Update model card: add paper link, license, and update metadata

README.md

@@ -1,49 +1,43 @@
 ---
 base_model: sentence-transformers/all-mpnet-base-v2
+library_name: sentence-transformers
+pipeline_tag: text-retrieval
+license: apache-2.0
 tags:
 - sentence-transformers
-- sentence-similarity
+- text-retrieval
 - feature-extraction
-pipeline_tag: sentence-similarity
-library_name: sentence-transformers
+- work-domain
+- skill-extraction
 ---
 
-# SentenceTransformer
+# ConTeXT-Skill-Extraction-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model based on the `all-mpnet-base-v2` architecture. It is designed for work-domain AI tasks, specifically skill extraction and normalization, as part of the **WorkRB** (Work Research Benchmark) framework.
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+The model is presented in the paper [WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain](https://huggingface.co/papers/2604.13055).
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **License:** Apache 2.0
 
 ### Model Sources
 
+- **Paper:** [WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain](https://huggingface.co/papers/2604.13055)
+- **Repository:** [WorkRB on GitHub](https://github.com/techwolf-ai/WorkRB)
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
 
 ## Usage
 
 ### Direct Usage (Sentence Transformers)
 
-First install the Sentence Transformers library:
+First, install the Sentence Transformers library:
 
 ```bash
 pip install -U sentence-transformers
@@ -55,11 +49,12 @@ from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
 model = SentenceTransformer("jensjorisdecorte/ConTeXT-Skill-Extraction-base")
+
 # Run inference
 sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
+    'Proficient in Python programming and machine learning.',
+    'Experienced in project management and agile methodologies.',
+    'Knowledge of cloud computing and AWS infrastructure.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -71,41 +66,14 @@ print(similarities.shape)
 # [3, 3]
 ```
 
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
+## Full Model Architecture
 
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
 
 ## Training Details
 
@@ -120,22 +88,16 @@ You can finetune this model on your own dataset.
 
 ## Citation
 
-### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+If you find this model useful, please consider citing the following work:
+
+```bibtex
+@misc{delange2025unifiedworkembeddings,
+      title={Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker}, 
+      author={Matthias De Lange and Jens-Joris Decorte and Jeroen Van Hautte},
+      year={2025},
+      eprint={2511.07969},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2511.07969}, 
+}
+```