Update README.md

README.md

@@ -1,273 +1,275 @@
----
-license: apache-2.0
-language:
-# slavic
-- bg
-- cs
-- hr
-- mk
-- pl
-- sk
-- sl
-- sr
-- uk
-
-# germanic
-- da
-- de
-- is
-- nl
-- no
-- sv
-
-# romance
-- ca
-- es
-- fr
-- gl
-- it
-- pt
-- ro
-
-# uralic
-- et
-- fi
-- hu
-
-# baltic
-- lt
-- lv
-
-# singleton
-- el
-- ga
-- eu
-- mt
-- tr
-- sq
-- hy
-
-
-base_model:
-- Snowflake/snowflake-arctic-embed-m-v2.0
-pipeline_tag: text-ranking
----
-## Summary
-
-A regression head that scores texts based on their educational value.
-
-<!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). -->
-
-## Model Details
-
-### Model Description
-
-This model is a regression head built on top of the `Snowflake/snowflake-arctic-embed-m-v2.0` embedding model. It assigns a score to text documents, representing their educational value on a scale from 0 (lowest) to 5 (highest). Since the underlying embedding model provides language-aligned embeddings, the regression head can be used for multiple languages.
-
-We provide checkpoints for three different training sets. These training sets were generated by letting a large language model (LLM) annotate 500k text documents. The following LLMs were used for annotation: Llama3.3-70B-it, Gemma-3-27B-it, and Mistral Small 3.1-24B-it. For each LLM, we also created training sets with balanced and unbalanced distributions of educational value scores. Checkpoints trained on balanced datasets are denoted with "balanced"; otherwise, they are denoted as "unbalanced".
-
-
-
-
-- **Developed by:** A collaboration between HessianAI, DFKI, Fraunhofer IAIS, Lamarr Institute, and TU Darmstadt.
-<!-- - **Funded by [optional]:** [More Information Needed] -->
-<!-- - **Shared by [optional]:** [More Information Needed] -->
-- **Model type:** Regression Head
-- **Language(s) (NLP):** Bulgarian, Czech, Croatian, Macedonian, Polish, Slovak, Slovenian, Serbian, Ukrainian, Danish, German, Icelandic, Dutch, Norwegian, Swedish, Catalan, Spanish, French, Galician, Italian, Portuguese, Romanian, Estonian, Finnish, Hungarian, Lithuanian, Latvian, Greek, Irish, Basque, Maltese, Turkish, Albanian, and Armenian.
-- **License:** [More Information Needed]
-<!-- - **Finetuned from model [optional]:** [More Information Needed] -->
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [https://github.com/JQL-AI](https://github.com/JQL-AI)
-- **Paper:** [arXiv]()
-<!-- - **Demo:** [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
-
-The model is designed to quickly and efficiently assess the educational value of texts—significantly faster than querying a large language model (LLM) directly. This makes it particularly useful for building large, high-quality text datasets.
-
-### Downstream Use
-
-In a downstream experiment, we demonstrate that training with high-quality texts selected by this model is faster and more effective than training with texts filtered only by heuristic methods. Details of our experiments can be found in the accompanying paper.
-
-<!-- ### 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.
-
-```python
-from utils.regression_head import RegressionHead
-from transformers.utils.hub import cached_file
-from utils.embedder import get_embedder_instance
-import torch
-
-# load embedder
-device = 'cuda'
-embedder = get_embedder_instance('Snowflake/snowflake-arctic-embed-m-v2.0', device, torch.bfloat16)
-# load JQL Edu annotation heads
-regression_head_checkpoints = {
-                'Edu-JQL-Gemma-SF': cached_file('Jackal-AI/JQL-Edu-Heads', 'checkpoints/edu-gemma-snowflake-balanced.ckpt'),
-                'Edu-JQL-Mistral-SF': cached_file('Jackal-AI/JQL-Edu-Heads', 'checkpoints/edu-mistral-snowflake-balanced.ckpt'),
-                'Edu-JQL-Llama-SF': cached_file('Jackal-AI/JQL-Edu-Heads', 'checkpoints/edu-llama-snowflake-balanced.ckpt'),
-            }
-regression_heads = {}
-for name, path in regression_head_checkpoints.items():
-    regression_heads[name] = RegressionHead.load_from_checkpoint(path, map_location=device).to(torch.bfloat16)
-
-# Given a single document
-doc = 'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua'
-embeddings = embedder.embed([doc])
-scores = {}
-with torch.no_grad():
-    for name, regression_head in regression_heads.items():
-        scores[f'score_{name}'] = regression_head(embeddings).cpu().squeeze(1)
-```
-<!-- 
-## 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) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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
-
-### Model Architecture and Objective
-
-The regression head consists of two linear layers with a ReLU activation function in between. The input dimension is 768, the hidden dimension is 1000, and the model uses bfloat16 precision.
-
-<!-- ### 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] -->
+---
+license: apache-2.0
+language:
+# slavic
+- bg
+- cs
+- hr
+- mk
+- pl
+- sk
+- sl
+- sr
+- uk
+
+# germanic
+- da
+- de
+- is
+- nl
+- no
+- sv
+
+# romance
+- ca
+- es
+- fr
+- gl
+- it
+- pt
+- ro
+
+# uralic
+- et
+- fi
+- hu
+
+# baltic
+- lt
+- lv
+
+# singleton
+- el
+- ga
+- eu
+- mt
+- tr
+- sq
+- hy
+
+
+base_model:
+- Snowflake/snowflake-arctic-embed-m-v2.0
+pipeline_tag: text-ranking
+---
+## Summary
+
+Multilingual JQL regression head that scores texts based on their educational value as described in our [paper](https://arxiv.org/abs/2505.22232).
+
+<!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). -->
+
+## Model Details
+
+### Model Description
+
+This model is a regression head built on top of the `Snowflake/snowflake-arctic-embed-m-v2.0` embedding model. It assigns a score to text documents, representing their educational value on a scale from 0 (lowest) to 5 (highest). Since the underlying embedding model provides language-aligned embeddings, the regression head can be used for multiple languages.
+
+We provide checkpoints for three different training sets. These training sets were generated by letting a large language model (LLM) annotate 500k text documents. We provide trained heads based on annotations from: Llama3.3-70B-it, Gemma-3-27B-it, and Mistral Small 3.1-24B-it. For each LLM, we also created training sets with balanced and unbalanced distributions of educational value scores. Checkpoints trained on balanced datasets are denoted with "balanced"; otherwise, they are denoted as "unbalanced".
+
+- **Developed by:** A collaboration between HessianAI, DFKI, Fraunhofer IAIS, Lamarr Institute, and TU Darmstadt.
+<!-- - **Funded by [optional]:** [More Information Needed] -->
+<!-- - **Shared by [optional]:** [More Information Needed] -->
+- **Model type:** Regression Head
+- **Language(s) (NLP):** Bulgarian, Czech, Croatian, Macedonian, Polish, Slovak, Slovenian, Serbian, Ukrainian, Danish, German, Icelandic, Dutch, Norwegian, Swedish, Catalan, Spanish, French, Galician, Italian, Portuguese, Romanian, Estonian, Finnish, Hungarian, Lithuanian, Latvian, Greek, Irish, Basque, Maltese, Turkish, Albanian, and Armenian.
+- **License:** Apache-2.0
+<!-- - **Finetuned from model [optional]:** [More Information Needed] -->
+
+As evaluated in the paper the trained regression heads generalize to any language of the backbone embedding model beyond those used in our training. 
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [github.com/JQL-AI/JQL-Annotation-Pipeline](https://github.com/JQL-AI/JQL-Annotation-Pipeline)
+- **Paper:** [arXiv](https://arxiv.org/abs/2505.22232)
+- **Project Page:** [https://huggingface.co/spaces/Jackal-AI/JQL]
+<!-- - **Demo:** [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
+
+The model is designed to quickly and efficiently assess the educational value of texts—significantly faster than querying a large language model (LLM) directly. This makes it particularly useful for building large, high-quality text datasets.
+
+### Downstream Use
+
+In a downstream experiment, we demonstrate that training with high-quality texts selected by this model is faster and more effective than training with texts filtered only by heuristic methods. Details of our experiments can be found in the accompanying paper.
+
+<!-- ### 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
+
+Usage is described in the accompanying [GitHub](https://github.com/JQL-AI/JQL-Annotation-Pipeline)
+<!-- 
+## 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) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **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
+
+### Model Architecture and Objective
+
+The regression head consists of two linear layers with a ReLU activation function in between. The input dimension is 768, the hidden dimension is 1000, and the model uses bfloat16 precision.
+
+<!-- ### 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] -->
+
+## 📖 Citation
+
+```bibtex
+@article{ali2025judging,
+    title     = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models},
+    author    = {
+      Mehdi Ali,
+      Manuel Brack,
+      Max Lübbering,
+      Elias Wendt,
+      Abbas Goher Khan,
+      Richard Rutmann,
+      Alex Jude,
+      Maurice Kraus,
+      Alexander Arno Weber,
+      Felix Stollenwerk,
+      David Kaczér,
+      Florian Mai,
+      Lucie Flek,
+      Rafet Sifa,
+      Nicolas Flores-Herr,
+      Joachim Köhler,
+      Patrick Schramowski,
+      Michael Fromm,
+      Kristian Kersting
+    },
+    year      = {2025},
+    journal   = {arXiv preprint arXiv:2505:22232}
+  }
+```