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monot5-small-msmarco-10k

Name: monot5-small-msmarco-10k
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castorini/monot5-small-msmarco-10k

quantized

apache-2.0

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monot5-small-msmarco-10k

A Teradata-compatible Generative Reranker Model

An ONNX-converted, BYOM-ready copy of castorini/monot5-small-msmarco-10k: given a (query, passage) pair, it outputs a single relevance score for that pair. Unlike the other rerankers in this collection, it's a seq2seq model, not a classification head, so it can't run through ONNXEmbeddings -- it's deployed through Teradata's ONNXSeq2Seq instead, using a small graph modification described in "How it works" below.

Reranking in two-stage retrieval

Retrieval pipelines typically run in two stages. The first stage -- a vector search over an index such as HNSW, or classical keyword search -- scans the whole corpus and returns a shortlist (say, the top 20-100 candidates). It's fast because it embeds the query and each document independently and only ever compares the resulting vectors; the two texts never interact. A reranker is a cross-encoder: it reads the query and one candidate together, token by token, and produces a single relevance score for that pair. That joint attention makes it far more accurate than the first-stage vector search, but also too expensive to run over an entire corpus. The standard pattern is therefore to let cheap first-stage retrieval narrow things down, then use a reranker like this one to re-score and re-order just that shortlist.

Overview of this Model

T5-small, 60M params (ONNX fp32: 294MB)
512 max input tokens
Output: relevance_token_id, a token id that decodes to <bucket_NNN> -- parse NNN and divide by 999 for a [0, 1] relevance score (see Quickstart)
License: Apache-2.0 (from upstream)
Reference to Original Model: https://huggingface.co/castorini/monot5-small-msmarco-10k

Quickstart: Deploying this Model in Teradata

python

import getpass
import teradataml as tdml
from huggingface_hub import hf_hub_download

repo_id  = "martinhillebrandtd/monot5-small-msmarco-10k"
model_id = "monot5-small-msmarco-10k"

# 1. Download artifacts from this repo
hf_hub_download(repo_id=repo_id, filename="onnx/model.onnx", local_dir="./")
hf_hub_download(repo_id=repo_id, filename="tokenizer.json",  local_dir="./")

# 2. Connect to Teradata
tdml.create_context(host=input("host: "), username=input("user: "), password=getpass.getpass("password: "))

# 3. Load model + tokenizer into BYOM tables
tdml.save_byom(model_id=model_id, model_file="onnx/model.onnx", table_name="seq2seq_models")
tdml.save_byom(model_id=model_id, model_file="tokenizer.json",  table_name="seq2seq_tokenizers")

# 4. Score an existing chunks table against one fixed query. No Const_* generation parameters
#    are needed: this graph has no BeamSearch op and no extra inputs beyond
#    input_ids/attention_mask. The bucket number is parsed out of relevance_token_id in SQL,
#    with REGEXP_SUBSTR -- no client-side Python post-processing.
chunks_table = "your_chunks_table"  # columns: chunk_id, chunk_txt
QUERY = "What causes rain?"

query = f"""
SELECT
    chunk_id,
    chunk_txt,
    relevance_token_id,
    CAST(REGEXP_SUBSTR(relevance_token_id, '[0-9]+') AS INTEGER) / 999.0 AS relevance_score
FROM mldb.ONNXSeq2Seq(
    ON (SELECT chunk_id, chunk_txt, 'Query: {QUERY} Document: ' || chunk_txt || ' Relevant:' AS txt
         FROM {chunks_table}) AS InputTable
    ON (SELECT model_id, model FROM seq2seq_models
         WHERE model_id = '{model_id}') AS ModelTable DIMENSION
    ON (SELECT model AS tokenizer FROM seq2seq_tokenizers
         WHERE model_id = '{model_id}') AS TokenizerTable DIMENSION
    USING
        Accumulate('chunk_id', 'chunk_txt')
        ModelOutputTensor('relevance_token_id')
        SkipSpecialTokens('false')
        OverwriteCachedModel('true')
) AS t
"""
result = tdml.DataFrame.from_query(query).to_pandas().sort_values("relevance_score", ascending=False)

Real output, QUERY = "What causes rain?" against three sample chunks:

chunk_id	chunk_txt	relevance_token_id	relevance_score
0	Rain forms when water vapor in clouds condenses into droplets heavy enough to fall.	`<bucket_306>`	0.306
1	Paris is the capital and most populous city of France.	`<bucket_000>`	0.0
2	Airplanes generate lift through the shape and angle of their wings.	`<bucket_000>`	0.0

Combining with vector search for two-stage retrieval

A typical pipeline: embed your corpus once with an embedding model, then at query time use VectorDistance to get a shortlist of candidates by cosine distance, and rerank only that shortlist with this model.

sql

-- Stage 1: shortlist the 20 nearest chunks to the query embedding
CREATE VOLATILE TABLE shortlist AS (
    SELECT ref_id AS chunk_id, distance
    FROM TD_VECTORDISTANCE(
        ON query_embedding AS TargetTable
        ON chunk_embeddings AS ReferenceTable DIMENSION
        USING
            TargetIDColumn('query_id')
            TargetFeatureColumns('[emb_0:emb_383]')
            RefIDColumn('chunk_id')
            RefFeatureColumns('[emb_0:emb_383]')
            DistanceMeasure('cosine')
            TopK(20)
    ) AS dt
) WITH DATA ON COMMIT PRESERVE ROWS;

-- Stage 2: rerank only those 20 shortlisted chunks (see Quickstart above, with
-- your_chunks_table replaced by a join against `shortlist`)

How it works

Teradata's ONNXSeq2Seq is built around com.microsoft.BeamSearch and always returns tokenizer-decoded text, never a raw number -- it's designed to reuse a text-generation model as a generic "text in, next-token-as-text out" engine. The original model reads relevance as the softmax probability of the token "true" at the first decode step -- a continuous score, not free text. Run unmodified, it would only ever return the literal string "true" or "false", discarding exactly the information a reranker needs.

This repo's ONNX graph is modified (graph surgery, no beam search, no third-party conversion package) to smuggle that score through as decoded text instead:

1000 dedicated vocabulary tokens, <bucket_000>...<bucket_999>, are added to the tokenizer -- 3-decimal-digit precision, i.e. quantization error ≤ 0.0005. They're only ever used as an output lookup, never fed through the model's own embedding layer or lm_head, so no model embedding resizing is needed.
A small wrapper module runs the real T5 encoder + one real decoder step, takes the softmax over just the true/false logits to get the real probability, quantizes it to a bucket index, and outputs that bucket token's id directly, as an int64 tensor named relevance_token_id. The exported graph has zero BeamSearch nodes -- just a plain feedforward computation.
ONNXSeq2Seq's ModelOutputTensor argument validates against the model's actual declared output tensors, not a hardcoded whitelist -- so relevance_token_id is accepted, and its value is looked up in the tokenizer's vocabulary and returned as "<bucket_NNN>".

Verified end-to-end, both locally (onnxruntime) and against a live Vantage instance, versus the true PyTorch score (T5ForConditionalGeneration, real softmax over the true/false logits):

	mean \|delta\|	max \|delta\|
Local ONNX (bucket-decoded) vs. true PyTorch	0.00021	0.00041
Live Teradata `ONNXSeq2Seq` vs. true PyTorch	0.00021	0.00041

Both at the quantization noise floor for 1000 buckets (max possible error ≈ 1/999 ≈ 0.001).

How this model was converted

convert.py is fully self-contained: no third-party conversion package is used. It (1) adds the 1000 bucket tokens to the tokenizer, (2) wraps T5ForConditionalGeneration's real forward pass in a small torch.nn.Module that computes the softmax score and looks up the matching bucket token id, and (3) exports via plain torch.onnx.export -- no merged/BeamSearch export machinery needed. See convert.py for the full implementation, and test_local.py for the ONNX-vs-PyTorch parity check above. The same technique generalizes to other seq2seq "generative" rerankers that score by reading a token probability rather than a classification logit -- swap in the target model's own true/false (or equivalent) token ids and prompt template.

Disclaimer

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Teradata/monot5-small-msmarco-10k