
# Uni-ColBERTer (Dim: 1) for Passage Retrieval

If you want to know more about our (Uni-)ColBERTer architecture check out our paper: https://arxiv.org/abs/2203.13088 🎉

For more information, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/colberter

## Limitations & Bias

- The model is only trained on english text.

- The model inherits social biases from both DistilBERT and MSMARCO. 

- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. 

## Citation

If you use our model checkpoint please cite our work as:

```
@article{Hofstaetter2022_colberter,
 author = {Sebastian Hofst{\"a}tter and Omar Khattab and Sophia Althammer and Mete Sertkan and Allan Hanbury},
 title = {Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction},
 publisher = {arXiv},
 url = {https://arxiv.org/abs/2203.13088},
 doi = {10.48550/ARXIV.2203.13088},
 year = {2022},
}
```
{"language": "en", "license": "apache-2.0", "tags": ["bag-of-words", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]}
transformers pytorch ColBERT bag-of-words dense-passage-retrieval knowledge-distillation en dataset:ms_marco arxiv:2203.13088 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us 


# Uni-ColBERTer (Dim: 1) for Passage Retrieval

If you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL 

For more information, source code, and a minimal usage example please visit: URL

## Limitations & Bias

- The model is only trained on english text.

- The model inherits social biases from both DistilBERT and MSMARCO. 

- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. 

If you use our model checkpoint please cite our work as:


# Uni-ColBERTer (Dim: 1) for Passage Retrieval

If you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL 

For more information, source code, and a minimal usage example please visit: URL ## Limitations & Bias

- The model is only trained on english text.

- The model inherits social biases from both DistilBERT and MSMARCO. 

- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. 

If you use our model checkpoint please cite our work as:
TAGS
#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us 
 # Uni-ColBERTer (Dim: 1) for Passage Retrieval

If you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL 

For more information, source code, and a minimal usage example please visit: URL ## Limitations & Bias

- The model is only trained on english text.

- The model inherits social biases from both DistilBERT and MSMARCO. 

- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. 

If you use our model checkpoint please cite our work as:
passage: TAGS
#transformers #pytorch #ColBERT #bag-of-words #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2203.13088 #license-apache-2.0 #endpoints_compatible #region-us 
# Uni-ColBERTer (Dim: 1) for Passage Retrieval

If you want to know more about our (Uni-)ColBERTer architecture check out our paper: URL 

For more information, source code, and a minimal usage example please visit: URL## Limitations & Bias

- The model is only trained on english text.

- The model inherits social biases from both DistilBERT and MSMARCO. 

- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text. 

If you use our model checkpoint please cite our work as: