ms-marco-ettin-reranker

About

What ms-marco-ettin-reranker is

CrossEncoder based on jhu-clsp/ettin-encoder-32m This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-32m on the msmarco dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search. Model Details Model Description - Model Type: Cross Encoder - Base model: jhu-clsp/ettin-encoder-32m <!-- at revision 1b8ba06455dd44f80fc9c1ca9e22806157a57379 -- - Maximum Sequence Length: 512 tokens - Number of Output Labels: 1 label - Training Dataset: - msmarco - Language: en Model Sources - Documentation: Sentence Transformers Documentation - Documentation: Cross Encoder Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Cross Encoders on Hugging Face Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Direct Usage (Transformers) -- Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. -- Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. -- Evaluation Metrics Cross Encoder Reranking Datasets: NanoMSMARCOR100, NanoNFCorpusR100 and NanoNQR100 Evaluated with <codeCrossEncoderRerankingEvaluator</code with these parameters: Cross Encoder Nano BEIR Dataset: NanoBEIRR100mean Evaluated with <codeCrossEncoderNanoBEIREvaluator</code with these parameters: Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -- Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -- Training Details Training Dataset msmarco Dataset: msmarco at 9e329ed Size: 39,770,704 training samples Columns: <codequeryid</code, <codepositiveid</code, <codenegativeid</code, and <codescore</code Approximate statistics based on the first 1000 samples: Samples: Loss: <codeMarginMSELoss</code with these parameters: Evaluation Dataset msmarco Dataset: msmarco at 9e329ed Size: 10,000 evaluation samples Columns: <codequeryid</code, <codep

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ms-marco-ettin-reranker for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ms-marco-ettin-reranker below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ms-marco-ettin-reranker","input":"text to embed"}'

Details

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Papers

Licence

Commercial licence needed

Sources

Weights & code

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