Instructions to use jsaizant/bsc-edu-annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsaizant/bsc-edu-annotator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jsaizant/bsc-edu-annotator")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jsaizant/bsc-edu-annotator") model = AutoModel.from_pretrained("jsaizant/bsc-edu-annotator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jsaizant/bsc-edu-annotator: direct link, hf CLI and curl.
- Browser
- Download file 6.06 kB
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https://huggingface.co/jsaizant/bsc-edu-annotator/resolve/main/README.md
- Command line
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hf download hf://jsaizant/bsc-edu-annotator/README.md
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curl -L -o README.md https://huggingface.co/jsaizant/bsc-edu-annotator/resolve/main/README.md
6.06 kB
| license: apache-2.0 | |
| base_model: | |
| - Snowflake/snowflake-arctic-embed-l-v2.0 | |
| library_name: transformers | |
| tags: [] | |
| # Model Card for BSC-EDU Regression Classifier | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| 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 | |
| <!-- Provide a longer summary of what this model is. --> | |
| 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] | |
| <!-- Provide the basic links for the model. --> | |
| - **Repository:** [More Information Needed] | |
| - **Paper [optional]:** [More Information Needed] | |
| - **Demo [optional]:** [More Information Needed] | |
| ## Uses | |
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> | |
| ### Direct Use | |
| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> | |
| [More Information Needed] | |
| ### Downstream Use [optional] | |
| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> | |
| [More Information Needed] | |
| ### Out-of-Scope Use | |
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> | |
| [More Information Needed] | |
| ## Bias, Risks, and Limitations | |
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> | |
| [More Information Needed] | |
| ### Recommendations | |
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> | |
| 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 | |
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| [More Information Needed] | |
| ### Training Procedure | |
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> | |
| #### Preprocessing [optional] | |
| [More Information Needed] | |
| #### Training Hyperparameters | |
| - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> | |
| #### Speeds, Sizes, Times [optional] | |
| <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> | |
| [More Information Needed] | |
| ## Evaluation | |
| <!-- This section describes the evaluation protocols and provides the results. --> | |
| ### Testing Data, Factors & Metrics | |
| #### Testing Data | |
| <!-- This should link to a Dataset Card if possible. --> | |
| [More Information Needed] | |
| #### Factors | |
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> | |
| [More Information Needed] | |
| #### Metrics | |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> | |
| [More Information Needed] | |
| ### Results | |
| [More Information Needed] | |
| #### Summary | |
| ## Model Examination [optional] | |
| <!-- Relevant interpretability work for the model goes here --> | |
| [More Information Needed] | |
| ## Environmental Impact | |
| <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> | |
| <!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). --> | |
| - **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] | |
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> | |
| **BibTeX:** | |
| [More Information Needed] | |
| **APA:** | |
| [More Information Needed] | |
| ## Glossary [optional] | |
| <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> | |
| [More Information Needed] | |
| ## More Information [optional] | |
| [More Information Needed] | |
| ## Model Card Authors [optional] | |
| [More Information Needed] | |
| ## Model Card Contact | |
| [More Information Needed] |