Data Enrichment
Usage Examples of Hugging Face Models
Use Cases
- Overview
- Automated Fine-Tuning
- AI Agents
- AI-Powered Data Retrieval
- Data Enrichment
- Predictive Analytics
- In-Database Machine Learning
- AI Workflow Automation
Data Enrichment
Usage Examples of Hugging Face Models
This document presents various use cases of Hugging Face models from MindsDB.
Spam Classifier
Here is an example of a binary classification. The model determines whether a text string is spam or not.
CREATE MODEL mindsdb.spam_classifier
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'mrm8488/bert-tiny-finetuned-sms-spam-detection',
input_column = 'text_spammy',
labels = ['ham', 'spam'];
Before querying for predictions, we should verify the status of the spam_classifier model.
DESCRIBE spam_classifier;
On execution, we get:
+---------------+-------+--------+--------+-------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT|UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+---------------+-------+--------+--------+-------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|spam_classifier|mindsdb|complete|[NULL] |PRED |up_to_date |22.10.2.1 |[NULL]|[NULL] |{'target': 'PRED', 'using': {'engine': 'huggingface', 'task': 'text-classification', 'model_name': 'mrm8488/bert-tiny-finetuned-sms-spam-detection', 'input_column': 'text_spammy', 'labels': ['ham', 'spam']}}|
+---------------+-------+--------+--------+-------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text_spammy AS input_text
FROM example_db.demo_data.hf_test AS t
JOIN mindsdb.spam_classifier AS h;
On execution, we get:
+----+---------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+
|PRED|PRED_explain |input_text |
+----+---------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+
|spam|{'spam': 0.9051626920700073, 'ham': 0.09483727067708969} |Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's |
|ham |{'ham': 0.9380123615264893, 'spam': 0.061987683176994324}|Nah I don't think he goes to usf, he lives around here though |
|spam|{'spam': 0.9064534902572632, 'ham': 0.09354648739099503} |WINNER!! As a valued network customer you have been selected to receive a £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only. |
+----+---------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+
Sentiment Classifier
Here is an example of a multi-value classification. The model determines the sentiment of a text string, where possible values are negative, neutral, and positive.
CREATE MODEL mindsdb.sentiment_classifier
PREDICT sentiment
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'cardiffnlp/twitter-roberta-base-sentiment',
input_column = 'text_short',
labels = ['negative', 'neutral', 'positive'];
Before querying for predictions, we should verify the status of the sentiment_classifier model.
DESCRIBE sentiment_classifier;
On execution, we get:
+--------------------+-------+--------+--------+---------+-------------+---------------+------+-----------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+--------------------+-------+--------+--------+---------+-------------+---------------+------+-----------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|sentiment_classifier|mindsdb|complete|[NULL] |sentiment|up_to_date |22.10.2.1 |[NULL]|[NULL] |{'target': 'sentiment', 'using': {'engine': 'huggingface', 'task': 'text-classification', 'model_name': 'cardiffnlp/twitter-roberta-base-sentiment', 'input_column': 'text_short', 'labels': ['negative', 'neutral', 'positive']}}|
+--------------------+-------+--------+--------+---------+-------------+---------------+------+-----------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text_short AS input_text
FROM example_db.demo_data.hf_test AS t
JOIN mindsdb.sentiment_classifier AS h;
On execution, we get:
+---------+----------------------------------------------------------------------------------------------------+-------------------+
|sentiment|sentiment_explain |input_text |
+---------+----------------------------------------------------------------------------------------------------+-------------------+
|negative |{'negative': 0.9679920077323914, 'neutral': 0.02736542373895645, 'positive': 0.0046426113694906235} |I hate tacos |
|positive |{'positive': 0.7607280015945435, 'neutral': 0.2332666665315628, 'negative': 0.006005281116813421} |I want to dance |
|positive |{'positive': 0.9835041761398315, 'neutral': 0.014900505542755127, 'negative': 0.0015953202964738011}|Baking is the best |
+---------+----------------------------------------------------------------------------------------------------+-------------------+
Zero-Shot Classifier
Here is an example of a zero-shot classification. The model determines to which of the defined categories a text string belongs.
CREATE MODEL mindsdb.zero_shot_tcd
PREDICT topic
USING
engine = 'huggingface',
task = 'zero-shot-classification',
model_name = 'facebook/bart-large-mnli',
input_column = 'text_short',
candidate_labels = ['travel', 'cooking', 'dancing'];
Before querying for predictions, we should verify the status of the zero_shot_tcd model.
DESCRIBE zero_shot_tcd;
On execution, we get:
+-------------+-------+--------+--------+--------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+-------------+-------+--------+--------+--------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|zero_shot_tcd|mindsdb|complete|[NULL] |topic |up_to_date |22.10.2.1 |[NULL]|[NULL] |{'target': 'topic', 'using': {'engine': 'huggingface', 'task': 'zero-shot-classification', 'model_name': 'facebook/bart-large-mnli', 'input_column': 'text_short', 'candidate_labels': ['travel', 'cooking', 'dancing']}}|
+-------------+-------+--------+--------+--------+-------------+---------------+------+-----------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text_short AS input_text
FROM example_db.demo_data.hf_test AS t
JOIN mindsdb.zero_shot_tcd AS h;
On execution, we get:
+-------+--------------------------------------------------------------------------------------------------+-------------------+
|topic |topic_explain |input_text |
+-------+--------------------------------------------------------------------------------------------------+-------------------+
|cooking|{'cooking': 0.7530364990234375, 'travel': 0.1607145369052887, 'dancing': 0.08624900877475739} |I hate tacos |
|dancing|{'dancing': 0.9746809601783752, 'travel': 0.015539299696683884, 'cooking': 0.009779711253941059} |I want to dance |
|cooking|{'cooking': 0.9936348795890808, 'travel': 0.0034196735359728336, 'dancing': 0.0029454431496560574}|Baking is the best |
+-------+--------------------------------------------------------------------------------------------------+-------------------+
Translation
Here is an example of a translation. The model gets an input string in English and translates it into French.
CREATE MODEL mindsdb.translator_en_fr
PREDICT translated
USING
engine = 'huggingface',
task = 'translation',
model_name = 't5-base',
input_column = 'text_short',
lang_input = 'en',
lang_output = 'fr';
Before querying for predictions, we should verify the status of the translator_en_fr model.
DESCRIBE translator_en_fr;
On execution, we get:
+----------------+-------+--------+--------+----------+-------------+---------------+------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+----------------+-------+--------+--------+----------+-------------+---------------+------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|translator_en_fr|mindsdb|complete|[NULL] |translated|up_to_date |22.10.2.1 |[NULL]|[NULL] |{'target': 'translated', 'using': {'engine': 'huggingface', 'task': 'translation', 'model_name': 't5-base', 'input_column': 'text_short', 'lang_input': 'en', 'lang_output': 'fr'}}|
+----------------+-------+--------+--------+----------+-------------+---------------+------+-----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text_short AS input_text
FROM example_db.demo_data.hf_test AS t
JOIN mindsdb.translator_en_fr AS h;
On execution, we get:
+-------------------------------+-------------------+
|translated |input_text |
+-------------------------------+-------------------+
|Je déteste les tacos |I hate tacos |
|Je veux danser |I want to dance |
|La boulangerie est la meilleure|Baking is the best |
+-------------------------------+-------------------+
Summarization
Here is an example of input text summarization.
CREATE MODEL mindsdb.summarizer_10_20
PREDICT text_summary
USING
engine = 'huggingface',
task = 'summarization',
model_name = 'sshleifer/distilbart-cnn-12-6',
input_column = 'text_long',
min_output_length = 10,
max_output_length = 20;
Before querying for predictions, we should verify the status of the summarizer_10_20 model.
DESCRIBE summarizer_10_20;
On execution, we get:
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|summarizer_10_20|mindsdb|complete|[NULL] |text_summary|up_to_date |22.10.2.1 |[NULL]|[NULL] |{'target': 'text_summary', 'using': {'engine': 'huggingface', 'task': 'summarization', 'model_name': 'sshleifer/distilbart-cnn-12-6', 'input_column': 'text_long', 'min_output_length': 10, 'max_output_length': 20}}|
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text_long AS input_text
FROM example_db.demo_data.hf_test AS t
JOIN mindsdb.summarizer_10_20 AS h;
On execution, we get:
+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|text_summary |input_text |
+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|A taco is a traditional Mexican food consisting of a small hand-sized corn- or |A taco is a traditional Mexican food consisting of a small hand-sized corn- or wheat-based tortilla topped with a filling. The tortilla is then folded around the filling and eaten by hand. A taco can be made with a variety of fillings, including beef, pork, chicken, seafood, beans, vegetables, and cheese, allowing for great versatility and variety. |
|Dance is a performing art form consisting of sequences of movement, either improvised or purposefully selected|Dance is a performing art form consisting of sequences of movement, either improvised or purposefully selected. This movement has aesthetic and often symbolic value.[nb 1] Dance can be categorized and described by its choreography, by its repertoire of movements, or by its historical period or place of origin. |
|Baking is a method of preparing food that uses dry heat, typically in an oven |Baking is a method of preparing food that uses dry heat, typically in an oven, but can also be done in hot ashes, or on hot stones. The most common baked item is bread but many other types of foods can be baked. Heat is gradually transferred from the surface of cakes, cookies, and pieces of bread to their center. As heat travels through, it transforms batters and doughs into baked goods and more with a firm dry crust and a softer center. Baking can be combined with grilling to produce a hybrid barbecue variant by using both methods simultaneously, or one after the other. Baking is related to barbecuing because the concept of the masonry oven is similar to that of a smoke pit.|
+--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Fill Mask
Here is an example of a masked language modeling task.
CREATE MODEL mindsdb.fill_mask
PREDICT text_filled
USING
engine = 'huggingface',
task = 'fill-mask',
model_name = 'bert-base-uncased',
input_column = 'text';
Before querying for predictions, we should verify the status of the fill_mask model.
DESCRIBE fill_mask;
On execution, we get:
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+--------------------------------------------------------------------------------------------------------------------+
|NAME |PROJECT|STATUS |ACCURACY|PREDICT |UPDATE_STATUS|MINDSDB_VERSION|ERROR |SELECT_DATA_QUERY|TRAINING_OPTIONS |
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+--------------------------------------------------------------------------------------------------------------------+
|fill_mask |mindsdb|complete|[NULL] |text_filled |up_to_date |23.3.5.0 |[NULL]|[NULL] |{'target': 'text_filled', 'using': {'task': 'fill-mask', 'model_name': 'bert-base-uncased', 'input_column': 'text'}}|
+----------------+-------+--------+--------+------------+-------------+---------------+------+-----------------+--------------------------------------------------------------------------------------------------------------------+
Once the status is complete, we can query for predictions.
SELECT h.*, t.text AS input_text
FROM demo.texts AS t
JOIN mindsdb.fill_mask AS h;
On execution, we get:
+-------------------------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|text_filled |input_text |text_filled_explain |
+-------------------------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|the food was great! |The [MASK] was great! |{'the food was great!': 0.16309359669685364, 'the party was great!': 0.06305009871721268, 'the fun was great!': 0.04633583873510361, 'the show was great!': 0.043319422751665115, 'the music was great!': 0.02990395948290825} |
|the weather is good today|The weather is [MASK] today|{'the weather is good today': 0.22563229501247406, 'the weather is warm today': 0.07954009622335434, 'the weather is fine today': 0.047255873680114746, 'the weather is better today': 0.034303560853004456, 'the weather is mild today': 0.03092862293124199}|
+-------------------------+---------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Hugging Face + MindsDB Models Library
Text Classification
Spam
Let’s create a model.
CREATE MODEL mindsdb.hf_spam
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'mariagrandury/roberta-base-finetuned-sms-spam-detection',
input_column = 'text',
labels = ['spam', 'ham'];
And check its status.
DESCRIBE hf_spam;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_spam
WHERE text = 'I like you. I love you.';
On execution, we get:
+----+--------------------------------------------------------+-----------------------+
|PRED|PRED_explain |text |
+----+--------------------------------------------------------+-----------------------+
|spam|{"ham":0.00020051795581821352,"spam":0.9997995495796204}|I like you. I love you.|
+----+--------------------------------------------------------+-----------------------+
Sentiment
Let’s create a model.
CREATE MODEL mindsdb.hf_sentiment
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'cardiffnlp/twitter-roberta-base-sentiment',
input_column = 'text',
labels = ['neg', 'neu', 'pos'];
And check its status.
DESCRIBE hf_sentiment;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_sentiment
WHERE text = 'I like you. I love you.';
On execution, we get:
+----+--------------------------------------------------------------------------------+-----------------------+
|PRED|PRED_explain |text |
+----+--------------------------------------------------------------------------------+-----------------------+
|pos |{"neg":0.003046575468033552,"neu":0.021965451538562775,"pos":0.9749879240989685}|I like you. I love you.|
+----+--------------------------------------------------------------------------------+-----------------------+
Sentiment (Finance)
Let’s create a model.
CREATE MODEL mindsdb.hf_sentiment_finance
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'ProsusAI/finbert',
input_column = 'text';
And check its status.
DESCRIBE hf_sentiment_finance;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_sentiment_finance
WHERE text = 'Stocks rallied and the British pound gained.';
On execution, we get:
+--------+-------------------------------------------------------------------------------------------+--------------------------------------------+
|PRED |PRED_explain |text |
+--------+-------------------------------------------------------------------------------------------+--------------------------------------------+
|positive|{"negative":0.0344734713435173,"neutral":0.06716493517160416,"positive":0.8983616232872009}|Stocks rallied and the British pound gained.|
+--------+-------------------------------------------------------------------------------------------+--------------------------------------------+
Emotions (6)
Let’s create a model.
CREATE MODEL mindsdb.hf_emotions_6
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'j-hartmann/emotion-english-distilroberta-base',
input_column = 'text';
And check its status.
DESCRIBE hf_emotions_6;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_emotions_6
WHERE text = 'Oh Happy Day';
On execution, we get:
+----+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
|PRED|PRED_explain |text |
+----+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
|joy |{"anger":0.0028446922078728676,"disgust":0.0009613594156689942,"fear":0.0007112706662155688,"joy":0.7692911624908447,"neutral":0.037753619253635406,"sadness":0.015293814241886139,"surprise":0.17314413189888}|Oh Happy Day|
+----+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
Toxicity
Let’s create a model.
CREATE MODEL mindsdb.hf_toxicity
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'SkolkovoInstitute/roberta_toxicity_classifier',
input_column = 'text';
And check its status.
DESCRIBE hf_toxicity;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_toxicity
WHERE text = 'I like you. I love you.';
On execution, we get:
+-------+-------------------------------------------------------------+-----------------------+
|PRED |PRED_explain |text |
+-------+-------------------------------------------------------------+-----------------------+
|neutral|{"neutral":0.9999547004699707,"toxic":0.00004535282641882077}|I like you. I love you.|
+-------+-------------------------------------------------------------+-----------------------+
ESG (6)
Let’s create a model.
CREATE MODEL mindsdb.hf_esg_6
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'yiyanghkust/finbert-esg',
input_column = 'text';
And check its status.
DESCRIBE hf_esg_6;
Once the status is complete, we can query for predictions.
SELECT * FROM mindsdb.hf_esg_6
WHERE text = 'Rhonda has been volunteering for several years for a variety of charitable community programs.';
On execution, we get:
+------+---------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
|PRED |PRED_explain |text |
+------+---------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
|Social|{"Environmental":0.0034267122391611338,"Governance":0.004729956854134798,"None":0.001239194767549634,"Social":0.9906041026115417}|Rhonda has been volunteering for several years for a variety of charitable community programs.|
+------+---------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------+
ESG (26)
Let’s create a model.
CREATE MODEL mindsdb.hf_esg_26
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'yiyanghkust/finbert-esg',
input_column = 'text';
And check its status.
DESCRIBE hf_esg_26;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_esg_26
WHERE text = 'We believe it is essential to establish validated conflict-free sources of 3TG within the Democratic Republic of the Congo (the “DRC”) and adjoining countries (together, with the DRC, the “Covered Countries”), so that these minerals can be procured in a way that contributes to economic growth and development in the region. To aid in this effort, we have established a conflict minerals policy and an internal team to implement the policy.';
On execution, we get:
+------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|PRED |PRED_explain |text |
+------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|Social|{"Environmental":0.2031959593296051,"Governance":0.08251894265413284,"None":0.050893042236566544,"Social":0.6633920073509216}|We believe it is essential to establish validated conflict-free sources of 3TG within the Democratic Republic of the Congo (the “DRC”) and adjoining countries (together, with the DRC, the “Covered Countries”), so that these minerals can be procured in a way that contributes to economic growth and development in the region. To aid in this effort, we have established a conflict minerals policy and an internal team to implement the policy.|
+------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Hate Speech
Let’s create a model.
CREATE MODEL mindsdb.hf_hate
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'Hate-speech-CNERG/bert-base-uncased-hatexplain',
input_column = 'text';
And check its status.
DESCRIBE hf_hate;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_hate
WHERE text = 'I like you. I love you.';
On execution, we get:
+------+-----------------------------------------------------------------------------------------------+-----------------------+
|PRED |PRED_explain |text |
+------+-----------------------------------------------------------------------------------------------+-----------------------+
|normal|{"hate speech":0.03551718592643738,"normal":0.7747423648834229,"offensive":0.18974047899246216}|I like you. I love you.|
+------+-----------------------------------------------------------------------------------------------+-----------------------+
Crypto Buy Signals
Let’s create a model.
CREATE MODEL mindsdb.hf_crypto
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'ElKulako/cryptobert',
input_column = 'text';
And check its status.
DESCRIBE hf_crypto;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_crypto
WHERE text = 'BTC is killing it right now';
On execution, we get:
+-------+------------------------------------------------------------------------------------------+---------------------------+
|PRED |PRED_explain |text |
+-------+------------------------------------------------------------------------------------------+---------------------------+
|Bullish|{"Bearish":0.0002816587220877409,"Bullish":0.559426486492157,"Neutral":0.4402918517589569}|BTC is killing it right now|
+-------+------------------------------------------------------------------------------------------+---------------------------+
US Political Party
Let’s create a model.
CREATE MODEL mindsdb.hf_us_party
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'm-newhauser/distilbert-political-tweets',
input_column = 'text';
And check its status.
DESCRIBE hf_us_party;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_us_party
WHERE text = 'This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs in the world, yet not enough nurses or doctors. Many millions are uninsured, while insurance company profits soar. The struggle continues. Healthcare is a human right. Medicare for all.';
On execution, we get:
+--------+-------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|PRED |PRED_explain |text |
+--------+-------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|Democrat|{"Democrat":0.9999973773956299,"Republican":0.00000261212517216336}|This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs in the world, yet not enough nurses or doctors. Many millions are uninsured, while insurance company profits soar. The struggle continues. Healthcare is a human right. Medicare for all.|
+--------+-------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Question Detection
Let’s create a model.
CREATE MODEL mindsdb.hf_question
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'shahrukhx01/bert-mini-finetune-question-detection',
input_column = 'text',
labels = ['question', 'query'];
And check its status.
DESCRIBE hf_question;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_question
WHERE text = 'Where can I buy electronics in London';
On execution, we get:
+-----+--------------------------------------------------------------+-------------------------------------+
|PRED |PRED_explain |text |
+-----+--------------------------------------------------------------+-------------------------------------+
|query|{"query":0.9997773766517639,"question":0.00022261829872149974}|Where can I buy electronics in London|
+-----+--------------------------------------------------------------+-------------------------------------+
Industry
Let’s create a model.
CREATE MODEL mindsdb.hf_industry
PREDICT PRED
USING
engine = 'huggingface',
task = 'text-classification',
model_name = 'sampathkethineedi/industry-classification',
input_column = 'text';
And check its status.
DESCRIBE hf_industry;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_industry
WHERE text = 'Low latency is one of our best cloud features';
On execution, we get:
+----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------+
|PRED |PRED_explain |text |
+----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------+
|Systems Software|{"Advertising":0.000006795735771447653,"Aerospace & Defense":0.00001537964453746099,"Apparel Retail":5.350161131900677e-7,"Apparel, Accessories & Luxury Goods":0.000002604161181807285,"Application Software":0.009111878462135792,"Asset Management & Custody Banks":0.00003155150625389069,"Auto Parts & Equipment":0.000015504940165556036,"Biotechnology":6.533917940032552e-8,"Building Products":7.348538133555849e-8,"Casinos & Gaming":0.000013775999832432717,"Commodity Chemicals":0.0000010432338513055583,"Communications Equipment":0.000019887389498762786,"Construction & Engineering":0.000001826199536480999,"Construction Machinery & Heavy Trucks":0.000009827364920056425,"Consumer Finance":0.0000018292046206624946,"Data Processing & Outsourced Services":0.0000010666744856280275,"Diversified Metals & Mining":0.000006960767223063158,"Diversified Support Services":0.000016824227714096196,"Electric Utilities":0.000003896044290740974,"Electrical Components & Equipment":0.000001626394464437908,"Electronic Equipment & Instruments":0.00003863943129545078,"Environmental & Facilities Services":0.000736175337806344,"Gold":0.00002220332135038916,"Health Care Equipment":4.6927588925882446e-8,"Health Care Facilities":7.432880124724761e-7,"Health Care Services":6.929263918209472e-7,"Health Care Supplies":2.1007431882935634e-7,"Health Care Technology":0.000003907185146090342,"Homebuilding":3.903339234057057e-7,"Hotels, Resorts & Cruise Lines":6.0527639789143e-7,"Human Resource & Employment Services":5.48697983049351e-7,"IT Consulting & Other Services":0.0000723653138265945,"Industrial Machinery":7.230253231682582e-7,"Integrated Telecommunication Services":2.8266379104024963e-7,"Interactive Media & Services":0.00003454017496551387,"Internet & Direct Marketing Retail":0.000003871373337460682,"Internet Services & Infrastructure":0.0007196652004495263,"Investment Banking & Brokerage":0.0000040634336073708255,"Leisure Products":0.000002158361439796863,"Life Sciences Tools & Services":0.000002861268058040878,"Movies & Entertainment":0.000007286199888767442,"Oil & Gas Equipment & Services":0.000004376991455501411,"Oil & Gas Exploration & Production":0.000005569149834627751,"Oil & Gas Refining & Marketing":0.000012647416951949708,"Oil & Gas Storage & Transportation":0.000005852583853993565,"Packaged Foods & Meats":0.0000011130315442642313,"Personal Products":0.00000970239307207521,"Pharmaceuticals":0.0000037546726616710657,"Property & Casualty Insurance":0.000006116194072092185,"Real Estate Operating Companies":0.00001882187461887952,"Regional Banks":0.0000011669454806906288,"Research & Consulting Services":0.000024276219846797176,"Restaurants":8.598511840318679e-7,"Semiconductors":0.0000021006283077440457,"Specialty Chemicals":0.000004160017397225602,"Specialty Stores":2.644004553076229e-7,"Steel":0.0000013566890402216814,"Systems Software":0.9889177083969116,"Technology Distributors":0.00001339179198112106,"Technology Hardware, Storage & Peripherals":0.00004790363891515881,"Thrifts & Mortgage Finance":3.924862141957419e-7,"Trading Companies & Distributors":0.0000035233156268077437}|Low latency is one of our best cloud features|
+----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------+
Zero-Shot Classification
Bart
Let’s create a model.
CREATE MODEL mindsdb.hf_zs_bart
PREDICT PRED
USING
engine = 'huggingface',
task = 'zero-shot-classification',
model_name = 'facebook/bart-large-mnli',
input_column = 'text',
candidate_labels = ['Books', 'Household', 'Clothing & Accessories', 'Electronics'];
And check its status.
DESCRIBE hf_zs_bart;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_zs_bart
WHERE text = 'Paper Plane Design Framed Wall Hanging Motivational Office Decor Art Prints';
On execution, we get:
+---------+------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+
|PRED |PRED_explain |text |
+---------+------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+
|Household|{"Books":0.1876104772090912,"Clothing & Accessories":0.08688066899776459,"Electronics":0.14785148203372955,"Household":0.5776574015617371}|Paper Plane Design Framed Wall Hanging Motivational Office Decor Art Prints|
+---------+------------------------------------------------------------------------------------------------------------------------------------------+---------------------------------------------------------------------------+
Translation
English to French (T5)
Let’s create a model.
CREATE MODEL mindsdb.hf_t5_en_fr
PREDICT PRED
USING
engine = 'huggingface',
task = 'translation',
model_name = 't5-base',
input_column = 'text',
lang_input = 'en',
lang_output = 'fr';
And check its status.
DESCRIBE hf_t5_en_fr;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_t5_en_fr
WHERE text = 'The monkey is on the branch';
On execution, we get:
+---------------------------+---------------------------+
|PRED |text |
+---------------------------+---------------------------+
|Le singe est sur la branche|The monkey is on the branch|
+---------------------------+---------------------------+
Summarization
Bart
Let’s create a model.
CREATE MODEL mindsdb.hf_bart_sum_20
PREDICT PRED
USING
engine = 'huggingface',
task = 'summarization',
model_name = 'sshleifer/distilbart-cnn-12-6',
input_column = 'text',
min_output_length = 5,
max_output_length = 20;
And check its status.
DESCRIBE hf_bart_sum_20;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_bart_sum_20
WHERE text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.';
On execution, we get:
+-------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|PRED |text |
+-------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|The tower is 324 metres (1,063 ft) tall, about the same|The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.|
+-------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Google Pegasus
Let’s create a model.
CREATE MODEL mindsdb.hf_peg_sum_20
PREDICT PRED
USING
engine = 'huggingface',
task = 'summarization',
model_name = 'google/pegasus-xsum',
input_column = 'text',
min_output_length = 5,
max_output_length = 20;
And check its status.
DESCRIBE hf_peg_sum_20;
Once the status is complete, we can query for predictions.
SELECT *
FROM mindsdb.hf_peg_sum_20
WHERE text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.';
On execution, we get:
+------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|PRED |text |
+------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|The Eiffel Tower is a landmark in Paris, France.|The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.|
+------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
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On this page
- Spam Classifier
- Sentiment Classifier
- Zero-Shot Classifier
- Translation
- Summarization
- Fill Mask
- Hugging Face + MindsDB Models Library
- Text Classification
- Spam
- Sentiment
- Sentiment (Finance)
- Emotions (6)
- Toxicity
- ESG (6)
- ESG (26)
- Hate Speech
- Crypto Buy Signals
- US Political Party
- Question Detection
- Industry
- Zero-Shot Classification
- Bart
- Translation
- English to French (T5)
- Summarization
- Bart
- Google Pegasus