You can deploy this model and start earning money today!

vespa-engine/
col-minilm

Name: col-minilm
Availability: InStock

cross-encoder/ms-marco-MiniLM-L-6-v2

quantized

mit

endpoints_compatible

Views

No views yet

MS Marco Ranking with ColBERT on Vespa.ai

Model is based on ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. This BERT model is based on cross-encoder/ms-marco-MiniLM-L-6-v2 and trained using the original ColBERT training routine.

This model has 22.3M trainable parameters and is approximately 2x faster than vespa-engine/colbert-medium and with better or on pair MRR@10 on dev.

The model weights have been tuned by training using a randomized sample of MS Marco training triplets MSMARCO-Passage-Ranking.

To use this model with vespa.ai for MS Marco Passage Ranking, see MS Marco Ranking using Vespa.ai sample app.

MS Marco Passage Ranking

MS Marco Passage Ranking Query Set	MRR@10 ColBERT on Vespa.ai
Dev	0.364

Recall@k On Dev (6980 queries)

K	Recall@K
50	0.816
200	0.905
1000	0.939

The MRR@10 on dev is achieved by re-ranking 1K retrieved by a dense retriever based on sentence-transformers/msmarco-MiniLM-L-6-v3. Re-ranking the original top 1000 dev is 0.354 MRR@10 (Recall@1K 0.82).

The official baseline BM25 ranking model MRR@10 0.16 on eval and 0.167 on dev question set. See MS Marco Passage Ranking Leaderboard.

Export ColBERT query encoder to ONNX

We represent the ColBERT query encoder in the Vespa runtime, to map the textual query representation to the tensor representation. For this we use Vespa's support for running ONNX models. One can use the following snippet to export the model for serving.

python

from transformers import BertModel
from transformers import BertPreTrainedModel
from transformers import BertConfig
import torch 
import torch.nn as nn

class VespaColBERT(BertPreTrainedModel):
   
    def __init__(self,config):
        super().__init__(config)
        self.bert = BertModel(config)
        self.linear = nn.Linear(config.hidden_size, 32, bias=False)
        self.init_weights()
        
    def forward(self, input_ids, attention_mask):
        Q = self.bert(input_ids,attention_mask=attention_mask)[0]
        Q = self.linear(Q)
        return torch.nn.functional.normalize(Q, p=2, dim=2)  

colbert_query_encoder = VespaColBERT.from_pretrained("vespa-engine/col-minilm") 

#Export model to ONNX for serving in Vespa 

input_names = ["input_ids", "attention_mask"]
output_names = ["contextual"]
#input, max 32 query term
input_ids = torch.ones(1,32, dtype=torch.int64)
attention_mask = torch.ones(1,32,dtype=torch.int64)
args = (input_ids, attention_mask)
torch.onnx.export(colbert_query_encoder,
                args=args,
                f="query_encoder_colbert.onnx",
                input_names = input_names,
                output_names = output_names,
                dynamic_axes = {
                    "input_ids": {0: "batch"},
                    "attention_mask": {0: "batch"},
                    "contextual": {0: "batch"},
                },
                opset_version=11)

Representing the model on Vespa.ai

See Ranking with ONNX models and the MS Marco Ranking sample app