Upload SentenceTransformer export

1_Pooling/config.json

@@ -1,7 +1,7 @@
 {
     "word_embedding_dimension": 384,
-    "pooling_mode_cls_token": true,
-    "pooling_mode_mean_tokens": false,
+    "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,

README.md

@@ -6,16 +6,11 @@ tags:
 - dense
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
-license: apache-2.0
-language:
-- en
-base_model:
-- intfloat/e5-small-unsupervised
 ---
 
 # SentenceTransformer
 
-This is a [sentence-transformers](https://www.SBERT.net) model that maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -40,7 +35,7 @@ This is a [sentence-transformers](https://www.SBERT.net) model that maps sentenc
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
-  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (1): Pooling({'word_embedding_dimension': 384, '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})
   (2): Dense({'in_features': 384, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
 )
 ```
@@ -74,9 +69,9 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.8807, 0.8350],
-#         [0.8807, 1.0000, 0.8025],
-#         [0.8350, 0.8025, 1.0000]])
+# tensor([[1.0000, 0.8101, 0.4448],
+#         [0.8101, 1.0000, 0.4627],
+#         [0.4448, 0.4627, 1.0000]])
 ```
 
 <!--
@@ -116,35 +111,6 @@ You can finetune this model on your own dataset.
 -->
 
 ## Training Details
-This model was trained via a re-implementation of the **LEAF** distillation framework (Vujanic & Rueckstiess, 2025). As no official training code was released, the procedure was independently reproduced based on the paper specification.
-
-The student backbone is `intfloat/e5-small-unsupervised` (Wang et al., 2022). Training aligns student embeddings to a stronger teacher model through representation-level distillation, rather than contrastive loss with hard negatives. The objective encourages geometric alignment in embedding space using large-scale unlabeled text.
-
-A projection layer expands the 384-dimensional backbone representations to 1024 dimensions.
-
-## Citation
-
-If you use this model, please cite:
-
-```bibtex
-@misc{mdbr_leaf,
-  title={LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations},
-  author={Robin Vujanic and Thomas Rueckstiess},
-  year={2025},
-  eprint={2509.12539},
-  archivePrefix={arXiv},
-  primaryClass={cs.IR},
-  url={https://arxiv.org/abs/2509.12539}
-}
-
-@article{wang2022e5,
-  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
-  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
-  journal={arXiv preprint arXiv:2212.03533},
-  year={2022},
-  url={https://arxiv.org/abs/2212.03533}
-}
-```
 
 ### Framework Versions
 - Python: 3.11.5
@@ -153,4 +119,26 @@ If you use this model, please cite:
 - PyTorch: 2.10.0+cu128
 - Accelerate: 
 - Datasets: 
-- Tokenizers: 0.22.2
+- Tokenizers: 0.22.2
+
+## 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.*
+-->