datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HR_Attritionseveral-file-3e055f
several-file-3e055f
Synthetic products test data: 30 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/bartoncolton/several-file-3e055f.Net-barter-terms-of-trade-index-2015-100
Net barter terms of trade index 2015 100 | Africa (World Bank)
Size category: n<1K - Formats: csv - Sector: economics_finance - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public datasets help analysts inspect… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Net-barter-terms-of-trade-index-2015-100.QMSum_BART_formatwiki_qa_bart_1000rowreal_estate_listing_analysis_bart_dataset
Real Estate Listing Analysis
Description: Perform various tasks to analyze real estate listings, including categorizing them by type, determining if they are for new buildings or business centers, identifying amenities, classifying listings based on location, and analyzing pricing trends.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name =… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/real_estate_listing_analysis_bart_dataset.wiki_qa_bart_10000rowcustomer_feedback_analysis_-_company_x_bart_dataset
Customer Feedback Analysis - Company X
Description: Classify customer feedback based on sentiment, topic, and urgency. Prioritize and address customer concerns, improve products and services, and enhance customer satisfaction.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/customer_feedback_analysis_-_company_x_bart"… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/customer_feedback_analysis_-_company_x_bart_dataset.SQuALTIY_gt_BART_formatbartenderasiancuisinefooddelivery_onlineorderclassification_bart_dataset
AsianCuisineFoodDelivery_OnlineOrderClassification
Description: Categorize online orders based on dish types, ingredients, or cooking methods to optimize food preparation processes, inventory management, and delivery logistics.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name =… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/asiancuisinefooddelivery_onlineorderclassification_bart_dataset.bank_melli_iran_customer_service_chatbot_bart_dataset
Bank Melli Iran Customer Service Chatbot
Description: Classify customer inquiries into predefined categories and provide automated responses, improving customer service and reducing response time
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/bank_melli_iran_customer_service_chatbot_bart"
model =… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/bank_melli_iran_customer_service_chatbot_bart_dataset.json-output-bart-beercompany_sentiment_analysis_bart_dataset
Company_Sentiment_Analysis
Description: Analyze customer opinions, feedback, and reviews about the company software, websites, and IT services to gain insights and improve products and services
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/company_sentiment_analysis_bart"
model =… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/company_sentiment_analysis_bart_dataset.student_progress_tracking_bart_dataset
Student Progress Tracking
Description: Classify student assessment results to monitor their progress and identify areas that require improvement.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/student_progress_tracking_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer =… See the full description on the dataset page: https://huggingface.co/datasets/interneuronai/student_progress_tracking_bart_dataset.MVRLT
MVRLT
bart-train-v1Bart_model