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README.md

@@ -1,143 +1,49 @@
 ---
+library_name: transformers
 tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- dense
+- arxiv:2409.12737
+license: mit
+base_model:
+- FacebookAI/xlm-roberta-large
 pipeline_tag: sentence-similarity
-library_name: sentence-transformers
 ---
 
-# SentenceTransformer
+Current pre-trained cross-lingual sentence encoders approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades the sentence representation. We propose MEXMA, a novel approach that integrates both sentence-level and token-level objectives. The sentence representation in one language is used to predict masked tokens in another language, with both the sentence representation and all tokens directly updating the encoder. We show that adding token-level objectives greatly improves the sentence representation quality across several tasks. Our approach outperforms current pre-trained cross-lingual sentence encoders on bi-text mining as well as several downstream tasks. We also analyse the information encoded in our tokens, and how the sentence representation is built from them.
 
-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
-
-### Model Description
-- **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 1024 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **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)
+# Usage
+You use this model as you would any other XLM-RoBERTa model, taking into account that the "pooler" has not been trained, so you should use the CLS the encoder outputs directly as your sentence representation:
+```
+from transformers import AutoTokenizer, XLMRobertaModel
 
-### Full Model Architecture
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
+model = XLMRobertaModel.from_pretrained("facebook/MEXMA", add_pooling_layer=False)
+example_sentences = ['Sentence1', 'Sentence2']
+example_inputs = tokenizer(example_sentences, return_tensors='pt')
 
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
-  (1): Pooling({'word_embedding_dimension': 1024, '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})
-)
+outputs = model(**example_inputs)
+sentence_representation = outputs.last_hidden_state[:, 0]
+print(sentence_representation.shape) # torch.Size([2, 1024])
 ```
 
-## Usage
+# License
+This model is released under the MIT license.
 
-### Direct Usage (Sentence Transformers)
+# Training code
+For the training code of this model, please check the official [MEXMA repo](https://github.com/facebookresearch/mexma).
 
-First install the Sentence Transformers library:
+# Paper
+[MEXMA: Token-level objectives improve sentence representations](https://arxiv.org/abs/2409.12737)
 
-```bash
-pip install -U sentence-transformers
+# Citation
+If you use this model in your work, please cite:
 ```
-
-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)
-# tensor([[1.0000, 0.8701, 0.8232],
-#         [0.8701, 1.0000, 0.7860],
-#         [0.8232, 0.7860, 1.0000]])
+@misc{janeiro2024mexma,
+  title={MEXMA: Token-level objectives improve sentence representations}, 
+  author={João Maria Janeiro and Benjamin Piwowarski and Patrick Gallinari and Loïc Barrault},
+  year={2024},
+  eprint={2409.12737},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2409.12737}, 
+}
 ```
-
-<!--
-### Direct Usage (Transformers)
-
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-</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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-
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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: 5.1.0
-- Transformers: 4.55.3
-- PyTorch: 2.8.0+cu128
-- Accelerate: 1.10.0
-- Datasets: 
-- Tokenizers: 0.21.4
-
-## Citation
-
-### BibTeX
-
-<!--
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