File size: 3,179 Bytes
e7e3092 ec9f05f e7e3092 44cb111 e7e3092 44cb111 63ecbef 44cb111 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
# MS Marco Ranking with ColBERT on Vespa.ai
Model is based on [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832).
This BERT model is based on [google/bert_uncased_L-8_H-512_A-8](https://huggingface.co/google/bert_uncased_L-8_H-512_A-8) and trained using the
original [ColBERT training routine](https://github.com/stanford-futuredata/ColBERT/).
The model weights have been tuned by training using the `triples.train.small.tar.gz from` [MSMARCO-Passage-Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking).
To use this model with vespa.ai for MS Marco Passage Ranking, see
[MS Marco Ranking using Vespa.ai sample app](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking).
# MS Marco Passage Ranking
| MS Marco Passage Ranking Query Set | MRR@10 ColBERT on Vespa.ai |
|------------------------------------|----------------|
| Dev | 0.354 |
| Eval | 0.347 |
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](https://microsoft.github.io/msmarco/).
## 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/colbert-medium")
#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](https://docs.vespa.ai/documentation/onnx.html) and [MS Marco Ranking sample app](https://github.com/vespa-engine/sample-apps/tree/master/msmarco-ranking)
|