Fix CPU benchmark (ran on fp32 this time)

025501c verified 3 days ago

70 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- cross-encoder
	- reranker
	- generated_from_trainer
	- dataset_size:143393475
	- loss:MSELoss
	base_model: jhu-clsp/ettin-encoder-150m
	pipeline_tag: text-ranking
	library_name: sentence-transformers
	metrics:
	- map
	- mrr@10
	- ndcg@10
	model-index:
	- name: ettin-reranker-150m-v1
	results:
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoMSMARCO R100
	type: NanoMSMARCO_R100
	metrics:
	- type: map
	value: 0.6425
	name: Map
	- type: mrr@10
	value: 0.6459
	name: Mrr@10
	- type: ndcg@10
	value: 0.7243
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNFCorpus R100
	type: NanoNFCorpus_R100
	metrics:
	- type: map
	value: 0.3657
	name: Map
	- type: mrr@10
	value: 0.5762
	name: Mrr@10
	- type: ndcg@10
	value: 0.4067
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoNQ R100
	type: NanoNQ_R100
	metrics:
	- type: map
	value: 0.7484
	name: Map
	- type: mrr@10
	value: 0.7678
	name: Mrr@10
	- type: ndcg@10
	value: 0.7992
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoFiQA2018 R100
	type: NanoFiQA2018_R100
	metrics:
	- type: map
	value: 0.563
	name: Map
	- type: mrr@10
	value: 0.6733
	name: Mrr@10
	- type: ndcg@10
	value: 0.6127
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoTouche2020 R100
	type: NanoTouche2020_R100
	metrics:
	- type: map
	value: 0.472
	name: Map
	- type: mrr@10
	value: 0.8155
	name: Mrr@10
	- type: ndcg@10
	value: 0.5518
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoSciFact R100
	type: NanoSciFact_R100
	metrics:
	- type: map
	value: 0.716
	name: Map
	- type: mrr@10
	value: 0.713
	name: Mrr@10
	- type: ndcg@10
	value: 0.7613
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoHotpotQA R100
	type: NanoHotpotQA_R100
	metrics:
	- type: map
	value: 0.9307
	name: Map
	- type: mrr@10
	value: 1.0
	name: Mrr@10
	- type: ndcg@10
	value: 0.9573
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoArguAna R100
	type: NanoArguAna_R100
	metrics:
	- type: map
	value: 0.6597
	name: Map
	- type: mrr@10
	value: 0.6579
	name: Mrr@10
	- type: ndcg@10
	value: 0.7375
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoFEVER R100
	type: NanoFEVER_R100
	metrics:
	- type: map
	value: 0.9436
	name: Map
	- type: mrr@10
	value: 0.975
	name: Mrr@10
	- type: ndcg@10
	value: 0.96
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoDBPedia R100
	type: NanoDBPedia_R100
	metrics:
	- type: map
	value: 0.679
	name: Map
	- type: mrr@10
	value: 0.8939
	name: Mrr@10
	- type: ndcg@10
	value: 0.7494
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoClimateFEVER R100
	type: NanoClimateFEVER_R100
	metrics:
	- type: map
	value: 0.4903
	name: Map
	- type: mrr@10
	value: 0.7496
	name: Mrr@10
	- type: ndcg@10
	value: 0.5722
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoSCIDOCS R100
	type: NanoSCIDOCS_R100
	metrics:
	- type: map
	value: 0.3227
	name: Map
	- type: mrr@10
	value: 0.5548
	name: Mrr@10
	- type: ndcg@10
	value: 0.3742
	name: Ndcg@10
	- task:
	type: cross-encoder-reranking
	name: Cross Encoder Reranking
	dataset:
	name: NanoQuoraRetrieval R100
	type: NanoQuoraRetrieval_R100
	metrics:
	- type: map
	value: 0.9608
	name: Map
	- type: mrr@10
	value: 0.98
	name: Mrr@10
	- type: ndcg@10
	value: 0.9753
	name: Ndcg@10
	- task:
	type: cross-encoder-nano-beir
	name: Cross Encoder Nano BEIR
	dataset:
	name: NanoBEIR R100 mean
	type: NanoBEIR_R100_mean
	metrics:
	- type: map
	value: 0.6534
	name: Map
	- type: mrr@10
	value: 0.7695
	name: Mrr@10
	- type: ndcg@10
	value: 0.7063
	name: Ndcg@10
	---

	# ettin-reranker-150m-v1

	This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) on the [cross-encoder/ettin-reranker-v1-data](https://huggingface.co/datasets/cross-encoder/ettin-reranker-v1-data) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

	See the [release blogpost](https://huggingface.co/blog/ettin-reranker) for details on the training recipe, evaluation results, and speed benchmarks against other public rerankers. The [Evaluation](#evaluation) section below also has the headline numbers.

	## Model Details

	### Model Description
	- Model Type: Cross Encoder
	- Base model: [jhu-clsp/ettin-encoder-150m](https://huggingface.co/jhu-clsp/ettin-encoder-150m) <!-- at revision 45d08642849e5c5701b162671ac811b7654bfd9f -->
	- Maximum Sequence Length: 7999 tokens
	- Number of Output Labels: 1 label
	- Supported Modality: Text
	- Training Dataset: [cross-encoder/ettin-reranker-v1-data](https://huggingface.co/datasets/cross-encoder/ettin-reranker-v1-data)
	- Language: en
	- License: apache-2.0

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Documentation: [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
	- Repository: [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
	- Hugging Face: [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

	### Full Model Architecture

	```
	CrossEncoder(
	(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertModel'})
	(1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True})
	(2): Dense({'in_features': 768, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.activation.GELU', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
	(3): LayerNorm({'dimension': 768})
	(4): Dense({'in_features': 768, 'out_features': 1, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'scores'})
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import CrossEncoder

	# Download from the 🤗 Hub
	model = CrossEncoder(
	"cross-encoder/ettin-reranker-150m-v1",
	model_kwargs={"dtype": "bfloat16", "attn_implementation": "flash_attention_2"}, # Optional: pip install kernels
	)

	# Get scores for pairs of inputs
	query = "Which planet is known as the Red Planet?"
	passages = [
	"Venus is often called Earth's twin because of its similar size and proximity.",
	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
	]
	scores = model.predict([(query, passage) for passage in passages])
	print(scores)
	# [ 4.875 11.625 7.375 10.3125]

	# Or rank passages by relevance to a single query
	ranked = model.rank(query, passages)
	print(ranked)
	# [{'corpus_id': 1, 'score': np.float32(11.625)}, ...]
	```

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### MTEB(eng, v2) Retrieval

	Each model in the ettin-reranker-v1 family was evaluated on the full [`MTEB(eng, v2)` Retrieval benchmark](https://github.com/embeddings-benchmark/mteb) (10 tasks, top-100 reranked) using MTEB's [two-stage reranking flow](https://embeddings-benchmark.github.io/mteb/get_started/advanced_usage/two_stage_reranking/), pairing each reranker with six embedding models that span the speed/quality spectrum.
	The dashed retriever-only line in each chart below is the headline number to beat. Anything below it means the reranker actively hurts the pipeline on average:

	\| \| \|
	\|-\|-\|
	\| ![MTEB(eng, v2) Retrieval with static-retrieval-mrl-en-v1 + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_static-retrieval-mrl-en-v1.png) \| ![MTEB(eng, v2) Retrieval with all-MiniLM-L6-v2 + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_all-MiniLM-L6-v2.png) \|
	\| ![MTEB(eng, v2) Retrieval with bge-small-en-v1.5 + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_bge-small-en-v1.5.png) \| ![MTEB(eng, v2) Retrieval with nomic-embed-text-v1.5 + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_nomic-embed-text-v1.5.png) \|
	\| ![MTEB(eng, v2) Retrieval with embeddinggemma-300m + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_embeddinggemma-300m.png) \| ![MTEB(eng, v2) Retrieval with jina-embeddings-v5-text-small-retrieval + reranker](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ettin-reranker/mteb_ndcg10_jina-embeddings-v5-text-small-retrieval.png) \|

	<details><summary>Full table of results (click to expand)</summary>

	Mean NDCG@10 over the 6 embedder pairings, sorted by MTEB. The released ettin-reranker-v1 family is in bold, and the teacher [`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) is <u>underlined</u>.

	\| Reranker \| Params \| MTEB(eng, v2) Retrieval NDCG@10 \|
	\| --- \| ---: \| ---: \|
	\| [`Qwen/Qwen3-Reranker-4B`](https://huggingface.co/Qwen/Qwen3-Reranker-4B)<sup>†</sup> \| 4.02B \| 0.6367 \|
	\| <u>[`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2)</u> \| <u>1.54B</u> \| <u>0.6115</u> \|
	\| [`cross-encoder/ettin-reranker-1b-v1`](https://huggingface.co/cross-encoder/ettin-reranker-1b-v1) \| 1.00B \| 0.6114 \|
	\| [`cross-encoder/ettin-reranker-400m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-400m-v1) \| 401M \| 0.6091 \|
	\| [`cross-encoder/ettin-reranker-150m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-150m-v1) \| 151M \| 0.5994 \|
	\| [`Qwen/Qwen3-Reranker-0.6B`](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) \| 596M \| 0.5940 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) \| 494M \| 0.5920 \|
	\| [`cross-encoder/ettin-reranker-68m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-68m-v1) \| 68.6M \| 0.5915 \|
	\| [`jinaai/jina-reranker-m0`](https://huggingface.co/jinaai/jina-reranker-m0) \| 2.44B \| 0.5856 \|
	\| [`Alibaba-NLP/gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) \| 150M \| 0.5843 \|
	\| [`cross-encoder/ettin-reranker-32m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-32m-v1) \| 32.8M \| 0.5779 \|
	\| [`ibm-granite/granite-embedding-reranker-english-r2`](https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2) \| 150M \| 0.5656 \|
	\| [`cross-encoder/ettin-reranker-17m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1) \| 17.6M \| 0.5576 \|
	\| [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| 568M \| 0.5526 \|
	\| [`zeroentropy/zerank-2-reranker`](https://huggingface.co/zeroentropy/zerank-2-reranker)<sup>†</sup> \| 4.02B \| 0.5300 \|
	\| [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) \| 560M \| 0.5098 \|
	\| [`cross-encoder/ms-marco-MiniLM-L6-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) \| 22.7M \| 0.5082 \|
	\| [`cross-encoder/ms-marco-MiniLM-L12-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) \| 33.4M \| 0.5066 \|
	\| [`mixedbread-ai/mxbai-rerank-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) \| 435M \| 0.5063 \|
	\| [`cross-encoder/ms-marco-MiniLM-L4-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L4-v2) \| 19.2M \| 0.4979 \|
	\| [`mixedbread-ai/mxbai-rerank-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) \| 70.8M \| 0.4968 \|
	\| [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) \| 278M \| 0.4890 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) \| 184M \| 0.4865 \|

	<sup>†</sup> Capped to `max_seq_length=8192` (the 4B Qwen3-based rerankers don't fit on a single H100 80GB at native context). Native-context evaluation is likely higher.

	</details>

	See the [release blogpost](https://huggingface.co/blog/ettin-reranker) for the full analysis and per-model commentary.

	### Speed

	All six released models were benchmarked against thirteen public rerankers on three hardware tiers, using [`sentence-transformers/natural-questions`](https://huggingface.co/datasets/sentence-transformers/natural-questions) at `max_length=512` with each model's best supported attention implementation. The full sweep over `fp32+SDPA`, `bf16+SDPA`, padded `bf16+FA2`, and unpadded `bf16+FA2` (showing why the ettin-reranker-v1 family is faster than other ModernBERT-based rerankers) is in the [release blogpost](https://huggingface.co/blog/ettin-reranker#speed). This table shows the throughput in pairs per second on a NVIDIA H100 80GB, all in `bfloat16`:

	\| Model \| Params \| Attn \| pairs / second \|
	\|---\|---:\|---\|---\|
	\| [`cross-encoder/ettin-reranker-17m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1) \| 17M \| FA2 \| 7517 \|
	\| [`cross-encoder/ettin-reranker-32m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-32m-v1) \| 32M \| FA2 \| 6602 \|
	\| [`cross-encoder/ettin-reranker-68m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-68m-v1) \| 68M \| FA2 \| 4913 \|
	\| [`cross-encoder/ms-marco-MiniLM-L4-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L4-v2) \| 19M \| FA2 \| 4029 \|
	\| [`cross-encoder/ms-marco-MiniLM-L6-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) \| 22M \| FA2 \| 3817 \|
	\| [`cross-encoder/ms-marco-MiniLM-L12-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) \| 33M \| FA2 \| 3311 \|
	\| [`cross-encoder/ettin-reranker-150m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-150m-v1) \| 150M \| FA2 \| 3237 \|
	\| [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) \| 278M \| FA2 \| 2858 \|
	\| [`mixedbread-ai/mxbai-rerank-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) \| 70M \| eager \| 2636 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) \| 184M \| eager \| 1953 \|
	\| [`cross-encoder/ettin-reranker-400m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-400m-v1) \| 400M \| FA2 \| 1738 \|
	\| [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) \| 560M \| FA2 \| 1659 \|
	\| [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| 568M \| FA2 \| 1569 \|
	\| [`Alibaba-NLP/gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) \| 150M \| FA2 \| 1418 \|
	\| [`ibm-granite/granite-embedding-reranker-english-r2`](https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2) \| 150M \| FA2 \| 1404 \|
	\| [`cross-encoder/ettin-reranker-1b-v1`](https://huggingface.co/cross-encoder/ettin-reranker-1b-v1) \| 1B \| FA2 \| 928 \|
	\| [`mixedbread-ai/mxbai-rerank-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) \| 435M \| eager \| 867 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) \| 494M \| FA2 \| 809 \|
	\| <u>[`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2)</u> \| <u>1.5B</u> \| FA2 \| <u>387</u> \|

	<details><summary>Same benchmark on a consumer GPU (RTX 3090, 24 GB)</summary>

	\| Model \| Params \| Best attn \| pairs / second \|
	\|---\|---:\|---\|---:\|
	\| [`cross-encoder/ettin-reranker-17m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1) \| 17M \| FA2 \| 9008 \|
	\| [`cross-encoder/ms-marco-MiniLM-L4-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L4-v2) \| 19M \| FA2 \| 5071 \|
	\| [`cross-encoder/ettin-reranker-32m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-32m-v1) \| 32M \| FA2 \| 4497 \|
	\| [`cross-encoder/ms-marco-MiniLM-L6-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) \| 22M \| FA2 \| 4234 \|
	\| [`cross-encoder/ms-marco-MiniLM-L12-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) \| 33M \| FA2 \| 2847 \|
	\| [`cross-encoder/ettin-reranker-68m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-68m-v1) \| 68M \| FA2 \| 1916 \|
	\| [`mixedbread-ai/mxbai-rerank-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) \| 70M \| eager \| 1677 \|
	\| [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) \| 278M \| FA2 \| 1329 \|
	\| [`cross-encoder/ettin-reranker-150m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-150m-v1) \| 150M \| FA2 \| 982 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) \| 184M \| eager \| 772 \|
	\| [`ibm-granite/granite-embedding-reranker-english-r2`](https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2) \| 150M \| FA2 \| 598 \|
	\| [`Alibaba-NLP/gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) \| 150M \| FA2 \| 586 \|
	\| [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) \| 560M \| FA2 \| 448 \|
	\| [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| 568M \| FA2 \| 436 \|
	\| [`cross-encoder/ettin-reranker-400m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-400m-v1) \| 400M \| FA2 \| 429 \|
	\| [`mixedbread-ai/mxbai-rerank-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) \| 435M \| eager \| 266 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) \| 494M \| FA2 \| 221 \|
	\| [`cross-encoder/ettin-reranker-1b-v1`](https://huggingface.co/cross-encoder/ettin-reranker-1b-v1) \| 1B \| FA2 \| 189 \|
	\| <u>[`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2)</u> \| <u>1.5B</u> \| FA2 \| <u>69</u> \|

	</details>

	<details><summary>Same benchmark on CPU (Intel Core i7-13700K)</summary>

	\| Model \| Params \| Best attn \| pairs / second \|
	\|---\|---:\|---\|---:\|
	\| [`cross-encoder/ettin-reranker-17m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1) \| 17M \| SDPA \| 267.4 \|
	\| [`cross-encoder/ms-marco-MiniLM-L4-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L4-v2) \| 19M \| SDPA \| 206.2 \|
	\| [`cross-encoder/ms-marco-MiniLM-L6-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) \| 22M \| SDPA \| 143.9 \|
	\| [`cross-encoder/ettin-reranker-32m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-32m-v1) \| 32M \| SDPA \| 92.5 \|
	\| [`cross-encoder/ms-marco-MiniLM-L12-v2`](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2) \| 33M \| SDPA \| 75.9 \|
	\| [`mixedbread-ai/mxbai-rerank-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) \| 70M \| eager \| 38.9 \|
	\| [`cross-encoder/ettin-reranker-68m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-68m-v1) \| 68M \| SDPA \| 31.2 \|
	\| [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) \| 278M \| SDPA \| 19.2 \|
	\| [`Alibaba-NLP/gte-reranker-modernbert-base`](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) \| 150M \| SDPA \| 14.7 \|
	\| [`ibm-granite/granite-embedding-reranker-english-r2`](https://huggingface.co/ibm-granite/granite-embedding-reranker-english-r2) \| 150M \| SDPA \| 14.5 \|
	\| [`cross-encoder/ettin-reranker-150m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-150m-v1) \| 150M \| SDPA \| 14.0 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) \| 184M \| eager \| 13.4 \|
	\| [`BAAI/bge-reranker-large`](https://huggingface.co/BAAI/bge-reranker-large) \| 560M \| SDPA \| 6.2 \|
	\| [`BAAI/bge-reranker-v2-m3`](https://huggingface.co/BAAI/bge-reranker-v2-m3) \| 568M \| SDPA \| 6.0 \|
	\| [`cross-encoder/ettin-reranker-400m-v1`](https://huggingface.co/cross-encoder/ettin-reranker-400m-v1) \| 400M \| SDPA \| 5.2 \|
	\| [`mixedbread-ai/mxbai-rerank-large-v1`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) \| 435M \| eager \| 4.3 \|
	\| [`mixedbread-ai/mxbai-rerank-base-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) \| 494M \| SDPA \| 3.5 \|
	\| [`cross-encoder/ettin-reranker-1b-v1`](https://huggingface.co/cross-encoder/ettin-reranker-1b-v1) \| 1B \| SDPA \| 2.1 \|

	</details>


	### Metrics

	#### Cross Encoder Reranking

	* Datasets: `NanoMSMARCO_R100`, `NanoNFCorpus_R100`, `NanoNQ_R100`, `NanoFiQA2018_R100`, `NanoTouche2020_R100`, `NanoSciFact_R100`, `NanoHotpotQA_R100`, `NanoArguAna_R100`, `NanoFEVER_R100`, `NanoDBPedia_R100`, `NanoClimateFEVER_R100`, `NanoSCIDOCS_R100` and `NanoQuoraRetrieval_R100`
	* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
	```json
	{
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| NanoMSMARCO_R100 \| NanoNFCorpus_R100 \| NanoNQ_R100 \| NanoFiQA2018_R100 \| NanoTouche2020_R100 \| NanoSciFact_R100 \| NanoHotpotQA_R100 \| NanoArguAna_R100 \| NanoFEVER_R100 \| NanoDBPedia_R100 \| NanoClimateFEVER_R100 \| NanoSCIDOCS_R100 \| NanoQuoraRetrieval_R100 \|
	\|:------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:---------------------\|:----------------------\|:---------------------\|:------------------------\|
	\| map \| 0.6425 (+0.1529) \| 0.3657 (+0.1047) \| 0.7484 (+0.3288) \| 0.5630 (+0.1979) \| 0.4720 (-0.0779) \| 0.7160 (+0.0463) \| 0.9307 (+0.1624) \| 0.6597 (+0.2491) \| 0.9436 (+0.1717) \| 0.6790 (+0.1671) \| 0.4903 (+0.2500) \| 0.3227 (+0.0484) \| 0.9608 (+0.1299) \|
	\| mrr@10 \| 0.6459 (+0.1684) \| 0.5762 (+0.0764) \| 0.7678 (+0.3411) \| 0.6733 (+0.1825) \| 0.8155 (-0.0916) \| 0.7130 (+0.0350) \| 1.0000 (+0.0771) \| 0.6579 (+0.2648) \| 0.9750 (+0.1950) \| 0.8939 (+0.0932) \| 0.7496 (+0.3457) \| 0.5548 (-0.0047) \| 0.9800 (+0.1118) \|
	\| ndcg@10 \| 0.7243 (+0.1839) \| 0.4067 (+0.0816) \| 0.7992 (+0.2986) \| 0.6127 (+0.1753) \| 0.5518 (-0.1420) \| 0.7613 (+0.0514) \| 0.9573 (+0.1296) \| 0.7375 (+0.2486) \| 0.9600 (+0.1506) \| 0.7494 (+0.1350) \| 0.5722 (+0.2544) \| 0.3742 (+0.0391) \| 0.9753 (+0.1067) \|

	#### Cross Encoder Nano BEIR

	* Dataset: `NanoBEIR_R100_mean`
	* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
	```json
	{
	"dataset_names": [
	"msmarco",
	"nfcorpus",
	"nq",
	"fiqa2018",
	"touche2020",
	"scifact",
	"hotpotqa",
	"arguana",
	"fever",
	"dbpedia",
	"climatefever",
	"scidocs",
	"quoraretrieval"
	],
	"dataset_id": "sentence-transformers/NanoBEIR-en",
	"rerank_k": 100,
	"at_k": 10,
	"always_rerank_positives": true
	}
	```

	\| Metric \| Value \|
	\|:------------\|:---------------------\|
	\| map \| 0.6534 (+0.1486) \|
	\| mrr@10 \| 0.7695 (+0.1381) \|
	\| ndcg@10 \| 0.7063 (+0.1318) \|

	> [!NOTE]
	> The [release blogpost](https://huggingface.co/blog/ettin-reranker) quotes a slightly higher NanoBEIR mean NDCG@10 of `0.7086` for this model, computed in `fp32` rather than the `bfloat16` used by the training-time evaluation above. Both numbers are valid.

	<!--
	## 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

	#### ettin-reranker-v1-data

	* Dataset: [cross-encoder/ettin-reranker-v1-data](https://huggingface.co/datasets/cross-encoder/ettin-reranker-v1-data)
	* Size: 143,393,475 training samples
	* Columns: <code>query</code>, <code>document</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| document \| label \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 26 characters</li><li>mean: 55.52 characters</li><li>max: 249 characters</li></ul> \| <ul><li>min: 63 characters</li><li>mean: 659.91 characters</li><li>max: 3975 characters</li></ul> \| <ul><li>min: -2.94</li><li>mean: 8.51</li><li>max: 13.88</li></ul> \|
	* Samples:
	\| query \| document \| label \|
	\|:----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Help me with my Reborn performance</code> \| <code>I was reading the comment section for Dotacinema's world of dota video, and a bunch of people were complaining how there were a lot of bugs and some talked about PERFORMANCE ISSUES. But there were also people saying that reborn has actually IMPROVED their gameplay?<br><br><br>I am one of those people who is running into performance issues and would desperately like to know how some are getting BETTER performance while others like me are getting worse. I'm not complaining about bugs, I'm complaing about framerate, I use to get 60 fps solid in source 1 but I now have 40 or at worst 30 fps in source 2.<br>I have an i3 processor/gtx560ti/16gb RAM<br><br>i dont think it's a potato pc, so I dont know what's happening, I cleaned my computer recently so dust isnt affecting anything in anyway.<br>So if you gained or had IMPROVED performance in source 2 please list the settings you are enabling, so I can see where I am at fault. (v sync is off btw)<br><br>TLDR: Have bad performance now from source 2, if you have good p...</code> \| <code>9.5</code> \|
	\| <code>Really wanna try out the game and expansion, ~$60 is hefty. Likelihood of sales?</code> \| <code>As per title, steam sells the game and its expansions for $60 total. Heavy price to drop. Are there sales on any other website? This game looks fantastic to immerse in otherwise and I'm pleased that this subreddit has at least some attention to help out new folks!</code> \| <code>9.25</code> \|
	\| <code>Your Avatar. [MGSV Spoilers]</code> \| <code>Was anyone else suprised he actually replaces the snake model in some cutscenes. I've only tried the first Quiet cutscenes, i was just amazed I haven't seen anybody else say this yet.<br>Sorry if repost.</code> \| <code>5.25</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#mseloss) with these parameters:
	```json
	{
	"activation_fn": "torch.nn.modules.linear.Identity"
	}
	```

	### Evaluation Dataset

	#### ettin-reranker-v1-data

	* Dataset: [cross-encoder/ettin-reranker-v1-data](https://huggingface.co/datasets/cross-encoder/ettin-reranker-v1-data)
	* Size: 5,000 evaluation samples
	* Columns: <code>query</code>, <code>document</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| document \| label \|
	\|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 14 characters</li><li>mean: 52.62 characters</li><li>max: 168 characters</li></ul> \| <ul><li>min: 11 characters</li><li>mean: 50.12 characters</li><li>max: 184 characters</li></ul> \| <ul><li>min: 4.44</li><li>mean: 13.49</li><li>max: 18.62</li></ul> \|
	* Samples:
	\| query \| document \| label \|
	\|:------------------------------------------------------------------\|:---------------------------------------------------------------------------------------\|:---------------------\|
	\| <code>Why do we need binomial distribution?</code> \| <code>Why is the binomial distribution important?</code> \| <code>11.375</code> \|
	\| <code>I already have Windows 10, can I delete Windows.old?</code> \| <code>After resetting windows 10, can I safely delete the "old windows" folder?</code> \| <code>10.875</code> \|
	\| <code>How can guys last longer during sex?</code> \| <code>How do men last longer in bed?</code> \| <code>10.8125</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#mseloss) with these parameters:
	```json
	{
	"activation_fn": "torch.nn.modules.linear.Identity"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 3
	- `num_train_epochs`: 1
	- `learning_rate`: 1.5e-05
	- `warmup_steps`: 0.03
	- `bf16`: True
	- `per_device_eval_batch_size`: 3
	- `load_best_model_at_end`: True
	- `seed`: 12

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `per_device_train_batch_size`: 3
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `learning_rate`: 1.5e-05
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: None
	- `warmup_steps`: 0.03
	- `optim`: adamw_torch
	- `optim_args`: None
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `optim_target_modules`: None
	- `gradient_accumulation_steps`: 1
	- `average_tokens_across_devices`: True
	- `max_grad_norm`: 1.0
	- `label_smoothing_factor`: 0.0
	- `bf16`: True
	- `fp16`: False
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `use_liger_kernel`: False
	- `liger_kernel_config`: None
	- `use_cache`: False
	- `neftune_noise_alpha`: None
	- `torch_empty_cache_steps`: None
	- `auto_find_batch_size`: False
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `include_num_input_tokens_seen`: no
	- `log_level`: passive
	- `log_level_replica`: warning
	- `disable_tqdm`: False
	- `project`: huggingface
	- `trackio_space_id`: None
	- `trackio_bucket_id`: None
	- `trackio_static_space_id`: None
	- `per_device_eval_batch_size`: 3
	- `prediction_loss_only`: True
	- `eval_on_start`: False
	- `eval_do_concat_batches`: True
	- `eval_use_gather_object`: False
	- `eval_accumulation_steps`: None
	- `include_for_metrics`: []
	- `batch_eval_metrics`: False
	- `save_only_model`: False
	- `save_on_each_node`: False
	- `enable_jit_checkpoint`: False
	- `push_to_hub`: False
	- `hub_private_repo`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_always_push`: False
	- `hub_revision`: None
	- `load_best_model_at_end`: True
	- `ignore_data_skip`: False
	- `restore_callback_states_from_checkpoint`: False
	- `full_determinism`: False
	- `seed`: 12
	- `data_seed`: None
	- `use_cpu`: False
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `parallelism_config`: None
	- `dataloader_drop_last`: True
	- `dataloader_num_workers`: 0
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `dataloader_prefetch_factor`: None
	- `remove_unused_columns`: True
	- `label_names`: None
	- `train_sampling_strategy`: random
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `ddp_static_graph`: None
	- `ddp_backend`: None
	- `ddp_timeout`: 1800
	- `fsdp`: []
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `deepspeed`: None
	- `debug`: []
	- `skip_memory_metrics`: True
	- `do_predict`: False
	- `resume_from_checkpoint`: None
	- `warmup_ratio`: None
	- `local_rank`: -1
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_R100_ndcg@10 \| NanoNFCorpus_R100_ndcg@10 \| NanoNQ_R100_ndcg@10 \| NanoFiQA2018_R100_ndcg@10 \| NanoTouche2020_R100_ndcg@10 \| NanoSciFact_R100_ndcg@10 \| NanoHotpotQA_R100_ndcg@10 \| NanoArguAna_R100_ndcg@10 \| NanoFEVER_R100_ndcg@10 \| NanoDBPedia_R100_ndcg@10 \| NanoClimateFEVER_R100_ndcg@10 \| NanoSCIDOCS_R100_ndcg@10 \| NanoQuoraRetrieval_R100_ndcg@10 \| NanoBEIR_R100_mean_ndcg@10 \|
	\|:-------:\|:----------:\|:-------------:\|:---------------:\|:------------------------:\|:-------------------------:\|:--------------------:\|:-------------------------:\|:---------------------------:\|:------------------------:\|:-------------------------:\|:------------------------:\|:----------------------:\|:------------------------:\|:-----------------------------:\|:------------------------:\|:-------------------------------:\|:--------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.0539 (-0.4865) \| 0.2325 (-0.0925) \| 0.0386 (-0.4620) \| 0.0694 (-0.3680) \| 0.1889 (-0.5049) \| 0.0380 (-0.6719) \| 0.0553 (-0.7724) \| 0.0977 (-0.3912) \| 0.0579 (-0.7515) \| 0.1942 (-0.4202) \| 0.1156 (-0.2021) \| 0.0984 (-0.2367) \| 0.0298 (-0.8389) \| 0.0977 (-0.4768) \|
	\| 0.0000 \| 1 \| 80.2007 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0250 \| 18672 \| 3.6973 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0500 \| 37343 \| - \| 0.7152 \| 0.7114 (+0.1710) \| 0.4466 (+0.1216) \| 0.7887 (+0.2881) \| 0.5918 (+0.1544) \| 0.5677 (-0.1261) \| 0.7818 (+0.0719) \| 0.9502 (+0.1225) \| 0.6812 (+0.1924) \| 0.9211 (+0.1117) \| 0.7203 (+0.1059) \| 0.5429 (+0.2252) \| 0.3965 (+0.0613) \| 0.9665 (+0.0978) \| 0.6974 (+0.1229) \|
	\| 0.0500 \| 37344 \| 1.1915 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0750 \| 56016 \| 1.0289 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1000 \| 74686 \| - \| 0.6249 \| 0.7006 (+0.1602) \| 0.4254 (+0.1003) \| 0.8031 (+0.3025) \| 0.5778 (+0.1403) \| 0.5672 (-0.1266) \| 0.7543 (+0.0444) \| 0.9452 (+0.1175) \| 0.6880 (+0.1992) \| 0.9356 (+0.1262) \| 0.7300 (+0.1156) \| 0.5544 (+0.2367) \| 0.3941 (+0.0590) \| 0.9590 (+0.0903) \| 0.6950 (+0.1204) \|
	\| 0.1000 \| 74688 \| 0.9532 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1250 \| 93360 \| 0.9019 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1500 \| 112029 \| - \| 0.5773 \| 0.7342 (+0.1937) \| 0.4191 (+0.0940) \| 0.8085 (+0.3078) \| 0.6031 (+0.1656) \| 0.5585 (-0.1353) \| 0.7760 (+0.0661) \| 0.9560 (+0.1283) \| 0.6932 (+0.2044) \| 0.9256 (+0.1162) \| 0.7471 (+0.1327) \| 0.5626 (+0.2449) \| 0.4136 (+0.0785) \| 0.9721 (+0.1034) \| 0.7053 (+0.1308) \|
	\| 0.1500 \| 112032 \| 0.8659 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1750 \| 130704 \| 0.8385 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2000 \| 149372 \| - \| 0.5116 \| 0.7199 (+0.1795) \| 0.4203 (+0.0952) \| 0.8069 (+0.3063) \| 0.6035 (+0.1660) \| 0.5615 (-0.1323) \| 0.7626 (+0.0527) \| 0.9547 (+0.1270) \| 0.7039 (+0.2151) \| 0.9336 (+0.1242) \| 0.7392 (+0.1248) \| 0.5489 (+0.2312) \| 0.3869 (+0.0518) \| 0.9648 (+0.0961) \| 0.7005 (+0.1260) \|
	\| 0.2000 \| 149376 \| 0.8144 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2250 \| 168048 \| 0.7916 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2500 \| 186715 \| - \| 0.5133 \| 0.7189 (+0.1785) \| 0.4117 (+0.0867) \| 0.8097 (+0.3090) \| 0.6114 (+0.1740) \| 0.5689 (-0.1249) \| 0.7531 (+0.0432) \| 0.9564 (+0.1287) \| 0.7079 (+0.2191) \| 0.9362 (+0.1268) \| 0.7450 (+0.1306) \| 0.5496 (+0.2319) \| 0.3797 (+0.0446) \| 0.9712 (+0.1026) \| 0.7015 (+0.1270) \|
	\| 0.2500 \| 186720 \| 0.7749 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2750 \| 205392 \| 0.7581 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3000 \| 224058 \| - \| 0.4803 \| 0.7353 (+0.1949) \| 0.4200 (+0.0950) \| 0.8052 (+0.3046) \| 0.6247 (+0.1873) \| 0.5554 (-0.1384) \| 0.7505 (+0.0406) \| 0.9575 (+0.1298) \| 0.7488 (+0.2599) \| 0.9432 (+0.1338) \| 0.7423 (+0.1279) \| 0.5628 (+0.2451) \| 0.3866 (+0.0515) \| 0.9681 (+0.0994) \| 0.7077 (+0.1332) \|
	\| 0.3000 \| 224064 \| 0.7453 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3250 \| 242736 \| 0.7322 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3500 \| 261401 \| - \| 0.4723 \| 0.7304 (+0.1899) \| 0.4056 (+0.0806) \| 0.8163 (+0.3156) \| 0.6131 (+0.1756) \| 0.5591 (-0.1347) \| 0.7655 (+0.0556) \| 0.9565 (+0.1288) \| 0.7243 (+0.2355) \| 0.9402 (+0.1307) \| 0.7403 (+0.1259) \| 0.5710 (+0.2532) \| 0.3856 (+0.0505) \| 0.9784 (+0.1097) \| 0.7066 (+0.1321) \|
	\| 0.3500 \| 261408 \| 0.7194 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3750 \| 280080 \| 0.7100 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4000 \| 298744 \| - \| 0.4721 \| 0.7425 (+0.2021) \| 0.4233 (+0.0982) \| 0.8043 (+0.3036) \| 0.6180 (+0.1806) \| 0.5520 (-0.1418) \| 0.7624 (+0.0525) \| 0.9543 (+0.1266) \| 0.7327 (+0.2439) \| 0.9439 (+0.1345) \| 0.7449 (+0.1305) \| 0.5753 (+0.2575) \| 0.3743 (+0.0392) \| 0.9799 (+0.1112) \| 0.7083 (+0.1337) \|
	\| 0.4000 \| 298752 \| 0.6993 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4250 \| 317424 \| 0.6884 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4500 \| 336087 \| - \| 0.4672 \| 0.7290 (+0.1885) \| 0.4065 (+0.0814) \| 0.8180 (+0.3174) \| 0.6193 (+0.1818) \| 0.5577 (-0.1361) \| 0.7496 (+0.0397) \| 0.9541 (+0.1264) \| 0.7372 (+0.2483) \| 0.9396 (+0.1302) \| 0.7394 (+0.1251) \| 0.5767 (+0.2590) \| 0.3795 (+0.0444) \| 0.9744 (+0.1058) \| 0.7062 (+0.1317) \|
	\| 0.4500 \| 336096 \| 0.6803 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4750 \| 354768 \| 0.6728 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5 \| 373430 \| - \| 0.4439 \| 0.7299 (+0.1895) \| 0.4344 (+0.1093) \| 0.8089 (+0.3083) \| 0.6072 (+0.1698) \| 0.5556 (-0.1382) \| 0.7586 (+0.0487) \| 0.9576 (+0.1299) \| 0.7345 (+0.2457) \| 0.9479 (+0.1385) \| 0.7403 (+0.1259) \| 0.5763 (+0.2585) \| 0.3834 (+0.0483) \| 0.9771 (+0.1085) \| 0.7086 (+0.1340) \|
	\| 0.5000 \| 373440 \| 0.6645 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5250 \| 392112 \| 0.6581 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5500 \| 410773 \| - \| 0.4483 \| 0.7301 (+0.1897) \| 0.4158 (+0.0908) \| 0.8135 (+0.3129) \| 0.6090 (+0.1716) \| 0.5470 (-0.1468) \| 0.7565 (+0.0466) \| 0.9588 (+0.1310) \| 0.7263 (+0.2375) \| 0.9521 (+0.1427) \| 0.7406 (+0.1263) \| 0.5677 (+0.2500) \| 0.3788 (+0.0437) \| 0.9842 (+0.1155) \| 0.7062 (+0.1317) \|
	\| 0.5500 \| 410784 \| 0.6520 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5750 \| 429456 \| 0.6443 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6000 \| 448116 \| - \| 0.4325 \| 0.7250 (+0.1846) \| 0.4188 (+0.0938) \| 0.8112 (+0.3106) \| 0.6165 (+0.1791) \| 0.5513 (-0.1425) \| 0.7573 (+0.0474) \| 0.9626 (+0.1349) \| 0.7358 (+0.2470) \| 0.9521 (+0.1427) \| 0.7326 (+0.1183) \| 0.5645 (+0.2468) \| 0.3679 (+0.0328) \| 0.9764 (+0.1077) \| 0.7056 (+0.1310) \|
	\| 0.6000 \| 448128 \| 0.6397 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6250 \| 466800 \| 0.6329 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6500 \| 485459 \| - \| 0.4353 \| 0.7331 (+0.1927) \| 0.4078 (+0.0827) \| 0.8045 (+0.3038) \| 0.6057 (+0.1683) \| 0.5525 (-0.1413) \| 0.7534 (+0.0435) \| 0.9540 (+0.1262) \| 0.7331 (+0.2443) \| 0.9546 (+0.1451) \| 0.7392 (+0.1248) \| 0.5750 (+0.2572) \| 0.3680 (+0.0329) \| 0.9790 (+0.1103) \| 0.7046 (+0.1300) \|
	\| 0.6500 \| 485472 \| 0.6294 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6750 \| 504144 \| 0.6252 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7000 \| 522802 \| - \| 0.4124 \| 0.7238 (+0.1833) \| 0.4095 (+0.0845) \| 0.8082 (+0.3076) \| 0.6077 (+0.1703) \| 0.5545 (-0.1393) \| 0.7544 (+0.0445) \| 0.9604 (+0.1326) \| 0.7337 (+0.2449) \| 0.9505 (+0.1411) \| 0.7412 (+0.1268) \| 0.5784 (+0.2607) \| 0.3708 (+0.0357) \| 0.9734 (+0.1047) \| 0.7051 (+0.1306) \|
	\| 0.7000 \| 522816 \| 0.6185 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7250 \| 541488 \| 0.6151 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7500 \| 560145 \| - \| 0.4054 \| 0.7264 (+0.1860) \| 0.4045 (+0.0795) \| 0.8081 (+0.3074) \| 0.6120 (+0.1746) \| 0.5503 (-0.1435) \| 0.7650 (+0.0551) \| 0.9603 (+0.1326) \| 0.7224 (+0.2336) \| 0.9572 (+0.1478) \| 0.7330 (+0.1187) \| 0.5690 (+0.2513) \| 0.3714 (+0.0363) \| 0.9760 (+0.1073) \| 0.7043 (+0.1297) \|
	\| 0.7500 \| 560160 \| 0.6109 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7750 \| 578832 \| 0.6077 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8000 \| 597488 \| - \| 0.3973 \| 0.7206 (+0.1802) \| 0.4082 (+0.0832) \| 0.8000 (+0.2994) \| 0.6085 (+0.1711) \| 0.5477 (-0.1461) \| 0.7540 (+0.0441) \| 0.9551 (+0.1274) \| 0.7464 (+0.2576) \| 0.9567 (+0.1472) \| 0.7356 (+0.1212) \| 0.5760 (+0.2583) \| 0.3770 (+0.0418) \| 0.9814 (+0.1127) \| 0.7052 (+0.1306) \|
	\| 0.8000 \| 597504 \| 0.6042 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8250 \| 616176 \| 0.5991 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8500 \| 634831 \| - \| 0.3948 \| 0.7159 (+0.1755) \| 0.4067 (+0.0816) \| 0.7989 (+0.2982) \| 0.6101 (+0.1726) \| 0.5469 (-0.1469) \| 0.7581 (+0.0482) \| 0.9574 (+0.1297) \| 0.7368 (+0.2480) \| 0.9523 (+0.1429) \| 0.7409 (+0.1265) \| 0.5749 (+0.2571) \| 0.3653 (+0.0302) \| 0.9787 (+0.1100) \| 0.7033 (+0.1287) \|
	\| 0.8500 \| 634848 \| 0.5962 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8750 \| 653520 \| 0.5937 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9000 \| 672174 \| - \| 0.3986 \| 0.7181 (+0.1777) \| 0.4080 (+0.0829) \| 0.8053 (+0.3047) \| 0.6106 (+0.1732) \| 0.5596 (-0.1342) \| 0.7488 (+0.0389) \| 0.9605 (+0.1328) \| 0.7402 (+0.2514) \| 0.9600 (+0.1506) \| 0.7452 (+0.1308) \| 0.5720 (+0.2543) \| 0.3687 (+0.0335) \| 0.9758 (+0.1071) \| 0.7056 (+0.1311) \|
	\| 0.9000 \| 672192 \| 0.5905 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9250 \| 690864 \| 0.5886 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9500 \| 709517 \| - \| 0.3992 \| 0.7285 (+0.1881) \| 0.4110 (+0.0860) \| 0.8043 (+0.3036) \| 0.6119 (+0.1744) \| 0.5564 (-0.1375) \| 0.7623 (+0.0524) \| 0.9612 (+0.1335) \| 0.7434 (+0.2546) \| 0.9567 (+0.1472) \| 0.7503 (+0.1360) \| 0.5736 (+0.2559) \| 0.3715 (+0.0363) \| 0.9758 (+0.1071) \| 0.7082 (+0.1337) \|
	\| 0.9500 \| 709536 \| 0.5856 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9751 \| 728208 \| 0.5863 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0 \| 746841 \| - \| 0.3898 \| 0.7243 (+0.1839) \| 0.4067 (+0.0816) \| 0.7992 (+0.2986) \| 0.6127 (+0.1753) \| 0.5518 (-0.1420) \| 0.7613 (+0.0514) \| 0.9573 (+0.1296) \| 0.7375 (+0.2486) \| 0.9600 (+0.1506) \| 0.7494 (+0.1350) \| 0.5722 (+0.2544) \| 0.3742 (+0.0391) \| 0.9753 (+0.1067) \| 0.7063 (+0.1318) \|

	* The bold row denotes the saved checkpoint.

	### Training Time
	- Training: 18.5 hours
	- Evaluation: 14.5 minutes
	- Total: 18.7 hours

	### Framework Versions
	- Python: 3.11.15
	- Sentence Transformers: 5.4.1
	- Transformers: 5.7.0
	- PyTorch: 2.7.0+cu126
	- Accelerate: 1.13.0
	- Datasets: 4.8.5
	- Tokenizers: 0.22.2

	## Citation

	### BibTeX

	#### Ettin Reranker Blogpost
	```bibtex
	@misc{aarsen2026ettin-reranker,
	title = "Introducing the Ettin Reranker Family",
	author = "Aarsen, Tom",
	year = "2026",
	publisher = "Hugging Face",
	url = "https://huggingface.co/blog/ettin-reranker",
	}
	```

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->