Model Card first version

README.md

@@ -5,14 +5,26 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
-
+- multilingual
+license: agpl-3.0
+language:
+- de
+- fr
+- en
+- lb
+base_model:
+- Alibaba-NLP/gte-multilingual-base
 ---
 
-# {MODEL_NAME}
+# OCR-robust-gte-multilingual-base
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+## Model Details
+
+This model that was adapted to be more robust to OCR Noise in German and French. This model would be particularly useful for libraries and archives in Central Europe that want to perform semantic search and longitudinal studies within their collections.
+
+This is an [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) model that was further adapted by (Michail et al., 2025)
 
 ## Usage (Sentence-Transformers)
 
@@ -28,7 +40,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer('OCR-robust-gte-multilingual-base}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -37,21 +49,15 @@ print(embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+I will add the model specific evaluation results once the instance is running again.
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+## Training Details
 
+### Training Dataset
 
-## Training
+### Contrastive Training
 The model was trained with the parameters:
 
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 2500 with parameters:
-```
-{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
 **Loss**:
 
 `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
@@ -87,6 +93,24 @@ SentenceTransformer(
 )
 ```
 
-## Citing & Authors
+## Citation
+
+### BibTeX
+
+#### Cheap Character Noise for OCR-Robust Multilingual Embeddings (introducing paper)
+
+```bibtex
+update once available
+```
+
+
+#### Original Multilingual GTE Model
 
-<!--- Describe where people can find more information -->
+```bibtex
+@inproceedings{zhang2024mgte,
+  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
+  author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
+  booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
+  pages={1393--1412},
+  year={2024}
+}