Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
 base_model: colbert-ir/colbertv2.0
-datasets: []
-language: []
+language:
+- en
 library_name: sentence-transformers
 pipeline_tag: sentence-similarity
 tags:
@@ -9,29 +9,25 @@ tags:
 - sentence-similarity
 - feature-extraction
 widget: []
+license: mit
 ---
 
-# SentenceTransformer based on colbert-ir/colbertv2.0
+# PyLate version of colbert-ir/colbertv2.0
+This checkpoint is a version of [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) compatible with the [PyLate](https://github.com/lightonai/pylate) library.
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0). It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+All the credits belong to the original authors and we thank Omar Khattab for allowing us to share this version of the model.
+
+Please refer to the [original repository](https://huggingface.co/colbert-ir/colbertv2.0) and [paper](https://arxiv.org/abs/2112.01488) for more information about the model and to [PyLate repository](https://github.com/lightonai/pylate) for information about usage of the model.
 
 ## Model Details
+The model maps query and documents to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
 
 ### Model Description
-- **Model Type:** Sentence Transformer
+- **Model Type:** PyLate model
 - **Base model:** [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) <!-- at revision c1e84128e85ef755c096a95bdb06b47793b13acf -->
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 128 tokens
 - **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)
 
 ### Full Model Architecture
 
@@ -42,103 +38,26 @@ ColBERT(
 )
 ```
 
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
+### Citation
 ```
-
-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, 128]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
+@inproceedings{santhanam-etal-2022-colbertv2,
+    title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction",
+    author = "Santhanam, Keshav  and
+      Khattab, Omar  and
+      Saad-Falcon, Jon  and
+      Potts, Christopher  and
+      Zaharia, Matei",
+    editor = "Carpuat, Marine  and
+      de Marneffe, Marie-Catherine  and
+      Meza Ruiz, Ivan Vladimir",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.272",
+    doi = "10.18653/v1/2022.naacl-main.272",
+    pages = "3715--3734",
+    abstract = "Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude. In this work, we introduce ColBERTv2, a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction. We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6{--}10x.",
+}
 ```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
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-
-<!--
-### 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
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-## Bias, Risks and Limitations
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-### Recommendations
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-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-
-## Training Details
-
-### Framework Versions
-- Python: 3.12.4
-- Sentence Transformers: 3.0.1
-- Transformers: 4.45.0.dev0
-- PyTorch: 2.4.0+cu121
-- Accelerate: 0.33.0
-- Datasets: 2.20.0
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-<!--
-## Glossary
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