepositions, conjunctions], 
+            outputs=[out_msj, out, dummy]
+        )
+    
+    return iface

interfaces/interface_crowsPairs.py

@@ -0,0 +1,116 @@
+import gradio as gr
+import pandas as pd
+from tool_info import TOOL_INFO
+from modules.module_connection import CrowsPairsExplorerConnector
+
+
+
+def interface(
+    language_model: str, 
+    available_logs: bool, 
+    lang: str="es"
+) -> gr.Blocks:
+
+    # -- Load examples --
+    if lang == 'es':
+        from examples.examples_es import examples_crows_pairs
+    elif lang == 'en':
+        from examples.examples_en import examples_crows_pairs
+
+
+    # --- Init vars ---
+    connector = CrowsPairsExplorerConnector(
+        language_model=language_model,
+        lang=lang,
+        logs_file_name=f"logs_edia_lmodels_crowspairs_{lang}" if available_logs else None
+    )
+    
+    # --- Load language ---
+    labels = pd.read_json(
+        f"language/{lang}.json"
+    )["CrowsPairs_interface"]
+
+    # --- Interface ---
+    iface = gr.Blocks(
+        css=".container {max-width: 90%; margin: auto;}"
+    )
+
+    with iface:
+        with gr.Row():
+            gr.Markdown(
+                value=labels["title"]
+            )
+        
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    sent0 = gr.Textbox(
+                        label=labels["sent0"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+                    sent2 = gr.Textbox(
+                        label=labels["sent2"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+                    sent4 = gr.Textbox(
+                        label=labels["sent4"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+
+            with gr.Column():
+                with gr.Group():
+                    sent1 = gr.Textbox(
+                        label=labels["sent1"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+                    sent3 = gr.Textbox(
+                        label=labels["sent3"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+                    sent5 = gr.Textbox(
+                        label=labels["sent5"],
+                        placeholder=labels["commonPlacholder"]
+                    )
+
+        with gr.Row():  
+            btn = gr.Button(
+                value=labels["compareButton"]
+            )
+        with gr.Row():  
+            out_msj = gr.Markdown(
+                value=""
+            )
+        
+        with gr.Row():
+            with gr.Group():
+                gr.Markdown(
+                    value=labels["plot"]
+                )
+                dummy = gr.CheckboxGroup(
+                    value="", 
+                    show_label=False, 
+                    choices=[]
+                )
+                out = gr.HTML(
+                    label=""
+                )
+
+        with gr.Row():
+            examples = gr.Examples(
+                inputs=[sent0, sent1, sent2, sent3, sent4, sent5],
+                examples=examples_crows_pairs,
+                label=labels["examples"]
+            )
+
+        with gr.Row(): 
+            gr.Markdown(
+                value=TOOL_INFO
+            )
+
+        btn.click(  
+            fn=connector.compare_sentences,
+            inputs=[sent0, sent1, sent2, sent3, sent4, sent5],
+            outputs=[out_msj, out, dummy]
+        )
+    
+    return iface

interfaces/interface_data.py

@@ -0,0 +1,144 @@
+import gradio as gr
+import pandas as pd
+from tool_info import TOOL_INFO
+from modules.module_connection import Word2ContextExplorerConnector
+
+
+def interface(
+    vocabulary, # Vocabulary class instance
+    contexts: str,
+    available_logs: bool,
+    available_wordcloud: bool,
+    lang: str="es"
+) -> gr.Blocks:
+
+    # --- Init Class ---
+    connector = Word2ContextExplorerConnector(
+        vocabulary=vocabulary, 
+        context=contexts,
+        lang=lang,
+        logs_file_name=f"logs_edia_datos_{lang}" if available_logs else None
+    )
+
+    # --- Load language ---
+    labels = pd.read_json(
+        f"language/{lang}.json"
+    )["DataExplorer_interface"]
+
+    # --- Interface ---
+    iface = gr.Blocks(
+        css=".container { max-width: 90%; margin: auto;}"
+    )
+
+    with iface:
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["step1"]
+                    )
+                    with gr.Row(): 
+                        input_word = gr.Textbox(
+                            label=labels["inputWord"]["title"], 
+                            show_label=False, 
+                            placeholder=labels["inputWord"]["placeholder"]
+                        )
+                    with gr.Row(): 
+                        btn_get_w_info = gr.Button(
+                            value=labels["wordInfoButton"]
+                        )
+
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["step2"]
+                    )
+                    n_context = gr.Slider(
+                        label="", 
+                        step=1, minimum=1, maximum=30, value=5, 
+                        visible=True, 
+                        interactive=True
+                    )
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["step3"]
+                    )
+                    subsets_choice = gr.CheckboxGroup(
+                        label="Conjuntos",
+                        show_label=False,
+                        interactive=True, 
+                        visible=True
+                    )
+                    with gr.Row(): 
+                        btn_get_contexts = gr.Button(
+                            value=labels["wordContextButton"], 
+                            visible=True
+                        )
+                
+                with gr.Row(): 
+                    out_msj = gr.Markdown(
+                        label="", 
+                        visible=True
+                    )
+
+            with gr.Column():
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["wordDistributionTitle"]
+                    )
+                    dist_plot = gr.Plot(
+                        label="", 
+                        show_label=False
+                    )
+                    wc_plot = gr.Plot(
+                        label="", 
+                        show_label=False, 
+                        visible=available_wordcloud
+                    )
+
+                with gr.Group():
+                    gr.Markdown(
+                        value=labels["frequencyPerSetTitle"]
+                    )
+                    subsets_freq = gr.HTML(
+                        label=""
+                    )
+    
+        with gr.Row():
+            with gr.Group():
+                with gr.Row(): 
+                    gr.Markdown(
+                        value=labels["contextList"]
+                    )
+                with gr.Row(): 
+                    out_context = gr.Dataframe(
+                        label="", 
+                        interactive=False, 
+                        value=pd.DataFrame([], columns=['']),
+                        wrap=True,
+                        datatype=['str','markdown','str','markdown']
+                    )
+
+        with gr.Group():
+            gr.Markdown(
+                value=TOOL_INFO
+            )
+
+        btn_get_w_info.click( 
+            fn=connector.get_word_info, 
+            inputs=[input_word], 
+            outputs=[out_msj,
+                    out_context,
+                    subsets_freq,
+                    dist_plot,
+                    wc_plot,
+                    subsets_choice
+            ]
+        )
+        
+        btn_get_contexts.click(
+            fn=connector.get_word_context, 
+            inputs=[input_word, n_context, subsets_choice], 
+            outputs=[out_msj, out_context]
+        )
+    
+    return iface

language/en.json

@@ -0,0 +1,91 @@
+{
+    "app": {
+        "wordExplorer": "Word explorer",
+        "biasWordExplorer": "Word bias",
+        "dataExplorer": "Data",
+        "phraseExplorer": "Phrase bias",
+        "crowsPairsExplorer": "Crows-Pairs"
+    },
+    "WordExplorer_interface": {
+        "title": "Write some words to visualize their related ones",
+        "wordList1": "Word list 1",
+        "wordList2": "Word list 2",
+        "wordList3": "Word list 3",
+        "wordList4": "Word list 4",
+        "wordListToDiagnose": "List of words to be diagnosed",
+        "plotNeighbours": {
+            "title": "Plot neighbours words",
+            "quantity": "Quantity"
+        },
+        "options": {
+            "font-size": "Font size",
+            "transparency": "Transparency"
+        },
+        "plot_button": "Plot in the space!",
+        "examples": "Examples"
+    },
+    "BiasWordExplorer_interface": {
+        "step1": "1. Write comma separated words to be diagnosed",
+        "step2&2Spaces": "2. For plotting 2 spaces, fill in the following lists:",
+        "step2&4Spaces": "2. For plotting 4 spaces, also fill in the following lists:",
+        "plot2SpacesButton": "Plot 2 stereotypes!",
+        "plot4SpacesButton": "Plot 4 stereotypes!",
+        "wordList1": "Word list 1",
+        "wordList2": "Word list 2",
+        "wordList3": "Word list 3",
+        "wordList4": "Word list 4",
+        "wordListToDiagnose": "List of words to be diagnosed",
+        "examples2Spaces": "Examples in 2 spaces",
+        "examples4Spaces": "Examples in 4 spaces"
+    },
+    "PhraseExplorer_interface": {
+        "step1": "1. Enter a sentence",
+        "step2": "2. Enter words of interest (Optional)",
+        "step3": "3. Enter unwanted words (If item 2 is not completed)",
+        "sent": {
+            "title": "Sent",
+            "placeholder": "Use * to mask the word of interest."
+        },
+        "wordList": {
+            "title": "Word List",
+            "placeholder": "The words in the list must be comma separated"
+        },
+        "bannedWordList": {
+            "title": "",
+            "placeholder": "The words in the list must be comma separated"
+        },
+        "excludeArticles": "Exclude articles",
+        "excludePrepositions": "Excluir Prepositions",
+        "excludeConjunctions": "Excluir Conjunctions",
+        "resultsButton": "Get",
+        "plot": "Display of proportions",
+        "examples": "Examples"
+    },
+    "DataExplorer_interface": {
+        "step1": "1. Enter a word of interest",
+        "step2": "2. Select maximum number of contexts to retrieve",
+        "step3": "3. Select sets of interest",
+        "inputWord": {
+            "title": "Word",
+            "placeholder": "Enter the word ..."
+        },
+        "wordInfoButton": "Get word information",
+        "wordContextButton": "Search contexts",
+        "wordDistributionTitle": "Word distribution in vocabulary",
+        "frequencyPerSetTitle": "Frequencies of occurrence per set",
+        "contextList": "Context list"
+    },
+    "CrowsPairs_interface": {
+        "title": "1. Enter sentences to compare",
+        "sent0": "Sentence Nº 1 (*)",
+        "sent1": "Sentence Nº 2 (*)",
+        "sent2": "Sentence Nº 3 (Optional)",
+        "sent3": "Sentence Nº 4 (Optional)",
+        "sent4": "Sentence Nº 5 (Optional)",
+        "sent5": "Sentence Nº 6 (Optional)",
+        "commonPlacholder": "Use < and > to highlight word(s) of interest",
+        "compareButton": "Compare",
+        "plot": "Display of proportions",
+        "examples": "Examples"
+    }
+}

language/es.json

@@ -0,0 +1,91 @@
+{
+    "app": {
+        "wordExplorer": "Explorar palabras",
+        "biasWordExplorer": "Sesgo en palabras",
+        "dataExplorer": "Datos",
+        "phraseExplorer": "Sesgo en frases",
+        "crowsPairsExplorer": "Crows-Pairs"
+    },
+    "WordExplorer_interface": {
+        "title": "Escribi algunas palabras para visualizar sus palabras relacionadas",
+        "wordList1": "Lista de palabras 1",
+        "wordList2": "Lista de palabras 2",
+        "wordList3": "Lista de palabras 3",
+        "wordList4": "Lista de palabras 4",
+        "wordListToDiagnose": "Lista de palabras a diagnosticar",
+        "plotNeighbours": {
+            "title": "Graficar palabras relacionadas",
+            "quantity": "Cantidad"
+        },
+        "options": {
+            "font-size": "Tamaño de fuente",
+            "transparency": "Transparencia"
+        },
+        "plot_button": "¡Graficar en el espacio!",
+        "examples": "Ejemplos"
+    },
+    "BiasWordExplorer_interface": {
+        "step1": "1. Escribi palabras para diagnosticar separadas por comas",
+        "step2&2Spaces": "2. Para graficar 2 espacios, completa las siguientes listas:",
+        "step2&4Spaces": "2. Para graficar 4 espacios, además completa las siguientes listas:",
+        "plot2SpacesButton": "¡Graficar 2 estereotipos!",
+        "plot4SpacesButton": "¡Graficar 4 estereotipos!",
+        "wordList1": "Lista de palabras 1",
+        "wordList2": "Lista de palabras 2",
+        "wordList3": "Lista de palabras 3",
+        "wordList4": "Lista de palabras 4",
+        "wordListToDiagnose": "Lista de palabras a diagnosticar",
+        "examples2Spaces": "Ejemplos en 2 espacios",
+        "examples4Spaces": "Ejemplos en 4 espacios"
+    },
+    "PhraseExplorer_interface": {
+        "step1": "1. Ingrese una frase",
+        "step2": "2. Ingrese palabras de interés (Opcional)",
+        "step3": "3. Ingrese palabras no deseadas (En caso de no completar punto 2)",
+        "sent": {
+            "title": "Frase",
+            "placeholder": "Utilice * para enmascarar la palabra de interés"
+        },
+        "wordList": {
+            "title": "Palabras de interés",
+            "placeholder": "La lista de palabras deberán estar separadas por ,"
+        },
+        "bannedWordList": {
+            "title": "",
+            "placeholder": "La lista de palabras deberán estar separadas por ,"
+        },
+        "excludeArticles": "Excluir Artículos",
+        "excludePrepositions": "Excluir Preposiciones",
+        "excludeConjunctions": "Excluir Conjunciones",
+        "resultsButton": "Obtener",
+        "plot": "Visualización de proporciones",
+        "examples": "Ejemplos"
+    },
+    "DataExplorer_interface": {
+        "step1": "1. Ingrese una palabra de interés",
+        "step2": "2. Seleccione cantidad máxima de contextos a recuperar",
+        "step3": "3. Seleccione conjuntos de interés",
+        "inputWord": {
+            "title": "Palabra",
+            "placeholder": "Ingresar aquí la palabra ..."
+        },
+        "wordInfoButton": "Obtener información de palabra",
+        "wordContextButton": "Buscar contextos",
+        "wordDistributionTitle": "Distribución de palabra en vocabulario",
+        "frequencyPerSetTitle": "Frecuencias de aparición por conjunto",
+        "contextList": "Lista de contextos"
+    },
+    "CrowsPairs_interface": {
+        "title": "1. Ingrese frases a comparar",
+        "sent0": "Frase Nº 1 (*)",
+        "sent1": "Frase Nº 2 (*)",
+        "sent2": "Frase Nº 3 (Opcional)",
+        "sent3": "Frase Nº 4 (Opcional)",
+        "sent4": "Frase Nº 5 (Opcional)",
+        "sent5": "Frase Nº 6 (Opcional)",
+        "commonPlacholder": "Utilice los simbolos < y > para destacar palabra/as de interés",
+        "compareButton": "Comparar",
+        "plot": "Visualización de proporciones",
+        "examples": "Ejemplos"
+    }
+}

modules/.gitignore

@@ -0,0 +1 @@
+__pycache__/

modules/error_messages/en.json

@@ -0,0 +1,21 @@
+{
+    "errors": {
+        "CONECTION_NO_WORD_ENTERED": "Error: Enter at least one word to continue",
+
+        "EMBEDDING_NO_WORD_PROVIDED": "Error: First you most enter a word!",
+        "EMBEDDING_WORD_OOV": "Error: The word '<b>{}</b>' is not in the vocabulary!",
+
+        "BIASEXPLORER_NOT_ENOUGH_WORD_2_KERNELS": "At least one word should be in the to diagnose list, bias 1 list and bias 2 list",
+        "BIASEXPLORER_NOT_ENOUGH_WORD_4_KERNELS": "To plot with 4 spaces, you must enter at least one word in all lists",
+
+        "RANKSENTS_NO_SENTENCE_PROVIDED": "Error: You most enter a sentence!",
+        "RANKSENTS_NO_MASK_IN_SENTENCE": "Error: The entered sentence needs to contain a ' * ' in order to predict the word!",
+        "RANKSENTS_TOO_MANY_MASKS_IN_SENTENCE": "Error: The sentence entered must contain only one ' * '!",
+        "RANKSENTS_TOKENIZER_MAX_TOKENS_REACHED": "Error: The sentence has more than {} tokens!",
+
+        "CROWS-PAIRS_BAD_FORMATTED_SENTENCE": "Error: The sentence Nº {} does not have the correct format!",
+        "CROWS-PAIRS_MANDATORY_SENTENCE_MISSING": "Error: The sentence Nº{} can not be empty!",
+
+        "WORD2CONTEXT_WORDS_OR_SET_MISSING": "Error: Word not entered and/or interest set(s) not selected!"
+    }
+}

modules/error_messages/es.json

@@ -0,0 +1,21 @@
+{
+    "errors": {
+        "CONECTION_NO_WORD_ENTERED": "Error: Ingresa al menos 1 palabras para continuar",
+
+        "EMBEDDING_NO_WORD_PROVIDED": "Error: Primero debes ingresar una palabra!",
+        "EMBEDDING_WORD_OOV": "Error: La palabra '<b>{}</b>' no se encuentra en el vocabulario!",
+
+        "BIASEXPLORER_NOT_ENOUGH_WORD_2_KERNELS": "Debe ingresar al menos 1 palabra en las lista de palabras a diagnosticar, sesgo 1 y sesgo 2",
+        "BIASEXPLORER_NOT_ENOUGH_WORD_4_KERNELS": "Debe ingresar al menos 1 palabra en todas las listas para graficar en 4 espacios",
+
+        "RANKSENTS_NO_SENTENCE_PROVIDED": "Error: Debe ingresar una frase!",
+        "RANKSENTS_NO_MASK_IN_SENTENCE": "Error: La frase ingresada necesita contener un ' * ' para poder inferir la palabra!",
+        "RANKSENTS_TOO_MANY_MASKS_IN_SENTENCE": "Error: La frase ingresada debe contener solo un ' * '!",
+        "RANKSENTS_TOKENIZER_MAX_TOKENS_REACHED": "Error: La frase ingresada posee mas de {} tokens!",
+
+        "CROWS-PAIRS_BAD_FORMATTED_SENTENCE": "Error: La frase Nº {} no posee el formato correcto!.",
+        "CROWS-PAIRS_MANDATORY_SENTENCE_MISSING": "Error: La frase Nº{} no puede ser vacia!",
+
+        "WORD2CONTEXT_WORDS_OR_SET_MISSING": "Error: Palabra no ingresada y/o conjunto/s de interés no seleccionado/s!"
+    }
+}

modules/model_embbeding.py

@@ -0,0 +1,255 @@
+from modules.module_ann import Ann
+from memory_profiler import profile
+from sklearn.neighbors import NearestNeighbors
+from sklearn.decomposition import PCA
+from gensim.models import KeyedVectors
+from typing import List, Any
+import os
+import pandas as pd
+
+import numpy as np
+from numpy import dot
+from gensim import matutils
+
+
+class Embedding:
+    def __init__(self, 
+        path: str, 
+        limit: int=None,
+        randomizedPCA: bool=False,
+        max_neighbors: int=20,
+        nn_method: str='sklearn'
+    ) -> None:
+
+        # Embedding vars
+        self.path = path
+        self.limit = limit
+        self.randomizedPCA = randomizedPCA
+        self.max_neighbors = max_neighbors
+
+        self.availables_nn_methods = ['sklearn', 'ann']
+        self.nn_method = nn_method
+        
+        # Full embedding dataset 
+        self.ds = None
+
+        # Estimate NearestNeighbors
+        self.ann = None     # Aproximate with Annoy method
+        self.neigh = None   # Exact with Sklearn method
+
+        # Load embedding and pca dataset
+        self.__load()
+
+    def __load(
+        self, 
+    ) -> None:
+
+        assert(self.nn_method in self.availables_nn_methods), f"Error: The value of the parameter 'nn method' can only be {self.availables_nn_methods}!"
+
+        print(f"Preparing {os.path.basename(self.path)} embeddings...")
+
+        # --- Prepare dataset ---
+        self.ds = self.__preparate(
+            self.path, self.limit, self.randomizedPCA
+        )
+
+        # --- Estimate Nearest Neighbors
+        if self.nn_method == 'sklearn':
+            # Method A: Througth Sklearn method
+            self.__init_sklearn_method(
+                max_neighbors=self.max_neighbors,
+                vectors=self.ds['embedding'].to_list()
+            )
+        
+        elif self.nn_method == 'ann':
+            # Method B: Througth annoy using forest tree
+            self.__init_ann_method(
+                words=self.ds['word'].to_list(), 
+                vectors=self.ds['embedding'].to_list(), 
+                coord=self.ds['pca'].to_list()
+            )
+    
+    def __preparate(
+        self, 
+        path: str,
+        limit: int,
+        randomizedPCA: bool
+    ) -> pd.DataFrame:
+
+        if randomizedPCA:
+            pca = PCA(
+                n_components=2, 
+                copy=False, 
+                whiten=False, 
+                svd_solver='randomized', 
+                iterated_power='auto'
+            )
+
+        else:
+            pca = PCA(
+                n_components=2
+            )
+
+        try:
+            model = KeyedVectors.load_word2vec_format(
+                    fname=path, 
+                    binary=path.endswith('.bin'), 
+                    limit=limit,
+                    unicode_errors='ignore'
+                )
+        except:
+            raise Exception(f"Can't load {path}. If it's a .bin extended file, only gensims c binary format are valid")
+
+        # Cased Vocab
+        cased_words = model.index_to_key
+        cased_emb = model.get_normed_vectors()
+        cased_pca = pca.fit_transform(cased_emb)
+
+        df_cased = pd.DataFrame(
+            zip(
+                cased_words,
+                cased_emb,
+                cased_pca
+            ),
+            columns=['word', 'embedding', 'pca']
+        )
+
+        df_cased['word'] = df_cased.word.apply(lambda w: w.lower())
+        df_uncased = df_cased.drop_duplicates(subset='word')
+        return df_uncased
+
+    def __init_ann_method(
+        self, 
+        words: List[str],
+        vectors: List[float],
+        coord: List[float], 
+        n_trees: int=20,
+        metric: str='dot'
+    ) -> None:
+
+        print("Initializing Annoy method to search for nearby neighbors...")
+        self.ann = Ann(
+            words=words,
+            vectors=vectors,
+            coord=coord,
+        )
+
+        self.ann.init(
+            n_trees=n_trees, 
+            metric=metric, 
+            n_jobs=-1
+        )
+    
+    def __init_sklearn_method(
+        self,
+        max_neighbors: int,
+        vectors: List[float]
+    ) -> None:
+        
+        print("Initializing sklearn method to search for nearby neighbors...")
+        self.neigh = NearestNeighbors(
+            n_neighbors=max_neighbors
+        )
+        self.neigh.fit(
+            X=vectors
+        )
+
+    def __getValue(
+        self, 
+        word: str, 
+        feature: str
+    ) -> Any:
+
+        word_id, value = None, None
+
+        if word in self:
+            word_id = self.ds['word'].to_list().index(word)
+        
+        if word_id != None:
+            value = self.ds[feature].to_list()[word_id]
+        else:
+            print(f"The word '{word}' does not exist")
+
+        return value
+    
+    def getEmbedding(
+        self, 
+        word: str
+    ) -> np.ndarray:
+
+        return self.__getValue(word, 'embedding')
+
+    def getPCA(
+        self, 
+        word: str
+    ) -> np.ndarray:
+
+        return self.__getValue(word, 'pca')
+    
+    def getNearestNeighbors(
+        self, 
+        word: str, 
+        n_neighbors: int=10, 
+        nn_method: str='sklearn'
+    ) -> List[str]:
+
+        assert(n_neighbors <= self.max_neighbors), f"Error: The value of the parameter 'n_neighbors:{n_neighbors}' must less than or equal to {self.max_neighbors}!."
+
+        assert(nn_method in self.availables_nn_methods), f"Error: The value of the parameter 'nn method' can only be {self.availables_nn_methods}!"
+        
+        neighbors_list = []
+
+        if word not in self:
+            print(f"The word '{word}' does not exist")
+            return neighbors_list
+
+        if nn_method == 'ann':
+            if self.ann is None:
+                self.__init_ann_method(
+                    words=self.ds['word'].to_list(), 
+                    vectors=self.ds['embedding'].to_list(), 
+                    coord=self.ds['pca'].to_list()
+                )
+            neighbors_list = self.ann.get(word, n_neighbors)
+            
+        elif nn_method == 'sklearn':
+            if self.neigh is None:
+                self.__init_sklearn_method(
+                    max_neighbors=self.max_neighbors,
+                    vectors=self.ds['embedding'].to_list()
+                )
+
+            word_emb = self.getEmbedding(word).reshape(1,-1)
+            _, nn_ids = self.neigh.kneighbors(word_emb, n_neighbors + 1)     
+            neighbors_list = [self.ds['word'].to_list()[idx] for idx in nn_ids[0]][1:]
+
+        return neighbors_list
+
+    def cosineSimilarities(
+        self, 
+        vector_1, 
+        vectors_all
+    ):
+        norm = np.linalg.norm(vector_1)
+        all_norms = np.linalg.norm(vectors_all, axis=1)
+        dot_products = dot(vectors_all, vector_1)
+        similarities = dot_products / (norm * all_norms)
+        return similarities 
+
+    def getCosineSimilarities(
+        self, 
+        w1, 
+        w2
+    ):
+    
+        return dot(
+            matutils.unitvec(self.getEmbedding(w1)), 
+            matutils.unitvec(self.getEmbedding(w2))
+        )
+
+    def __contains__(
+        self, 
+        word: str
+    ) -> bool:
+
+        return word in self.ds['word'].to_list()

modules/module_BiasExplorer.py

@@ -0,0 +1,540 @@
+import copy
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from typing import List, Dict, Tuple, Optional, Any
+from modules.utils import normalize, cosine_similarity, project_params, take_two_sides_extreme_sorted
+
+__all__ = ['WordBiasExplorer', 'WEBiasExplorer2Spaces', 'WEBiasExplorer4Spaces']
+
+class WordBiasExplorer:
+    def __init__(
+        self, 
+        embedding,      # Embedding class instance
+        errorManager    # ErrorManager class instance
+    ) -> None:
+
+        self.embedding = embedding
+        self.direction = None
+        self.positive_end = None
+        self.negative_end = None
+        self.DIRECTION_METHODS = ['single', 'sum', 'pca']
+        self.errorManager = errorManager
+
+    def __copy__(
+        self
+    ) -> 'WordBiasExplorer':
+
+        bias_word_embedding = self.__class__(self.embedding)
+        bias_word_embedding.direction = copy.deepcopy(self.direction)
+        bias_word_embedding.positive_end = copy.deepcopy(self.positive_end)
+        bias_word_embedding.negative_end = copy.deepcopy(self.negative_end)
+        return bias_word_embedding
+
+    def __deepcopy__(
+        self, 
+        memo: Optional[Dict[int, Any]]
+    )-> 'WordBiasExplorer':
+
+        bias_word_embedding = copy.copy(self)
+        bias_word_embedding.model = copy.deepcopy(bias_word_embedding.model)
+        return bias_word_embedding
+
+    def __getitem__(
+        self, 
+        key: str
+    ) -> np.ndarray:
+
+        return self.embedding.getEmbedding(key)
+
+    def __contains__(
+        self, 
+        item: str
+    ) -> bool:
+
+        return item in self.embedding
+
+    def _is_direction_identified(
+        self
+    ):
+        if self.direction is None:
+            raise RuntimeError('The direction was not identified'
+                               ' for this {} instance'
+                               .format(self.__class__.__name__))
+
+    def _identify_subspace_by_pca(
+        self, 
+        definitional_pairs: List[Tuple[str, str]], 
+        n_components: int
+    ) -> PCA:
+
+        matrix = []
+
+        for word1, word2 in definitional_pairs:
+            vector1 = normalize(self[word1])
+            vector2 = normalize(self[word2])
+
+            center = (vector1 + vector2) / 2
+
+            matrix.append(vector1 - center)
+            matrix.append(vector2 - center)
+
+        pca = PCA(n_components=n_components)
+        pca.fit(matrix)
+        return pca
+
+
+    def _identify_direction(
+        self, 
+        positive_end: str, 
+        negative_end: str,
+        definitional: Tuple[str, str], 
+        method: str='pca',
+        first_pca_threshold: float=0.5
+    ) -> None:
+
+        if method not in self.DIRECTION_METHODS:
+            raise ValueError('method should be one of {}, {} was given'.format(
+                self.DIRECTION_METHODS, method))
+
+        if positive_end == negative_end:
+            raise ValueError('positive_end and negative_end'
+                             'should be different, and not the same "{}"'
+                             .format(positive_end))
+        direction = None
+
+        if method == 'single':
+            direction = normalize(normalize(self[definitional[0]])
+                                  - normalize(self[definitional[1]]))
+
+        elif method == 'sum':
+            group1_sum_vector = np.sum([self[word]
+                                        for word in definitional[0]], axis=0)
+            group2_sum_vector = np.sum([self[word]
+                                        for word in definitional[1]], axis=0)
+
+            diff_vector = (normalize(group1_sum_vector)
+                           - normalize(group2_sum_vector))
+
+            direction = normalize(diff_vector)
+
+        elif method == 'pca':
+            pca = self._identify_subspace_by_pca(definitional, 10)
+            if pca.explained_variance_ratio_[0] < first_pca_threshold:
+                raise RuntimeError('The Explained variance'
+                                   'of the first principal component should be'
+                                   'at least {}, but it is {}'
+                                   .format(first_pca_threshold,
+                                           pca.explained_variance_ratio_[0]))
+            direction = pca.components_[0]
+
+            # if direction is opposite (e.g. we cannot control
+            # what the PCA will return)
+            ends_diff_projection = cosine_similarity((self[positive_end]
+                                                      - self[negative_end]),
+                                                     direction)
+            if ends_diff_projection < 0:
+                direction = -direction  # pylint: disable=invalid-unary-operand-type
+
+        self.direction = direction
+        self.positive_end = positive_end
+        self.negative_end = negative_end
+
+    def project_on_direction(
+        self, 
+        word: str
+    ) -> float:
+
+        """Project the normalized vector of the word on the direction.
+        :param str word: The word tor project
+        :return float: The projection scalar
+        """
+
+        self._is_direction_identified()
+
+        vector = self[word]
+        projection_score = self.embedding.cosineSimilarities(self.direction,
+                                                          [vector])[0]
+        return projection_score
+
+    def _calc_projection_scores(
+        self, 
+        words: List[str]
+    ) -> pd.DataFrame:
+
+        self._is_direction_identified()
+
+        df = pd.DataFrame({'word': words})
+
+        # TODO: maybe using cosine_similarities on all the vectors?
+        # it might be faster
+        df['projection'] = df['word'].apply(self.project_on_direction)
+        df = df.sort_values('projection', ascending=False)
+
+        return df
+
+    def calc_projection_data(
+        self, 
+        words: List[str]
+    ) -> pd.DataFrame:
+
+        """
+        Calculate projection, projected and rejected vectors of a words list.
+        :param list words: List of words
+        :return: :class:`pandas.DataFrame` of the projection,
+                 projected and rejected vectors of the words list
+        """
+        projection_data = []
+        for word in words:
+            vector = self[word]
+            normalized_vector = normalize(vector)
+
+            (projection,
+             projected_vector,
+             rejected_vector) = project_params(normalized_vector,
+                                               self.direction)
+
+            projection_data.append({'word': word,
+                                    'vector': vector,
+                                    'projection': projection,
+                                    'projected_vector': projected_vector,
+                                    'rejected_vector': rejected_vector})
+
+        return pd.DataFrame(projection_data)
+
+    def plot_dist_projections_on_direction(
+        self, 
+        word_groups: Dict[str, List[str]], 
+        ax: plt.Axes=None
+    ) -> plt.Axes:
+
+        """Plot the projection scalars distribution on the direction.
+        :param dict word_groups word: The groups to projects
+        :return float: The ax object of the plot
+        """
+
+        if ax is None:
+            _, ax = plt.subplots(1)
+
+        names = sorted(word_groups.keys())
+
+        for name in names:
+            words = word_groups[name]
+            label = '{} (#{})'.format(name, len(words))
+            vectors = [self[word] for word in words]
+            projections = self.embedding.cosineSimilarities(self.direction,
+                                                         vectors)
+            sns.distplot(projections, hist=False, label=label, ax=ax)
+
+        plt.axvline(0, color='k', linestyle='--')
+
+        plt.title('← {} {} {} →'.format(self.negative_end,
+                                        ' ' * 20,
+                                        self.positive_end))
+        plt.xlabel('Direction Projection')
+        plt.ylabel('Density')
+        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+        return ax
+
+    def __errorChecking(
+        self, 
+        word: str
+    ) -> str:
+
+        out_msj = ""
+
+        if not word:
+            out_msj = ['EMBEDDING_NO_WORD_PROVIDED']
+        else:
+            if word not in self.embedding:
+                out_msj = ['EMBEDDING_WORD_OOV', word]
+
+        return self.errorManager.process(out_msj)
+
+    def check_oov(
+        self, 
+        wordlists: List[str]
+    ) -> str:
+
+        for wordlist in wordlists:
+            for word in wordlist:
+                msg = self.__errorChecking(word)
+                if msg:
+                    return msg
+        return None
+    
+class WEBiasExplorer2Spaces(WordBiasExplorer):
+    def __init__(
+        self, 
+        embedding,      # Embedding class instance
+        errorManager    # ErrorManager class instance
+    ) -> None:
+
+        super().__init__(embedding, errorManager)
+
+    def calculate_bias(
+        self,
+        wordlist_to_diagnose: List[str],
+        wordlist_right: List[str],
+        wordlist_left: List[str]
+    ) -> plt.Figure:
+
+        wordlists = [wordlist_to_diagnose, wordlist_right, wordlist_left] 
+        
+        for wordlist in wordlists:
+            if not wordlist:
+                raise Exception('At least one word should be in the to diagnose list, bias 1 list and bias 2 list')
+        
+        err = self.check_oov(wordlists)
+        if err:
+            raise Exception(err)
+
+        return self.get_bias_plot(
+                wordlist_to_diagnose,
+                definitional=(wordlist_left, wordlist_right),
+                method='sum',
+                n_extreme=10
+            )
+
+    def get_bias_plot(
+        self,
+        wordlist_to_diagnose: List[str],
+        definitional: Tuple[List[str], List[str]],
+        method: str='sum',
+        n_extreme: int=10,
+        figsize: Tuple[int, int]=(10, 10)
+    ) -> plt.Figure:
+
+        fig, ax = plt.subplots(1, figsize=figsize)
+        self.method = method
+        self.plot_projection_scores(
+            definitional,
+            wordlist_to_diagnose, n_extreme, ax=ax,)
+
+        fig.tight_layout()
+        fig.canvas.draw()
+
+        return fig
+
+    def plot_projection_scores(
+        self, 
+        definitional: Tuple[List[str], List[str]],
+        words: List[str], 
+        n_extreme: int=10,
+        ax: plt.Axes=None, 
+        axis_projection_step: float=None
+    ) -> plt.Axes:
+
+        """Plot the projection scalar of words on the direction.
+        :param list words: The words tor project
+        :param int or None n_extreme: The number of extreme words to show
+        :return: The ax object of the plot
+        """
+        name_left = ', '.join(definitional[0])
+        name_right = ', '.join(definitional[1])
+
+        self._identify_direction(name_left, name_right,
+                                 definitional=definitional,
+                                 method='sum')
+
+        self._is_direction_identified()
+
+        projections_df = self._calc_projection_scores(words)
+        projections_df['projection'] = projections_df['projection'].round(2)
+
+        if n_extreme is not None:
+            projections_df = take_two_sides_extreme_sorted(projections_df,
+                                                           n_extreme=n_extreme)
+
+        if ax is None:
+            _, ax = plt.subplots(1)
+
+        if axis_projection_step is None:
+            axis_projection_step = 0.1
+
+        cmap = plt.get_cmap('RdBu')
+        projections_df['color'] = ((projections_df['projection'] + 0.5)
+                                   .apply(cmap))
+
+        most_extream_projection = np.round(
+            projections_df['projection']
+            .abs()
+            .max(),
+            decimals=1)
+
+        sns.barplot(x='projection', y='word', data=projections_df,
+                    palette=projections_df['color'])
+
+        plt.xticks(np.arange(-most_extream_projection,
+                             most_extream_projection + axis_projection_step,
+                             axis_projection_step))
+        xlabel = ('← {} {} {} →'.format(self.negative_end,
+                                        ' ' * 20,
+                                        self.positive_end))
+
+        plt.xlabel(xlabel)
+        plt.ylabel('Words')
+
+        return ax
+
+
+class WEBiasExplorer4Spaces(WordBiasExplorer):
+    def __init__(
+        self, 
+        embedding,      # Embedding Class instance
+        errorManager    # ErrorManager class instance
+    ) -> None:
+
+        super().__init__(embedding, errorManager)
+
+    def calculate_bias(
+        self,
+        wordlist_to_diagnose: List[str],
+        wordlist_right: List[str],
+        wordlist_left: List[str],
+        wordlist_top: List[str],
+        wordlist_bottom: List[str],
+    ) -> plt.Figure:
+
+        wordlists = [
+            wordlist_to_diagnose,
+            wordlist_left,
+            wordlist_right,
+            wordlist_top,
+            wordlist_bottom
+        ]
+
+        for wordlist in wordlists:
+            if not wordlist:
+                raise Exception('To plot with 4 spaces, you must enter at least one word in all lists')
+
+        err = self.check_oov(wordlists)
+        if err:
+            raise Exception(err)
+
+        return self.get_bias_plot(
+                wordlist_to_diagnose,
+                definitional_1=(wordlist_right, wordlist_left),
+                definitional_2=(wordlist_top, wordlist_bottom),
+                method='sum',
+                n_extreme=10
+            )
+
+    def get_bias_plot(
+        self,
+        wordlist_to_diagnose: List[str],
+        definitional_1: Tuple[List[str], List[str]],
+        definitional_2: Tuple[List[str], List[str]],
+        method: str='sum',
+        n_extreme: int=10,
+        figsize: Tuple[int, int]=(10, 10)
+    ) -> plt.Figure:
+
+        fig, ax = plt.subplots(1, figsize=figsize)
+        self.method = method
+        self.plot_projection_scores(
+            definitional_1,
+            definitional_2,
+            wordlist_to_diagnose, n_extreme, ax=ax,)
+        fig.canvas.draw()
+
+        return fig
+
+    def plot_projection_scores(
+        self, 
+        definitional_1: Tuple[List[str], List[str]], 
+        definitional_2: Tuple[List[str], List[str]],
+        words: List[str], 
+        n_extreme: int=10,
+        ax: plt.Axes=None, 
+        axis_projection_step: float=None
+    ) -> plt.Axes:
+
+        """Plot the projection scalar of words on the direction.
+        :param list words: The words tor project
+        :param int or None n_extreme: The number of extreme words to show
+        :return: The ax object of the plot
+        """
+
+        name_left = ', '.join(definitional_1[1])
+        name_right = ', '.join(definitional_1[0])
+
+        self._identify_direction(name_left, name_right,
+                                 definitional=definitional_1,
+                                 method='sum')
+
+        self._is_direction_identified()
+
+        projections_df = self._calc_projection_scores(words)
+        projections_df['projection_x'] = projections_df['projection'].round(2)
+
+        name_top = ', '.join(definitional_2[1])
+        name_bottom = ', '.join(definitional_2[0])
+        self._identify_direction(name_top, name_bottom,
+                                 definitional=definitional_2,
+                                 method='sum')
+
+        self._is_direction_identified()
+
+        projections_df['projection_y'] = self._calc_projection_scores(words)[
+            'projection'].round(2)
+
+        if n_extreme is not None:
+            projections_df = take_two_sides_extreme_sorted(projections_df,
+                                                           n_extreme=n_extreme)
+
+        if ax is None:
+            _, ax = plt.subplots(1)
+
+        if axis_projection_step is None:
+            axis_projection_step = 0.1
+
+        cmap = plt.get_cmap('RdBu')
+        projections_df['color'] = ((projections_df['projection'] + 0.5)
+                                   .apply(cmap))
+        most_extream_projection = np.round(
+            projections_df['projection']
+            .abs()
+            .max(),
+            decimals=1
+        )
+        
+        sns.scatterplot(x='projection_x', 
+                        y='projection_y', 
+                        data=projections_df,
+                        # color=list(projections_df['color'].to_list()), # No se distinguen los colores
+                        color='blue'
+        )
+
+        plt.xticks(np.arange(-most_extream_projection,
+                             most_extream_projection + axis_projection_step,
+                             axis_projection_step))
+        for _, row in (projections_df.iterrows()):
+            ax.annotate(
+                row['word'], (row['projection_x'], row['projection_y']))
+        x_label = '← {} {} {} →'.format(name_left,
+                                        ' ' * 20,
+                                        name_right)
+
+        y_label = '← {} {} {} →'.format(name_top,
+                                        ' ' * 20,
+                                        name_bottom)
+
+        plt.xlabel(x_label)
+        ax.xaxis.set_label_position('bottom')
+        ax.xaxis.set_label_coords(.5, 0)
+
+        plt.ylabel(y_label)
+        ax.yaxis.set_label_position('left')
+        ax.yaxis.set_label_coords(0, .5)
+
+        ax.spines['left'].set_position('center')
+        ax.spines['bottom'].set_position('center')
+
+        ax.set_xticks([])
+        ax.set_yticks([])
+
+        return ax

modules/module_ErrorManager.py

@@ -0,0 +1,34 @@
+import pandas as pd
+from typing import List
+
+class ErrorManager:
+    def __init__(
+        self, 
+        path: str,
+        str_to_prepend: str="<center><h3>",
+        str_to_append: str="</h3></center>"
+    ) -> None:
+
+        self.error2text = pd.read_json(path)["errors"]
+        self.str_to_prepend = str_to_prepend
+        self.str_to_append  = str_to_append
+
+    def __get_text_from_code(
+        self, 
+        error_info: str
+    ) -> str:
+
+        error_code = error_info[0]
+        error_args = error_info[1:]
+        return str(self.error2text[error_code]).format(*error_args)
+
+    def process(
+        self, 
+        error_info: List[str],
+    ) -> str:
+
+        if not error_info:
+            return ""
+        
+        error = self.__get_text_from_code(error_info=error_info)
+        return self.str_to_prepend + error + self.str_to_append

modules/module_WordExplorer.py

@@ -0,0 +1,255 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from numpy.linalg import norm
+
+import matplotlib as mpl
+mpl.use('Agg')
+from typing import List, Dict, Tuple
+
+
+class WordToPlot:
+    def __init__(
+        self,
+        word: str,
+        color: str,
+        bias_space: int,
+        alpha: float
+    ) -> None:
+
+        self.word = word
+        self.color = color
+        self.bias_space = bias_space
+        self.alpha = alpha
+
+
+class WordExplorer:
+    def __init__(
+        self,
+        embedding,      # Embedding Class instance
+        errorManager    # ErrorManager class instance
+    ) -> None:
+
+        self.embedding = embedding
+        self.errorManager = errorManager
+
+    def __errorChecking(
+        self,
+        word: str
+    ) -> str:
+
+        out_msj = ""
+
+        if not word:
+            out_msj = ['EMBEDDING_NO_WORD_PROVIDED']
+        else:
+            if word not in self.embedding:
+                out_msj = ['EMBEDDING_WORD_OOV', word]
+
+        return self.errorManager.process(out_msj)
+
+    def check_oov(
+        self, 
+        wordlists: List[List[str]]
+    ) -> str:
+
+        for wordlist in wordlists:
+            for word in wordlist:
+                msg = self.__errorChecking(word)
+                if msg:
+                    return msg
+        return None
+
+    def get_neighbors(
+        self, 
+        word: str, 
+        n_neighbors: int, 
+        nn_method: str
+    ) -> List[str]:
+
+        err = self.check_oov([[word]])
+        if err:
+            raise Exception(err)
+        
+        return self.embedding.getNearestNeighbors(word, n_neighbors, nn_method)
+
+    def get_df(
+        self, 
+        words_embedded: np.ndarray, 
+        processed_word_list: List[str]
+    ) -> pd.DataFrame:
+
+        df = pd.DataFrame(words_embedded)
+
+        df['word'] = [wtp.word for wtp in processed_word_list]
+        df['color'] = [wtp.color for wtp in processed_word_list]
+        df['alpha'] = [wtp.alpha for wtp in processed_word_list]
+        df['word_bias_space'] = [wtp.bias_space for wtp in processed_word_list]
+        return df
+
+    def get_plot(
+        self,
+        data: pd.DataFrame,
+        processed_word_list: List[str],
+        words_embedded: np.ndarray,
+        color_dict: Dict,
+        n_neighbors: int,
+        n_alpha: float,
+        fontsize: int=18,
+        figsize: Tuple[int, int]=(20, 15)
+    ):
+
+        fig, ax = plt.subplots(figsize=figsize)
+
+        sns.scatterplot(
+            data=data[data['alpha'] == 1],
+            x=0,
+            y=1,
+            style='word_bias_space',
+            hue='word_bias_space',
+            ax=ax,
+            palette=color_dict
+        )
+
+        if n_neighbors > 0:
+            sns.scatterplot(
+                data=data[data['alpha'] != 1],
+                x=0,
+                y=1,
+                style='color',
+                hue='word_bias_space',
+                ax=ax,
+                alpha=n_alpha,
+                legend=False,
+                palette=color_dict
+            )
+            
+        for i, wtp in enumerate(processed_word_list):
+            x, y = words_embedded[i, :]
+            ax.annotate(
+                wtp.word, 
+                xy=(x, y), 
+                xytext=(5, 2), 
+                color=wtp.color,
+                textcoords='offset points',
+                ha='right', 
+                va='bottom', 
+                size=fontsize, 
+                alpha=wtp.alpha
+            )
+
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.set_xlabel('')
+        ax.set_ylabel('')
+        fig.tight_layout()
+
+        return fig
+
+    def plot_projections_2d(
+        self,
+        wordlist_0: List[str],
+        wordlist_1: List[str]=[],
+        wordlist_2: List[str]=[],
+        wordlist_3: List[str]=[],
+        wordlist_4: List[str]=[],
+        **kwargs
+    ):
+
+        # convertirlas a vector
+        choices = [0, 1, 2, 3, 4]
+        wordlist_choice = [
+            wordlist_0,
+            wordlist_1,
+            wordlist_2,
+            wordlist_3,
+            wordlist_4
+        ]
+
+        err = self.check_oov(wordlist_choice)
+        if err:
+            raise Exception(err)
+
+        color_dict = {
+            0: kwargs.get('color_wordlist_0', '#000000'),
+            1: kwargs.get('color_wordlist_1', '#1f78b4'),
+            2: kwargs.get('color_wordlist_2', '#33a02c'),
+            3: kwargs.get('color_wordlist_3', '#e31a1c'),
+            4: kwargs.get('color_wordlist_4', '#6a3d9a')
+        }
+
+        n_neighbors = kwargs.get('n_neighbors', 0)
+        n_alpha = kwargs.get('n_alpha', 0.3)
+
+        processed_word_list = []
+        for word_list_to_process, color in zip(wordlist_choice, choices):
+            for word in word_list_to_process:
+                processed_word_list.append(
+                    WordToPlot(word, color_dict[color], color, 1)
+                )
+
+                if n_neighbors > 0:
+                    neighbors = self.get_neighbors(
+                        word,
+                        n_neighbors=n_neighbors,
+                        nn_method=kwargs.get('nn_method', 'sklearn')
+                    )
+
+                    for n in neighbors:
+                        if n not in [wtp.word for wtp in processed_word_list]:
+                            processed_word_list.append(
+                                WordToPlot(n, color_dict[color], color, n_alpha)
+                            )
+
+        if not processed_word_list:
+            raise Exception('Only empty lists were passed')
+
+        words_embedded = np.array(
+            [self.embedding.getPCA(wtp.word) for wtp in processed_word_list]
+        )
+
+        data = self.get_df(
+            words_embedded, 
+            processed_word_list
+        )
+
+        fig = self.get_plot(
+            data, 
+            processed_word_list, 
+            words_embedded,
+            color_dict, 
+            n_neighbors, 
+            n_alpha,
+            kwargs.get('fontsize', 18),
+            kwargs.get('figsize', (20, 15))
+        )
+
+        plt.show()
+        return fig
+
+    # ToDo: No hay usos de este método. ¿Borrar?
+    def doesnt_match(
+        self, 
+        wordlist: List[str]
+    ) -> str:
+
+        err = self.check_oov([wordlist])
+        if err:
+            raise Exception(err)
+
+        words_emb = np.array([self.embedding.getEmbedding(word)
+                             for word in wordlist])
+        mean_vec = np.mean(words_emb, axis=0)
+
+        doesnt_match = ""
+        farthest_emb = 1.0
+        for word in wordlist:
+            word_emb = self.embedding.getEmbedding(word)
+            cos_sim = np.dot(mean_vec, word_emb) / \
+                (norm(mean_vec)*norm(word_emb))
+            if cos_sim <= farthest_emb:
+                farthest_emb = cos_sim
+                doesnt_match = word
+
+        return doesnt_match

modules/module_ann.py

@@ -0,0 +1,91 @@
+import time
+from tqdm import tqdm
+from annoy import AnnoyIndex
+from typing import List
+
+class TicToc:
+    def __init__(
+        self
+    ) -> None:
+
+        self.i = None
+
+    def start(
+        self
+    ) -> None:
+
+        self.i = time.time()
+
+    def stop(
+        self
+    ) -> None:
+
+        f = time.time()
+        print(f - self.i, "seg.")
+
+
+class Ann:
+    def __init__(
+        self, 
+        words: List[str], 
+        vectors: List, 
+        coord: List, 
+    ) -> None:
+
+        self.words = words
+        self.vectors = vectors
+        self.coord = coord
+        self.tree = None
+
+        self.tt = TicToc()
+        self.availables_metrics = ['angular','euclidean','manhattan','hamming','dot']
+
+    def init(self, 
+        n_trees: int=10, 
+        metric: str='angular', 
+        n_jobs: int=-1  # n_jobs=-1 Run over all CPU availables
+    ) -> None:
+
+        assert(metric in self.availables_metrics), f"Error: The value of the parameter 'metric' can only be {self.availables_metrics}!"
+
+        print("\tInit tree...")
+        self.tt.start()
+        self.tree = AnnoyIndex(len(self.vectors[0]), metric=metric)
+        for i, v in tqdm(enumerate(self.vectors), total=len(self.vectors)):
+            self.tree.add_item(i, v)
+        self.tt.stop()
+
+        print("\tBuild tree...")
+        self.tt.start()
+        self.tree.build(n_trees=n_trees, n_jobs=n_jobs)
+        self.tt.stop()
+
+    def __getWordId(
+        self, 
+        word: str
+    ) -> int:
+
+        word_id = None
+        try:
+            word_id = self.words.index(word)
+        except:
+            pass
+        return word_id
+
+    def get(
+        self, 
+        word: str, 
+        n_neighbors: int=10
+    ) -> List[str]:
+        
+        word_id = self.__getWordId(word)
+        neighbors_list = None
+
+        if word_id != None:
+            neighbords_id = self.tree.get_nns_by_item(word_id, n_neighbors + 1)
+            neighbors_list = [self.words[idx] for idx in neighbords_id][1:]
+
+        else:
+            print(f"The word '{word}' does not exist")
+
+        return neighbors_list

modules/module_connection.py

@@ -0,0 +1,517 @@
+import csv, os
+import pandas as pd
+import gradio as gr
+from abc import ABC
+from modules.utils import DateLogs
+from typing import List, Tuple, Any
+from modules.module_WordExplorer import WordExplorer
+from modules.module_BiasExplorer import WEBiasExplorer2Spaces, WEBiasExplorer4Spaces
+from modules.module_word2Context import Word2Context
+from modules.module_rankSents import RankSents
+from modules.module_crowsPairs import CrowsPairs
+from modules.module_ErrorManager import ErrorManager
+
+
+class Connector(ABC):
+
+    def __init__(
+        self,
+        lang: str
+    ) -> None:
+
+        self.datalog = DateLogs()
+        self.log_folder = 'logs'
+
+        if not hasattr(Connector, 'errorManager'):
+            Connector.errorManager = ErrorManager(
+                path=f"modules/error_messages/{lang}.json"
+            )
+
+    def parse_word(
+        self, 
+        word: str
+    ) -> str:
+        
+        return word.lower().strip()
+
+    def parse_words(
+        self, 
+        array_in_string: str
+    ) -> List[str]:
+
+        words = array_in_string.strip()
+        if not words:
+            return []
+
+        words = [
+            self.parse_word(word) 
+            for word in words.split(',') if word.strip() != ''
+        ]
+        return words
+    
+    def logs_save(
+        self,
+        file_name: str,
+        headers: List[str]=None,
+        *data: List[Any]
+    ) -> None:
+
+        if file_name is None:
+            return None
+
+        if not os.path.exists(self.log_folder):
+            print(f"Creating logs folder '{self.log_folder}' ...")
+            os.mkdir(self.log_folder)
+
+        file_path = os.path.join(self.log_folder, file_name+'.csv')
+        f_out = None
+
+        if not os.path.exists(file_path):
+            print(f"Creating new '{file_name}' logs file...")
+
+            with open(file_path, mode='w', encoding='UTF8') as f_out:
+                # Create the csv writer
+                writer = csv.writer(f_out)
+                
+                # Write the header
+                if headers is None:
+                    headers = [
+                        "input_" + str(ith)  
+                        for ith,_ in enumerate(data)
+                    ]
+                headers = headers + ["datatime"]
+
+                writer.writerow(headers)
+                    
+        with open(file_path, mode='a', encoding='UTF8') as f_out:
+            # Create the csv writer
+            writer = csv.writer(f_out)
+
+            # Write a row to the csv file
+            data = list(data) + [ self.datalog.full() ]
+            writer.writerow(data)
+
+            print(f"Logs: '{file_path}' successfully saved!")
+
+class WordExplorerConnector(Connector):
+    def __init__(
+        self, 
+        **kwargs
+    ) -> None:
+
+        Connector.__init__(self, kwargs.get('lang', 'en'))
+        embedding = kwargs.get('embedding', None)
+        self.logs_file_name = kwargs.get('logs_file_name', None)
+        self.headers = [
+            "word_list_to_diagnose",
+            "word_list_1",
+            "word_list_2",
+            "word_list_3",
+            "word_list_4"
+        ]
+        
+        if embedding is None:
+            raise KeyError
+        
+        self.word_explorer = WordExplorer(
+            embedding=embedding,
+            errorManager=self.errorManager
+        )
+
+    def plot_proyection_2d( 
+        self,
+        wordlist_0: str,
+        wordlist_1: str,
+        wordlist_2: str,
+        wordlist_3: str,
+        wordlist_4: str,
+        color_wordlist_0: str,
+        color_wordlist_1: str,
+        color_wordlist_2: str,
+        color_wordlist_3: str,
+        color_wordlist_4: str,
+        n_alpha: float,
+        fontsize: int,
+        n_neighbors: int
+    ) -> Tuple:
+
+        err = ""
+        neighbors_method = 'sklearn'
+        wordlist_0 = self.parse_words(wordlist_0)
+        wordlist_1 = self.parse_words(wordlist_1)
+        wordlist_2 = self.parse_words(wordlist_2)
+        wordlist_3 = self.parse_words(wordlist_3)
+        wordlist_4 = self.parse_words(wordlist_4)
+
+        if not (wordlist_0 or wordlist_1 or wordlist_2 or wordlist_1 or wordlist_4):
+            err = self.errorManager.process(['CONECTION_NO_WORD_ENTERED'])
+            return None, err
+        
+        err = self.word_explorer.check_oov(
+            [wordlist_0, wordlist_1, wordlist_2, wordlist_3, wordlist_4]
+        )
+
+        if err:
+            return None, err
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name,
+            self.headers,
+            wordlist_0,
+            wordlist_1,
+            wordlist_2,
+            wordlist_3,
+            wordlist_4,
+        )
+
+        fig = self.word_explorer.plot_projections_2d(
+            wordlist_0,
+            wordlist_1,
+            wordlist_2,
+            wordlist_3,
+            wordlist_4,
+            color_wordlist_0=color_wordlist_0,
+            color_wordlist_1=color_wordlist_1,
+            color_wordlist_2=color_wordlist_2,
+            color_wordlist_3=color_wordlist_3,
+            color_wordlist_4=color_wordlist_4,
+            n_alpha=n_alpha,
+            fontsize=fontsize,
+            n_neighbors=n_neighbors,
+            nn_method = neighbors_method
+        )
+
+        return fig, err
+
+class BiasWordExplorerConnector(Connector):
+
+    def __init__(
+        self, 
+        **kwargs
+    ) -> None:
+
+        Connector.__init__(self, kwargs.get('lang', 'en'))
+        embedding = kwargs.get('embedding', None)
+        self.logs_file_name = kwargs.get('logs_file_name', None)
+        self.headers = [
+            "word_list_to_diagnose",
+            "word_list_1",
+            "word_list_2",
+            "word_list_3",
+            "word_list_4",
+            "plot_space"
+        ]
+
+        if embedding is None:
+            raise KeyError
+
+        self.bias_word_explorer_2_spaces = WEBiasExplorer2Spaces(
+            embedding=embedding,
+            errorManager=self.errorManager
+        )
+        self.bias_word_explorer_4_spaces = WEBiasExplorer4Spaces(
+            embedding=embedding,
+            errorManager=self.errorManager
+        )
+
+    def calculate_bias_2d(
+        self,
+        wordlist_1: str,
+        wordlist_2: str,
+        to_diagnose_list: str
+    ) -> Tuple:
+
+        err = ""
+        wordlist_1 = self.parse_words(wordlist_1)
+        wordlist_2 = self.parse_words(wordlist_2)
+        to_diagnose_list = self.parse_words(to_diagnose_list)
+
+        word_lists = [wordlist_1, wordlist_2, to_diagnose_list]
+        for _list in word_lists:
+            if not _list:
+                err = self.errorManager.process(['BIASEXPLORER_NOT_ENOUGH_WORD_2_KERNELS'])
+        if err:
+            return None, err
+
+        err = self.bias_word_explorer_2_spaces.check_oov(word_lists)
+        if err:
+            return None, err
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name,
+            self.headers,
+            to_diagnose_list,
+            wordlist_1,
+            wordlist_2,
+            "",
+            "",
+            "2d"
+        )
+
+        fig = self.bias_word_explorer_2_spaces.calculate_bias(
+            to_diagnose_list, 
+            wordlist_1, 
+            wordlist_2
+        )
+
+        return fig, err
+
+    def calculate_bias_4d(
+        self,
+        wordlist_1: str,
+        wordlist_2: str,
+        wordlist_3: str,
+        wordlist_4: str,
+        to_diagnose_list: str
+    ) -> Tuple:
+
+        err = ""
+        wordlist_1 = self.parse_words(wordlist_1)
+        wordlist_2 = self.parse_words(wordlist_2)
+        wordlist_3 = self.parse_words(wordlist_3)
+        wordlist_4 = self.parse_words(wordlist_4)
+        to_diagnose_list = self.parse_words(to_diagnose_list)
+
+        wordlists = [wordlist_1, wordlist_2, wordlist_3, wordlist_4, to_diagnose_list]
+        for _list in wordlists:
+            if not _list:
+                err = self.errorManager.process(['BIASEXPLORER_NOT_ENOUGH_WORD_4_KERNELS'])
+        if err:
+            return None, err
+
+        err = self.bias_word_explorer_4_spaces.check_oov(wordlists)
+        if err:
+            return None, err
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name,
+            self.headers,
+            to_diagnose_list, 
+            wordlist_1, 
+            wordlist_2, 
+            wordlist_3, 
+            wordlist_4,
+            "4d"
+        )
+
+        fig = self.bias_word_explorer_4_spaces.calculate_bias(
+            to_diagnose_list, 
+            wordlist_1, 
+            wordlist_2, 
+            wordlist_3, 
+            wordlist_4
+        )
+        
+        return fig, err
+
+class Word2ContextExplorerConnector(Connector):
+    def __init__(
+        self, 
+        **kwargs
+    ) -> None:
+
+        Connector.__init__(self, kwargs.get('lang', 'en'))
+        vocabulary = kwargs.get('vocabulary', None)
+        context = kwargs.get('context', None)
+        self.logs_file_name = kwargs.get('logs_file_name', None)
+        self.headers = [
+            "word",
+            "subsets_choice"
+        ]
+
+        if vocabulary is None or context is None:
+            raise KeyError
+
+        self.word2context_explorer = Word2Context(
+            context,
+            vocabulary,
+            errorManager=self.errorManager
+        )
+
+    def get_word_info(
+        self, 
+        word: str
+    ) -> Tuple:
+    
+        err = ""
+        contexts = pd.DataFrame([], columns=[''])
+        subsets_info = ""
+        distribution_plot = None
+        word_cloud_plot = None
+        subsets_choice = gr.CheckboxGroup.update(choices=[])
+
+        err = self.word2context_explorer.errorChecking(word)
+        if err:
+            return err, contexts, subsets_info, distribution_plot, word_cloud_plot, subsets_choice
+
+        word = self.parse_word(word)
+
+        subsets_info, subsets_origin_info = self.word2context_explorer.getSubsetsInfo(word)
+
+        clean_keys = [key.split(" ")[0].strip() for key in subsets_origin_info]
+        subsets_choice = gr.CheckboxGroup.update(choices=clean_keys)
+
+        distribution_plot = self.word2context_explorer.genDistributionPlot(word)
+        word_cloud_plot = self.word2context_explorer.genWordCloudPlot(word)
+
+        return err, contexts, subsets_info, distribution_plot, word_cloud_plot, subsets_choice
+
+    def get_word_context(
+        self,
+        word: str,
+        n_context: int,
+        subset_choice: List[str]
+    ) -> Tuple:
+
+        word = self.parse_word(word)
+        err = ""
+        contexts = pd.DataFrame([], columns=[''])
+
+        err = self.word2context_explorer.errorChecking(word)
+        if err:
+            return err, contexts
+
+        if len(subset_choice) > 0:
+            ds = self.word2context_explorer.findSplits(word, subset_choice)
+        else:
+            err = self.errorManager.process(['WORD2CONTEXT_WORDS_OR_SET_MISSING'])
+            return err, contexts
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name, 
+            self.headers,
+            word,
+            subset_choice
+        )
+
+        list_of_contexts = self.word2context_explorer.getContexts(word, n_context, ds)
+
+        contexts = pd.DataFrame(list_of_contexts, columns=['#','contexto','conjunto'])
+        contexts["buscar"] = contexts.contexto.apply(lambda text: self.word2context_explorer.genWebLink(text))
+
+        return err, contexts
+
+class PhraseBiasExplorerConnector(Connector):
+    def __init__(
+        self, 
+        **kwargs
+    ) -> None:
+
+        Connector.__init__(self, kwargs.get('lang', 'en'))
+        language_model = kwargs.get('language_model', None)
+        lang =  kwargs.get('lang', None)
+        self.logs_file_name = kwargs.get('logs_file_name', None)
+        self.headers = [
+            "sent",
+            "word_list"
+        ]
+
+        if language_model is None or lang is None:
+            raise KeyError
+
+        self.phrase_bias_explorer = RankSents(
+            language_model=language_model,
+            lang=lang,
+            errorManager=self.errorManager
+        )
+
+    def rank_sentence_options(
+        self,
+        sent: str,
+        word_list: str,
+        banned_word_list: str,
+        useArticles: bool,
+        usePrepositions: bool,
+        useConjunctions: bool
+    ) -> Tuple:
+
+        sent = " ".join(sent.strip().replace("*"," * ").split())
+
+        err = self.phrase_bias_explorer.errorChecking(sent)
+        if err:
+            return err, "", ""
+
+        word_list = self.parse_words(word_list)
+        banned_word_list = self.parse_words(banned_word_list)
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name, 
+            self.headers,
+            sent,
+            word_list
+        )
+
+        all_plls_scores = self.phrase_bias_explorer.rank(
+            sent, 
+            word_list, 
+            banned_word_list, 
+            useArticles, 
+            usePrepositions, 
+            useConjunctions
+        )
+        
+        all_plls_scores = self.phrase_bias_explorer.Label.compute(all_plls_scores)
+        return err, all_plls_scores, ""
+
+class CrowsPairsExplorerConnector(Connector):
+    def __init__(
+        self, 
+        **kwargs
+    ) -> None:
+
+        Connector.__init__(self, kwargs.get('lang', 'en'))
+        language_model = kwargs.get('language_model', None)
+        self.logs_file_name = kwargs.get('logs_file_name', None)
+        self.headers = [
+            "sent_1",
+            "sent_2",
+            "sent_3",
+            "sent_4",
+            "sent_5",
+            "sent_6",
+        ]
+
+        if language_model is None:
+            raise KeyError
+        
+        self.crows_pairs_explorer = CrowsPairs(
+            language_model=language_model,
+            errorManager=self.errorManager
+        )
+
+    def compare_sentences(
+        self,
+        sent0: str,
+        sent1: str,
+        sent2: str,
+        sent3: str,
+        sent4: str,
+        sent5: str
+    ) -> Tuple:
+
+        sent_list = [sent0, sent1, sent2, sent3, sent4, sent5]
+        err = self.crows_pairs_explorer.errorChecking(
+            sent_list
+        )
+
+        if err:
+            return err, "", ""
+
+        # Save inputs in logs file
+        self.logs_save(
+            self.logs_file_name, 
+            self.headers,
+            sent_list
+        )
+
+        all_plls_scores = self.crows_pairs_explorer.rank(
+            sent_list
+        )
+        
+        all_plls_scores = self.crows_pairs_explorer.Label.compute(all_plls_scores)
+        return err, all_plls_scores, ""

modules/module_crowsPairs.py

@@ -0,0 +1,53 @@
+from modules.module_customPllLabel import CustomPllLabel
+from modules.module_pllScore import PllScore
+from typing import Dict, List
+
+class CrowsPairs:
+    def __init__(
+        self, 
+        language_model, # LanguageModel class instance
+        errorManager    # ErrorManager class instance
+    ) -> None:
+
+        self.Label = CustomPllLabel()
+        self.pllScore = PllScore(
+            language_model=language_model
+        )
+        self.errorManager = errorManager
+
+    def errorChecking(
+        self, 
+        sent_list: List[str],
+    ) -> str:
+
+        out_msj = ""
+
+        mandatory_sents = [0,1]
+        for sent_id, sent in enumerate(sent_list):
+            c_sent = sent.strip()
+            if c_sent:
+                if not self.pllScore.sentIsCorrect(c_sent):
+                    out_msj = ['CROWS-PAIRS_BAD_FORMATTED_SENTENCE', sent_id+1]
+                    break
+            else:
+                if sent_id in mandatory_sents:
+                    out_msj = ['CROWS-PAIRS_MANDATORY_SENTENCE_MISSING', sent_id+1]
+                    break
+        
+        return self.errorManager.process(out_msj)
+
+    def rank(
+        self, 
+        sent_list: List[str],
+    ) -> Dict[str, float]:
+
+        err = self.errorChecking(sent_list)
+        if err:
+            raise Exception(err)
+        
+        all_plls_scores = {}
+        for sent in sent_list:
+            if sent:
+                all_plls_scores[sent] = self.pllScore.compute(sent)
+
+        return all_plls_scores

modules/module_customPllLabel.py

@@ -0,0 +1,110 @@
+from typing import List, Dict
+
+class CustomPllLabel:
+    def __init__(
+        self
+    ) -> None:
+
+        self.html_head = """
+        <html>
+            <head>
+                <meta charset="utf-8">
+                <meta name="viewport" content="width=device-width, initial-scale=1">
+                <style>
+                    progress {
+                        -webkit-appearance: none;
+                    }
+                    progress::-webkit-progress-bar {
+                        background-color: #666;
+                        border-radius: 7px;
+                    }
+                    #myturn span {
+                        position: absolute;
+                        display: inline-block;
+                        color: #fff;
+                        text-align: right;
+                        font-size:15px
+                    }
+                    #myturn {
+                        display: block;
+                        position: relative;
+                        margin: auto;
+                        width: 90%;
+                        padding: 2px;
+                    }
+                    progress {
+                        width:100%;
+                        height:20px;
+                        border-radius: 7px;
+                    }
+                </style>
+            </head>
+            <body>
+        """
+
+        self.html_footer ="</body></html>"
+    
+    def __progressbar(
+        self, 
+        percentage: int, 
+        sent: str, 
+        ratio: float, 
+        score: float, 
+        size: int=15
+    ) -> str:
+
+        html = f"""
+        <div id="myturn">
+            <span data-value="{percentage/2}" style="width:{percentage/2}%;">
+                <strong>x{round(ratio,3)}</strong>
+            </span>
+            <progress value="{percentage}" max="100"></progress>
+            <p style='font-size:22px; padding:2px;'>{sent}</p>
+        </div>
+        """
+        return html
+
+    def __render(
+        self, 
+        sents: List[str],
+        scores: List[float], 
+        ratios: List[float]
+    ) -> str:
+
+        max_ratio = max(ratios)
+        ratio2percentage = lambda ratio: int(ratio*100/max_ratio)
+
+        html = ""
+        for sent, ratio, score in zip(sents, ratios, scores):
+            html += self.__progressbar(
+                percentage=ratio2percentage(ratio), 
+                sent=sent,
+                ratio=ratio, 
+                score=score
+            )
+
+        return self.html_head + html + self.html_footer
+    
+    def __getProportions(
+        self, 
+        scores: List[float], 
+    ) -> List[float]:
+    
+        min_score = min(scores)
+        return [min_score/s for s in scores]
+
+    def compute(
+        self, 
+        pll_dict: Dict[str, float]
+    ) -> str:
+
+        sorted_pll_dict = dict(sorted(pll_dict.items(), key=lambda x: x[1], reverse=True))
+        
+        sents = list(sorted_pll_dict.keys())
+        # Scape < and > marks from hightlight word/s
+        sents = [s.replace("<","&#60;").replace(">","&#62;")for s in sents]
+
+        scores  = list(sorted_pll_dict.values())
+        ratios = self.__getProportions(scores)
+
+        return self.__render(sents, scores, ratios)

modules/module_customSubsetsLabel.py

@@ -0,0 +1,118 @@
+from typing import List, Dict
+
+class CustomSubsetsLabel:
+    def __init__(
+        self
+    ) -> None:
+
+        self.html_head = """
+        <html>
+            <head>
+                <meta charset="utf-8">
+                <meta name="viewport" content="width=device-width, initial-scale=1">
+                <style>
+                    progress {
+                        -webkit-appearance: none;
+                    }
+                    progress::-webkit-progress-bar {
+                        background-color: #666;
+                        border-radius: 7px;
+                    }
+                    progress {
+                        width:100%;
+                        height:4px;
+                        border-radius: 1px;
+                    }
+                    #myturn {
+                        display: block;
+                        position: relative;
+                        margin: auto;
+                        width: 90%;
+                        padding: 2px;
+                    }
+                </style>
+            </head>
+            <body>
+        """
+
+        self.html_footer ="</body></html>"
+
+        self.subset_links = {
+            'allwikis': "https://github.com/josecannete/wikiextractorforBERT",
+            'DGT': "http://opus.nlpl.eu/DGT.php",
+            'DOGC': "http://opus.nlpl.eu/DOGC.php",
+            'ECB': "http://opus.nlpl.eu/ECB.php",
+            'EMEA': "http://opus.nlpl.eu/EMEA.php",
+            'EUBookShop': "http://opus.nlpl.eu/EUbookshop.php",
+            'Europarl': "http://opus.nlpl.eu/Europarl.php",
+            'GlobalVoices': "http://opus.nlpl.eu/GlobalVoices.php",
+            'JRC': "http://opus.nlpl.eu/JRC-Acquis.php",
+            'multiUN': "http://opus.nlpl.eu/MultiUN.php",
+            'NewsCommentary11': "http://opus.nlpl.eu/News-Commentary-v11.php",
+            'OpenSubtitles2018': "http://opus.nlpl.eu/OpenSubtitles-v2018.php",
+            'ParaCrawl': "http://opus.nlpl.eu/ParaCrawl.php",
+            'TED': "http://opus.nlpl.eu/TED2013.php",
+            'UN': "http://opus.nlpl.eu/UN.php",
+        }
+
+    def __progressbar(
+        self, 
+        percentage: float, 
+        subset: str, 
+        freq: int, 
+        size: int=15
+    ) -> str:
+
+        html = f"""
+        <div id="myturn">
+            <progress value="{int(percentage)}" max="100"></progress>
+            <p style="text-align:left; font-size:{size}px; padding:0px;">
+                <a href="{self.subset_links[subset]}" target="_blank">
+                    <strong>{subset}</strong> <span style="font-size:{size-2}px">(Frecuencia: {freq})</span>
+                </a>
+                <span style="float:right;">
+                    <strong>{percentage}%</strong>
+                </span>
+            </p>
+        </div>
+        """
+        return html
+
+    def __render(
+        self, 
+        subsets: List[str], 
+        freqs: List[int], 
+        percentages: List[float]
+    ) -> str:
+
+        html = ""
+        for subset, freq, perc in zip(subsets, freqs, percentages):
+            html += self.__progressbar(
+                percentage=perc,
+                subset=subset,
+                freq=freq
+            )
+
+        return self.html_head + html + self.html_footer
+    
+    def compute(
+        self, 
+        subsets_dic: Dict[str, int]
+    ) -> str:
+
+        subsets_dic_info = {
+            k.split()[0]:{'freq':int(k.split()[1][1:-1]),'perc':round(v*100,2)} 
+            for k,v in subsets_dic.items()
+        }
+        
+        subsets = list(subsets_dic_info.keys())
+        freqs = [
+            d['freq'] 
+            for d in subsets_dic_info.values()
+        ]
+        percentages = [
+            d['perc'] 
+            for d in subsets_dic_info.values()
+        ]
+
+        return self.__render(subsets, freqs, percentages)

modules/module_languageModel.py

@@ -0,0 +1,27 @@
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import os
+
+# Disabling parallelism to avoid deadlocks in the hf tokenizer
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+class LanguageModel:
+    def __init__(
+        self, 
+        model_name
+    ) -> None:
+    
+        print("Downloading language model...")
+        self.__tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.__model = AutoModelForMaskedLM.from_pretrained(model_name)
+    
+    def initTokenizer(
+        self
+    ) -> AutoTokenizer:
+
+        return self.__tokenizer
+    
+    def initModel(
+        self
+    ) -> AutoModelForMaskedLM:
+
+        return self.__model

modules/module_pllScore.py

@@ -0,0 +1,147 @@
+from difflib import Differ
+import torch, re
+
+
+class PllScore:
+    def __init__(
+        self, 
+        language_model  # LanguageModel class instance
+    ) -> None:
+
+        self.tokenizer = language_model.initTokenizer()
+        self.model = language_model.initModel()
+        _ = self.model.eval()
+
+        self.logSoftmax = torch.nn.LogSoftmax(dim=-1)
+
+    def sentIsCorrect(
+        self, 
+        sent: str
+    ) -> bool:
+
+        # Mod
+        is_correct = True
+
+        # Check mark existence
+        open_mark = sent.count("<")
+        close_mark = sent.count(">")
+        total_mark = open_mark + close_mark
+        if (total_mark == 0) or (open_mark != close_mark):
+            is_correct = False
+
+        # Check existence of twin marks (ie: '<<' or '>>')
+        if is_correct:
+            left_twin = sent.count("<<")
+            rigth_twin = sent.count(">>")
+            if left_twin + rigth_twin > 0:
+                is_correct = False
+
+        if is_correct:
+            # Check balanced symbols '<' and '>'
+            stack = []
+            for c in sent:
+                if c == '<':
+                    stack.append('<')
+                elif c == '>':
+                    if len(stack) == 0:
+                        is_correct = False
+                        break
+
+                    if stack.pop() != "<":
+                        is_correct = False
+                        break
+
+            if len(stack) > 0:
+                is_correct = False
+
+        if is_correct:
+            for w in re.findall("\<.*?\>", sent):
+                # Check empty interest words
+                word = w.replace("<","").replace(">","").strip() 
+                if not word:
+                    is_correct = False
+                    break
+                
+                # Check if there are any marks inside others (ie: <this is a <sentence>>)
+                word = w.strip()[1:-1]  #Delete the first and last mark
+                if '<' in word or '>' in word:
+                    is_correct = False
+                    break
+        
+        if is_correct:
+            # Check that there is at least one uninteresting word. The next examples should not be allowed 
+            # (ie: <this is a sent>, <this> <is a sent>)
+            outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
+            outside_words = [w for w in outside_words.split() if w != ""]
+            if not outside_words:
+                is_correct = False
+
+
+        return is_correct
+
+    def compute(
+        self, 
+        sent: str
+    ) -> float:
+
+        assert(self.sentIsCorrect(sent)), f"Error: The sentence '{sent}' does not have the correct format!"
+
+        outside_words = re.sub("\<.*?\>", "", sent.replace("<", " < ").replace(">", " > "))
+        outside_words = [w for w in outside_words.split() if w != ""]
+        all_words = [w.strip() for w in sent.replace("<"," ").replace(">"," ").split() if w != ""]
+
+        tks_id_outside_words = self.tokenizer.encode(
+            " ".join(outside_words), 
+            add_special_tokens=False, 
+            truncation=True
+        )
+        tks_id_all_words = self.tokenizer.encode(
+            " ".join(all_words), 
+            add_special_tokens=False,
+            truncation=True
+        )
+
+        diff = [(tk[0], tk[2:]) for tk in Differ().compare(tks_id_outside_words, tks_id_all_words)]
+
+        cls_tk_id = self.tokenizer.cls_token_id
+        sep_tk_id = self.tokenizer.sep_token_id
+        mask_tk_id = self.tokenizer.mask_token_id
+
+        all_sent_masked = []
+        all_tks_id_masked = []
+        all_tks_position_masked = []
+
+        for i in range(0, len(diff)):
+            current_sent_masked = [cls_tk_id]
+            add_sent = True
+            for j, (mark, tk_id) in enumerate(diff):
+                if j == i:
+                    if mark == '+':
+                        add_sent = False
+                        break
+                    else:
+                        current_sent_masked.append(mask_tk_id)
+                        all_tks_id_masked.append(int(tk_id))
+                        all_tks_position_masked.append(i+1)
+                else:
+                    current_sent_masked.append(int(tk_id))
+
+            if add_sent:
+                current_sent_masked.append(sep_tk_id)
+                all_sent_masked.append(current_sent_masked)
+        
+        inputs_ids = torch.tensor(all_sent_masked)
+        attention_mask = torch.ones_like(inputs_ids)
+
+        with torch.no_grad():
+            out = self.model(inputs_ids, attention_mask)
+            logits = out.logits
+            outputs = self.logSoftmax(logits)
+
+        pll_score = 0
+        for out, tk_pos, tk_id in zip(outputs, all_tks_position_masked, all_tks_id_masked):
+            probabilities = out[tk_pos]
+            tk_prob = probabilities[tk_id]
+            pll_score += tk_prob.item()
+
+        return pll_score

modules/module_rankSents.py

@@ -0,0 +1,171 @@
+from modules.module_customPllLabel import CustomPllLabel
+from modules.module_pllScore import PllScore
+from typing import List, Dict
+import torch
+
+
+class RankSents:
+    def __init__(
+        self, 
+        language_model, # LanguageModel class instance
+        lang: str,
+        errorManager    # ErrorManager class instance
+    ) -> None:
+        
+        self.tokenizer = language_model.initTokenizer()
+        self.model = language_model.initModel()
+        _ = self.model.eval()
+
+        self.Label = CustomPllLabel()
+        self.pllScore = PllScore(
+            language_model=language_model
+        )
+        self.softmax = torch.nn.Softmax(dim=-1)
+
+        if lang == "es":
+            self.articles = [
+                'un','una','unos','unas','el','los','la','las','lo'
+            ]
+            self.prepositions = [
+                'a','ante','bajo','cabe','con','contra','de','desde','en','entre','hacia','hasta','para','por','según','sin','so','sobre','tras','durante','mediante','vía','versus'
+            ]
+            self.conjunctions = [
+                'y','o','ni','que','pero','si'
+            ]
+
+        elif lang == "en":
+            self.articles = [
+                'a','an', 'the'
+            ]
+            self.prepositions = [
+                'above', 'across', 'against', 'along', 'among', 'around', 'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'by', 'down', 'from', 'in', 'into', 'near', 'of', 'off', 'on', 'to', 'toward', 'under', 'upon', 'with', 'within'
+            ]
+            self.conjunctions = [
+                'and', 'or', 'but', 'that', 'if', 'whether'
+            ]
+
+        self.errorManager = errorManager
+
+    def errorChecking(
+        self, 
+        sent: str
+    ) -> str:
+
+        out_msj = ""
+        if not sent:
+            out_msj = ['RANKSENTS_NO_SENTENCE_PROVIDED']
+        elif sent.count("*") > 1:
+            out_msj = ['RANKSENTS_TOO_MANY_MASKS_IN_SENTENCE']
+        elif sent.count("*") == 0:
+            out_msj = ['RANKSENTS_NO_MASK_IN_SENTENCE']
+        else:
+            sent_len = len(self.tokenizer.encode(sent.replace("*", self.tokenizer.mask_token)))
+            max_len = self.tokenizer.max_len_single_sentence
+            if sent_len > max_len:
+                out_msj = ['RANKSENTS_TOKENIZER_MAX_TOKENS_REACHED', max_len]
+        
+        return self.errorManager.process(out_msj)
+
+    def getTop5Predictions(
+        self, 
+        sent: str,
+        banned_wl: List[str], 
+        articles: bool,
+        prepositions: bool,
+        conjunctions: bool
+    ) -> List[str]:
+                                
+        sent_masked = sent.replace("*", self.tokenizer.mask_token)
+        inputs = self.tokenizer.encode_plus( 
+            sent_masked,
+            add_special_tokens=True,
+            return_tensors='pt',
+            return_attention_mask=True, truncation=True
+        )
+
+        tk_position_mask = torch.where(inputs['input_ids'][0] == self.tokenizer.mask_token_id)[0].item()
+
+        with torch.no_grad():
+            out = self.model(**inputs)
+            logits = out.logits
+            outputs = self.softmax(logits)
+            outputs = torch.squeeze(outputs, dim=0)
+        
+        probabilities = outputs[tk_position_mask]
+        first_tk_id = torch.argsort(probabilities, descending=True)
+        
+        top5_tks_pred = []
+        for tk_id in first_tk_id:
+            tk_string = self.tokenizer.decode([tk_id])
+            
+            tk_is_banned = tk_string in banned_wl
+            tk_is_punctuation = not tk_string.isalnum()
+            tk_is_substring = tk_string.startswith("##")
+            tk_is_special = (tk_string in self.tokenizer.all_special_tokens)
+
+            if articles:
+                tk_is_article = tk_string in self.articles
+            else:
+                tk_is_article = False
+            
+            if prepositions:
+                tk_is_prepositions = tk_string in self.prepositions
+            else:
+                tk_is_prepositions = False
+            
+            if conjunctions:
+                tk_is_conjunctions = tk_string in self.conjunctions
+            else:
+                tk_is_conjunctions = False
+            
+            predictions_is_dessire = not any([  
+                                    tk_is_banned,
+                                    tk_is_punctuation,
+                                    tk_is_substring, 
+                                    tk_is_special, 
+                                    tk_is_article, 
+                                    tk_is_prepositions,
+                                    tk_is_conjunctions
+            ])
+
+            if predictions_is_dessire and len(top5_tks_pred) < 5:
+                top5_tks_pred.append(tk_string)
+
+            elif len(top5_tks_pred) >= 5:
+                break
+
+        return top5_tks_pred
+
+    def rank(self, 
+        sent: str, 
+        word_list: List[str]=[], 
+        banned_word_list: List[str]=[], 
+        articles: bool=False, 
+        prepositions: bool=False, 
+        conjunctions: bool=False
+    ) -> Dict[str, float]:
+        
+        err = self.errorChecking(sent)
+        if err:
+            raise Exception(err)
+
+        if not word_list:
+            word_list = self.getTop5Predictions(
+                sent,
+                banned_word_list,
+                articles,
+                prepositions,
+                conjunctions
+            )
+
+        sent_list = []
+        sent_list2print = []
+        for word in word_list:
+            sent_list.append(sent.replace("*", "<"+word+">"))
+            sent_list2print.append(sent.replace("*", "<"+word+">"))
+            
+        all_plls_scores = {}
+        for sent, sent2print in zip(sent_list, sent_list2print):
+            all_plls_scores[sent2print] = self.pllScore.compute(sent)
+
+        return all_plls_scores

modules/module_segmentedWordCloud.py

@@ -0,0 +1,80 @@
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from typing import Dict, Tuple, List
+
+
+class SimpleGroupedColorFunc(object):
+    """Create a color function object which assigns EXACT colors
+       to certain words based on the color to words mapping
+
+       Parameters
+       ----------
+       color_to_words : dict(str -> list(str))
+         A dictionary that maps a color to the list of words.
+
+       default_color : str
+         Color that will be assigned to a word that's not a member
+         of any value from color_to_words.
+    """
+
+    def __init__(
+        self, 
+        color_to_words: Dict, 
+        default_color: str
+    ) -> Dict:
+
+        self.word_to_color = {
+            word: color
+            for (color, words) in color_to_words.items()
+            for word in words
+        }
+
+        self.default_color = default_color
+
+    def __call__(self, word, **kwargs):
+        return self.word_to_color.get(word, self.default_color)
+
+
+class SegmentedWordCloud:
+    def __init__(
+        self, 
+        freq_dic: Dict[str, int], 
+        less_group: List[str], 
+        greater_group: List[str]
+    ) -> WordCloud:
+
+        colors = {
+            'less': '#529ef3',
+            'salient':'#d35400',
+            'greater':'#5d6d7e',
+        }
+
+        color_to_words = {
+            colors['greater']: greater_group,
+            colors['less']: less_group,
+        }
+        
+
+        grouped_color_func = SimpleGroupedColorFunc(
+            color_to_words=color_to_words, 
+            default_color=colors['salient']
+        )
+
+        self.wc = WordCloud(
+            background_color="white", 
+            width=900, 
+            height=300,
+            random_state=None).generate_from_frequencies(freq_dic)
+        
+        self.wc.recolor(color_func=grouped_color_func)
+
+    def plot(
+        self, 
+        figsize: Tuple[int,int]
+    ) -> plt.Figure:
+    
+        fig, ax = plt.subplots(figsize=figsize)
+        ax.imshow(self.wc, interpolation="bilinear")
+        ax.axis("off")
+        fig.tight_layout()
+        return fig

modules/module_vocabulary.py

@@ -0,0 +1,123 @@
+import pandas as pd
+from typing import List, Dict, Tuple
+
+class Vocabulary:
+    def __init__(
+        self, 
+        subset_name: str
+    ) -> None:
+
+        # Dataset info
+        self.subset_name = subset_name
+        self.ds_path = f"data/{subset_name}_vocab_v6.zip"
+        
+        # Pandas dataset
+        self.df_vocab = None
+
+        # Minimal list with (percentile,freq) tuples to be able to plot the word distribution graph
+        self.histogram = None
+
+        # Load vocabulary dataset
+        self.__load()
+
+    def __contains__(
+        self, 
+        word: str
+    ) -> bool:
+
+        return word in self.df_vocab['word'].to_list()
+
+    def __load(
+        self
+    ) -> None:
+
+        print(f"Preparing {self.subset_name} vocabulary...")
+
+        # --- Download vocab dataset ---
+        self.df_vocab = pd.read_json(self.ds_path)
+
+        # --- Create min histogram to plot the word distribution graph ---
+        x_values = self.df_vocab['percentile'].to_list()
+        y_values = self.df_vocab['freq'].to_list()
+
+        # Delete duplicated tups
+        uniques_tups_list = set(list(zip(x_values, y_values)))
+        # Leave only tuples with different first element
+        uniques_tups_list = dict(uniques_tups_list)
+
+        self.histogram = sorted(
+            uniques_tups_list.items(),
+            key=lambda tup: tup[0], 
+            reverse=True
+        )
+        
+    def __getValue(
+        self, 
+        word: str, 
+        feature: str
+    ):
+        word_id, value = None, None
+
+        if word in self:
+            word_id = self.df_vocab['word'].to_list().index(word)
+        
+        if word_id != None:
+            value = self.df_vocab[feature].to_list()[word_id]
+
+        return value
+
+    def getFreq(
+        self, 
+        word
+    ) -> int:
+
+        return self.__getValue(word, 'freq')
+
+    def getPercentile(
+        self, 
+        word:str
+    ) -> float:
+
+        return self.__getValue(word, 'percentile')
+
+    def getSplits(
+        self, 
+        word: str
+    ) -> List[str]:
+
+        return self.__getValue(word, 'splits')
+    
+    def getSubsets(
+        self, 
+        word: str
+    ) -> Dict[str, int]:
+
+        return self.__getValue(word, 'in_subset')
+
+    def distribution(
+        self
+    ) -> Tuple:
+
+        x_values, y_values = zip(*self.histogram)
+        return x_values, y_values
+     
+    def getWordNeighbors(
+        self, 
+        word: str, 
+        n_neighbors: int=20
+    )-> Tuple:
+
+        word_id = self.df_vocab['word'].to_list().index(word)
+        words = self.df_vocab['word'].to_list()
+        freqs = self.df_vocab['freq'].to_list()
+        l_sorted = list(zip(words, freqs))
+
+        g = l_sorted[max(0, word_id-n_neighbors):word_id]    # less than
+        e = l_sorted[word_id]                               # equal than
+        l = l_sorted[word_id+1:word_id+n_neighbors]         # greter than
+
+        dic = dict(g+[e]+l)
+        l = [x[0] for x in l]
+        g = [x[0] for x in g]
+
+        return dic, l, g

modules/module_word2Context.py

@@ -0,0 +1,208 @@
+from datasets import load_dataset, interleave_datasets
+from modules.module_segmentedWordCloud import SegmentedWordCloud
+from modules.module_customSubsetsLabel import CustomSubsetsLabel
+from random import sample as random_sample
+from typing import Tuple, List, Dict
+import re
+
+import matplotlib as mpl
+mpl.use('Agg')
+import matplotlib.pyplot as plt
+
+
+class Word2Context:
+    def __init__(
+        self, 
+        context_ds_name: str,       # Context dataset HF name | path 
+        vocabulary,                 # Vocabulary class instance
+        errorManager                # ErrorManager class instance
+    ) -> None:
+
+        self.context_ds_name = context_ds_name
+        
+        # Vocabulary class 
+        self.vocab = vocabulary
+
+        # Custom Label component
+        self.Label = CustomSubsetsLabel()
+
+        self.errorManager = errorManager
+
+    def errorChecking(
+        self, 
+        word: str
+    ) -> str:
+
+        out_msj = ""
+
+        if not word:
+            out_msj = ['EMBEDDING_NO_WORD_PROVIDED']
+        else:
+            if word not in self.vocab:
+                out_msj = ['EMBEDDING_WORD_OOV', word]
+        
+        return self.errorManager.process(out_msj)
+
+    def genWebLink(
+        self,
+        text: str
+    ) -> str:
+
+        text = text.replace("\"", "'")
+        text = text.replace("<u><b>", "")
+        text = text.replace("</b></u>", "")
+        url = "https://www.google.com.tr/search?q={}".format(text)
+        return '<a href="{}" rel="noopener noreferrer" target="_blank"><center>🌐🔍</center></a>'.format(url)
+
+    def genWordCloudPlot(
+        self, 
+        word: str, 
+        figsize: Tuple[int,int]=(9,3)
+    ) -> plt.Figure:
+
+        err = self.errorChecking(word)
+        if err:
+            raise Exception(err)
+
+        freq_dic, l_group, g_group = self.vocab.getWordNeighbors(word, n_neighbors=10)
+        wc = SegmentedWordCloud(freq_dic, l_group, g_group)
+        return wc.plot(figsize)
+
+    def genDistributionPlot(
+        self, 
+        word: str, 
+        figsize: Tuple[int,int]=(6,1)
+    ) -> plt.Figure:
+
+        err = self.errorChecking(word)
+        if err:
+            raise Exception(err)
+
+        x_values, y_values = self.vocab.distribution()
+        w_percentile = self.vocab.getPercentile(word)
+        w_freq = self.vocab.getFreq(word)
+
+        fig, ax = plt.subplots(figsize=figsize)
+        ax.plot(x_values, y_values, color='green')
+        ax.fill_between(x_values, y_values, color='lightgreen',)
+        
+        ax.axvline(x=max(0,w_percentile-.01), 
+            color='blue', 
+            linewidth=7, 
+            alpha=.1,
+            linestyle='-'
+        )
+
+        ax.axvline(x=min(100,w_percentile+.01), 
+            color='black', 
+            linewidth=7, 
+            alpha=.1, 
+            linestyle='-'
+        )
+
+        ax.axvline(x=w_percentile, 
+            color='#d35400', 
+            linewidth=2, 
+            linestyle='--',
+            label=f'{w_freq}\n(frecuencia total)'
+        )
+
+        ax.axis('off')
+        plt.legend(loc='upper left', prop={'size': 7})
+        return fig
+    
+    def findSplits(
+        self, 
+        word: str, 
+        subsets_list: List[str]
+    ):
+
+        err = self.errorChecking(word)
+        if err:
+            raise Exception(err)
+
+        w_splits = self.vocab.getSplits(word)
+
+        splits_list = [] 
+        for subset in subsets_list:
+            current_split_list = []
+            for s in w_splits:
+                if (subset == s.split("_")[0]):
+                    current_split_list.append(s)
+            
+            if current_split_list:
+                splits_list.append(current_split_list)
+
+        splits_list = [random_sample(s_list, 1)[0] for s_list in splits_list]
+
+        ds_list = [ 
+            load_dataset(path=self.context_ds_name, name=split, streaming=True, split='all') 
+            for split in splits_list
+        ]
+
+        datasets = ds_list[0]
+        if len(ds_list) > 1:
+            datasets = interleave_datasets(ds_list, probabilities=None)
+
+        return datasets
+
+    def findContexts(
+        self, 
+        sample: str, 
+        word: str
+    ) -> Dict[str,str]:
+
+        sample = sample['text'].strip()
+        context = ""
+        m = re.search(r'\b{}\b'.format(word), sample)
+        if m:
+            init = m.span()[0]
+            end = init+len(word)
+            context = sample[:init]+"<u><b>"+word+"</b></u>"+sample[end:]
+        return {'context':context}
+
+    def getSubsetsInfo(
+        self, 
+        word: str
+    ) -> Tuple:
+
+        err = self.errorChecking(word)
+        if err:
+            raise Exception(err)
+
+        total_freq = self.vocab.getFreq(word)
+        subsets_name_list = list(self.vocab.getSubsets(word).keys())
+        subsets_freq_list = list(self.vocab.getSubsets(word).values())
+
+        # Create subset frequency dict to subset_freq component
+        subsets_info = {
+            s_name + f" ({s_freq})": s_freq/total_freq
+            for s_name, s_freq in zip(subsets_name_list, subsets_freq_list) 
+        }
+
+        subsets_origin_info = dict(sorted(subsets_info.items(), key=lambda x: x[1], reverse=True))
+        subsets_info = self.Label.compute(subsets_origin_info)
+        return subsets_info, subsets_origin_info
+
+    def getContexts(
+        self, 
+        word: str, 
+        n_context: int, 
+        ds
+    ) -> List[Tuple]:
+
+        err = self.errorChecking(word)
+        if err:
+            raise Exception(err)
+
+        ds_w_contexts = ds.map(lambda sample: self.findContexts(sample, word))
+        only_contexts = ds_w_contexts.filter(lambda sample: sample['context'] != "")
+        shuffle_contexts = only_contexts.shuffle(buffer_size=10)
+        
+        list_of_dict = list(shuffle_contexts.take(n_context))
+        list_of_contexts = [
+            (i, dic['context'], dic['subset']) 
+            for i,dic in enumerate(list_of_dict)
+        ]
+
+        return list_of_contexts

modules/utils.py

@@ -0,0 +1,83 @@
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import pytz
+
+
+class DateLogs:
+    def __init__(
+        self, 
+        zone: str="America/Argentina/Cordoba"
+    ) -> None:
+
+        self.time_zone = pytz.timezone(zone)
+        
+    def full(
+        self
+    ) -> str:
+
+        now = datetime.now(self.time_zone)
+        return now.strftime("%H:%M:%S %d-%m-%Y")
+    
+    def day(
+        self
+    ) -> str:
+
+        now = datetime.now(self.time_zone)
+        return now.strftime("%d-%m-%Y")
+
+def take_two_sides_extreme_sorted(
+    df: pd.DataFrame, 
+    n_extreme: int,
+    part_column: str=None,
+    head_value: str='',
+    tail_value: str=''
+) -> pd.DataFrame:
+
+    head_df = df.head(n_extreme)[:]
+    tail_df = df.tail(n_extreme)[:]
+
+    if part_column is not None:
+        head_df[part_column] = head_value
+        tail_df[part_column] = tail_value
+
+    return (pd.concat([head_df, tail_df])
+            .drop_duplicates()
+            .reset_index(drop=True))
+
+def normalize(
+    v: np.ndarray
+) -> np.ndarray:
+
+    """Normalize a 1-D vector."""
+    if v.ndim != 1:
+        raise ValueError('v should be 1-D, {}-D was given'.format(
+            v.ndim))
+    norm = np.linalg.norm(v)
+    if norm == 0:
+        return v
+    return v / norm
+
+def project_params(
+    u: np.ndarray, 
+    v: np.ndarray
+) -> np.ndarray:
+
+    """Projecting and rejecting the vector v onto direction u with scalar."""
+    normalize_u = normalize(u)
+    projection = (v @ normalize_u)
+    projected_vector = projection * normalize_u
+    rejected_vector = v - projected_vector
+    return projection, projected_vector, rejected_vector
+
+
+def cosine_similarity(
+    v: np.ndarray, 
+    u: np.ndarray
+) -> np.ndarray:
+
+    """Calculate the cosine similarity between two vectors."""
+    v_norm = np.linalg.norm(v)
+    u_norm = np.linalg.norm(u)
+    similarity = v @ u / (v_norm * u_norm)
+    return similarity

requirements.txt

@@ -0,0 +1,14 @@
+regex==2022.10.31
+torch==1.13.1
+scikit-learn==0.24.2
+transformers==4.25.1
+wordcloud==1.8.2.2
+matplotlib
+numpy
+uuid
+python-dotenv
+memory_profiler
+gensim==4.2.0
+seaborn
+annoy==1.17.1
+datasets==2.8.0

tool.cfg

@@ -0,0 +1,25 @@
+[INTERFACE]
+# [es | en]
+language            = en
+
+[WORD_EXPLORER]
+# [data/100k_es_embedding.vec | data/100k_en_embedding.vec ]
+embeddings_path     = data/100k_en_embedding.vec
+# [sklearn | ann]
+nn_method           = sklearn   
+max_neighbors       = 20
+
+[DATA]
+contexts_dataset    = vialibre/splittedspanish3bwc
+# [full | mini]
+vocabulary_subset   = full
+# [True | False]
+available_wordcloud = False
+
+[LMODEL]
+# [bert-base-uncased | dccuchile/bert-base-spanish-wwm-uncased]
+language_model      = bert-base-uncased
+
+[LOGS]
+# [True | False]
+available_logs      = False

tool_info.py

@@ -0,0 +1,23 @@
+TOOL_INFO = """
+> ### A tool to overcome technical barriers for bias assessment in human language technologies
+
+* [Read Full Paper](https://arxiv.org/abs/2207.06591)
+
+> ### Licensing Information 
+* [MIT Licence](https://github.com/fvialibre/edia/blob/master/LICENSE)
+
+> ### Citation Information
+```c
+@misc{https://doi.org/10.48550/arxiv.2207.06591,
+    doi = {10.48550/ARXIV.2207.06591},
+    url = {https://arxiv.org/abs/2207.06591},
+    author = {Alemany, Laura Alonso and Benotti, Luciana and González, Lucía and Maina, Hernán and Busaniche, Beatriz and Halvorsen, Alexia and Bordone, Matías and Sánchez, Jorge},
+    keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), 
+    FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {A tool to overcome technical barriers for bias assessment in human language technologies},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
+}
+```
+"""