jsaizant/bsc-edu-annotator

Model Card for BSC-EDU Regression Classifier

A multilingual text-embedding regression model for assigning a continuous educational-value score to web documents. The model is designed for quality-based data filtering, producing scores in the range [0, 4].

Model Details

Model Description

The BSC-EDU classifier is a lightweight multilingual regression model based on a text-embedding encoder. It is fine-tuned to predict a continuous educational-value score for text documents. The resulting score can be used to rank or filter documents according to their predicted educational value.

The model was trained using examples in Spanish, Catalan, and Basque, with 500,000 examples per language. The first 512 tokens from each example were used. The model's predictions are continuous rather than restricted to discrete classes, allowing flexible score thresholds for downstream data filtering.

• Developed by: Barcelona Supercomputing Center (BSC)
• Funded by: OpenEuroLLM consortium (Grant Agreement No 101195233) and Ministerio para la Transformación Digital y de la Función Pública (ALIA)
• Shared by: OpenEuroLLM consortium
• Model type: Multilingual text-embedding regression model / text quality classifier
• Language(s) (NLP): 74 languages supported by the base model.
• License: Apache 2.0
• Finetuned from model: snowflake-arctic-embed-l-v2.0 multilingual text-embedding model

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]