Update README.md

README.md

@@ -1,18 +1,24 @@
 ---
-datasets: []
-language: []
+datasets:
+- unicamp-dl/mmarco
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
 - sentence-transformers
-- sentence-similarity
 - feature-extraction
+- sentence-similarity
+license: mit
 widget: []
+base_model:
+- BAAI/bge-m3
 ---
 
-# SentenceTransformer
+# BGE-m3 ZH mMARCO/v2 Transliterated Queries
+
+This is a [BGE-M3](https://huggingface.co/BAAI/bge-m3) model post-trained on the Chinese dataset from MMARCO/v2. 
+The queries are transliterated Chinese to English using [uroman](https://github.com/isi-nlp/uroman).
 
-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.
+The model was used for the SIGIR 2025 Short paper: Lost in Transliteration: Bridging the Script Gap in Neural IR.
 
 ## Model Details
 
@@ -26,119 +32,13 @@ This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
-### Model Sources
-
-- **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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 1024, '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})
-  (2): Normalize()
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 1024]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-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>
--->
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-### Out-of-Scope Use
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-## Bias, Risks and Limitations
-
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-### Recommendations
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-
 ## Training Details
 
 ### Framework Versions
-- Python: 3.10.14
-- Sentence Transformers: 3.0.1
-- Transformers: 4.41.2
-- PyTorch: 2.4.0.post301
-- Accelerate: 0.32.1
-- Datasets: 2.19.1
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-<!--
-## Glossary
-
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+- Python: 3.10.13
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.1
+- PyTorch: 2.4.1
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3