# ColBERT NQ Checkpoint

This trained model is based on the [ColBERT](https://github.com/stanford-futuredata/ColBERT) model, trained on the [Natural Questions](https://huggingface.co/datasets/natural_questions) dataset. 

# Model Details

Model is based on ColBERT, which in turn is based around a BERT encoder. The model is trained for text retrieval using a contrastive loss; given a query there's a relevant and non relevant passages. 

The corpus is based on [Wikipeida](https://huggingface.co/datasets/wiki_dpr). 

# Uses

Model can be used by the [ColBERT](https://github.com/stanford-futuredata/ColBERT) codebase to initiate a retriever; one needs to build a vector index and then queries can be ran. 

# Evaluation

Evaluation results on NQ dev:

<table>
<colgroup>
<col  class="org-right">
<col  class="org-right">
<col  class="org-right">
</colgroup>
<thead>
<tr>
<th scope="col" class="org-right">NQ</th>
<th scope="col" class="org-right">Recall</th>
<th scope="col" class="org-right">MRR</th>
</tr>
</thead>

<tbody>
<tr>
<td class="org-right">10</td>
<td class="org-right">71.1</td>
<td class="org-right">52.0</td>
</tr>

<tr>
<td class="org-right">20</td>
<td class="org-right">76.3</td>
<td class="org-right">52.3</td>
</tr>

<tr>
<td class="org-right">50</td>
<td class="org-right">80.4</td>
<td class="org-right">52.5</td>
</tr>

<tr>
<td class="org-right">100</td>
<td class="org-right">82.7</td>
<td class="org-right">52.5</td>
</tr>
</tbody>
</table>
{"language": ["en"], "license": "cc-by-sa-3.0", "tags": ["colbert"], "datasets": ["natural_questions"]}
transformers pytorch bert colbert en dataset:natural_questions license:cc-by-sa-3.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #colbert #en #dataset-natural_questions #license-cc-by-sa-3.0 #endpoints_compatible #region-us 

ColBERT NQ Checkpoint
=====================


This trained model is based on the ColBERT model, trained on the Natural Questions dataset.


Model Details
=============


Model is based on ColBERT, which in turn is based around a BERT encoder. The model is trained for text retrieval using a contrastive loss; given a query there's a relevant and non relevant passages.


The corpus is based on Wikipeida.


Uses
====


Model can be used by the ColBERT codebase to initiate a retriever; one needs to build a vector index and then queries can be ran.


Evaluation
==========


Evaluation results on NQ dev:



TAGS
#transformers #pytorch #bert #colbert #en #dataset-natural_questions #license-cc-by-sa-3.0 #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #colbert #en #dataset-natural_questions #license-cc-by-sa-3.0 #endpoints_compatible #region-us 
