--- 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-1b pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: ettin-reranker-1b-v1 results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoMSMARCO R100 type: NanoMSMARCO_R100 metrics: - type: map value: 0.6111 name: Map - type: mrr@10 value: 0.6142 name: Mrr@10 - type: ndcg@10 value: 0.6978 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNFCorpus R100 type: NanoNFCorpus_R100 metrics: - type: map value: 0.3971 name: Map - type: mrr@10 value: 0.6391 name: Mrr@10 - type: ndcg@10 value: 0.4485 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoNQ R100 type: NanoNQ_R100 metrics: - type: map value: 0.7361 name: Map - type: mrr@10 value: 0.7547 name: Mrr@10 - type: ndcg@10 value: 0.798 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoFiQA2018 R100 type: NanoFiQA2018_R100 metrics: - type: map value: 0.5993 name: Map - type: mrr@10 value: 0.6997 name: Mrr@10 - type: ndcg@10 value: 0.6605 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoTouche2020 R100 type: NanoTouche2020_R100 metrics: - type: map value: 0.4946 name: Map - type: mrr@10 value: 0.8275 name: Mrr@10 - type: ndcg@10 value: 0.584 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoSciFact R100 type: NanoSciFact_R100 metrics: - type: map value: 0.7337 name: Map - type: mrr@10 value: 0.7406 name: Mrr@10 - type: ndcg@10 value: 0.7775 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoHotpotQA R100 type: NanoHotpotQA_R100 metrics: - type: map value: 0.9295 name: Map - type: mrr@10 value: 0.98 name: Mrr@10 - type: ndcg@10 value: 0.9515 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoArguAna R100 type: NanoArguAna_R100 metrics: - type: map value: 0.6853 name: Map - type: mrr@10 value: 0.6985 name: Mrr@10 - type: ndcg@10 value: 0.7637 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoFEVER R100 type: NanoFEVER_R100 metrics: - type: map value: 0.9432 name: Map - type: mrr@10 value: 0.98 name: Mrr@10 - type: ndcg@10 value: 0.9607 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoDBPedia R100 type: NanoDBPedia_R100 metrics: - type: map value: 0.6963 name: Map - type: mrr@10 value: 0.8822 name: Mrr@10 - type: ndcg@10 value: 0.7631 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoClimateFEVER R100 type: NanoClimateFEVER_R100 metrics: - type: map value: 0.4873 name: Map - type: mrr@10 value: 0.7342 name: Mrr@10 - type: ndcg@10 value: 0.574 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoSCIDOCS R100 type: NanoSCIDOCS_R100 metrics: - type: map value: 0.3412 name: Map - type: mrr@10 value: 0.5922 name: Mrr@10 - type: ndcg@10 value: 0.3991 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: NanoQuoraRetrieval R100 type: NanoQuoraRetrieval_R100 metrics: - type: map value: 0.9509 name: Map - type: mrr@10 value: 0.9733 name: Mrr@10 - type: ndcg@10 value: 0.9668 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: 0.662 name: Map - type: mrr@10 value: 0.7782 name: Mrr@10 - type: ndcg@10 value: 0.7189 name: Ndcg@10 --- # ettin-reranker-1b-v1 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [jhu-clsp/ettin-encoder-1b](https://huggingface.co/jhu-clsp/ettin-encoder-1b) 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-1b](https://huggingface.co/jhu-clsp/ettin-encoder-1b) - **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': 1792, 'pooling_mode': 'cls', 'include_prompt': True}) (2): Dense({'in_features': 1792, 'out_features': 1792, 'bias': False, 'activation_function': 'torch.nn.modules.activation.GELU', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'}) (3): LayerNorm({'dimension': 1792}) (4): Dense({'in_features': 1792, '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-1b-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) # [ 2.984375 11.9375 5.71875 9.625 ] # Or rank passages by relevance to a single query ranked = model.rank(query, passages) print(ranked) # [{'corpus_id': 1, 'score': np.float32(11.9375)}, ...] ``` ## 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) |
Full table of results (click to expand) 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 underlined. | Reranker | Params | MTEB(eng, v2) Retrieval NDCG@10 | | --- | ---: | ---: | | [`Qwen/Qwen3-Reranker-4B`](https://huggingface.co/Qwen/Qwen3-Reranker-4B)† | 4.02B | 0.6367 | | [`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) | 1.54B | 0.6115 | | **[`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)† | 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 | † 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.
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 | | [`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) | 1.5B | FA2 | 387 |
Same benchmark on a consumer GPU (RTX 3090, 24 GB) | 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** | | [`mixedbread-ai/mxbai-rerank-large-v2`](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) | 1.5B | FA2 | 69 |
Same benchmark on CPU (Intel Core i7-13700K) | 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** |
### 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 [CrossEncoderRerankingEvaluator](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 | 0.6111 (+0.1215) | 0.3971 (+0.1362) | 0.7361 (+0.3165) | 0.5993 (+0.2342) | 0.4946 (-0.0553) | 0.7337 (+0.0640) | 0.9295 (+0.1612) | 0.6853 (+0.2747) | 0.9432 (+0.1713) | 0.6963 (+0.1844) | 0.4873 (+0.2470) | 0.3412 (+0.0669) | 0.9509 (+0.1201) | | mrr@10 | 0.6142 (+0.1367) | 0.6391 (+0.1393) | 0.7547 (+0.3280) | 0.6997 (+0.2089) | 0.8275 (-0.0796) | 0.7406 (+0.0625) | 0.9800 (+0.0571) | 0.6985 (+0.3054) | 0.9800 (+0.2000) | 0.8822 (+0.0816) | 0.7342 (+0.3303) | 0.5922 (+0.0327) | 0.9733 (+0.1052) | | **ndcg@10** | **0.6978 (+0.1574)** | **0.4485 (+0.1235)** | **0.7980 (+0.2974)** | **0.6605 (+0.2231)** | **0.5840 (-0.1098)** | **0.7775 (+0.0676)** | **0.9515 (+0.1237)** | **0.7637 (+0.2749)** | **0.9607 (+0.1513)** | **0.7631 (+0.1487)** | **0.5740 (+0.2563)** | **0.3991 (+0.0639)** | **0.9668 (+0.0981)** | #### Cross Encoder Nano BEIR * Dataset: `NanoBEIR_R100_mean` * Evaluated with [CrossEncoderNanoBEIREvaluator](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 | 0.6620 (+0.1571) | | mrr@10 | 0.7782 (+0.1468) | | **ndcg@10** | **0.7189 (+0.1443)** | > [!NOTE] > The [release blogpost](https://huggingface.co/blog/ettin-reranker) quotes a slightly higher NanoBEIR mean NDCG@10 of `0.7237` for this model, computed in `fp32` rather than the `bfloat16` used by the training-time evaluation above. Both numbers are valid. ## 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: query, document, and label * Approximate statistics based on the first 1000 samples: | | query | document | label | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | document | label | |:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------| | Help me with my Reborn performance | 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?


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.
I have an i3 processor/gtx560ti/16gb RAM

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.
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)

TLDR: Have bad performance now from source 2, if you have good p... | 9.5 | | Really wanna try out the game and expansion, ~$60 is hefty. Likelihood of sales? | 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! | 9.25 | | Your Avatar. [MGSV Spoilers] | 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.
Sorry if repost. | 5.25 | * Loss: [MSELoss](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: query, document, and label * Approximate statistics based on the first 1000 samples: | | query | document | label | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | document | label | |:------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:---------------------| | Why do we need binomial distribution? | Why is the binomial distribution important? | 11.375 | | I already have Windows 10, can I delete Windows.old? | After resetting windows 10, can I safely delete the "old windows" folder? | 10.875 | | How can guys last longer during sex? | How do men last longer in bed? | 10.8125 | * Loss: [MSELoss](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`: 4 - `num_train_epochs`: 1 - `learning_rate`: 3e-06 - `warmup_steps`: 0.03 - `bf16`: True - `per_device_eval_batch_size`: 4 - `load_best_model_at_end`: True - `seed`: 12 #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 4 - `num_train_epochs`: 1 - `max_steps`: -1 - `learning_rate`: 3e-06 - `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`: 4 - `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`: {}
### 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 | |:-------:|:----------:|:-------------:|:---------------:|:------------------------:|:-------------------------:|:--------------------:|:-------------------------:|:---------------------------:|:------------------------:|:-------------------------:|:------------------------:|:----------------------:|:------------------------:|:-----------------------------:|:------------------------:|:-------------------------------:|:--------------------------:| | 0.0000 | 1 | 76.3278 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0250 | 7002 | 4.8750 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0500 | 14004 | 0.9298 | 0.5204 | 0.7122 (+0.1718) | 0.4922 (+0.1672) | 0.7952 (+0.2945) | 0.6233 (+0.1859) | 0.6020 (-0.0918) | 0.7809 (+0.0710) | 0.9575 (+0.1298) | 0.6976 (+0.2087) | 0.9453 (+0.1358) | 0.7601 (+0.1458) | 0.5736 (+0.2559) | 0.4162 (+0.0811) | 0.9620 (+0.0933) | 0.7168 (+0.1422) | | 0.0750 | 21006 | 0.7986 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1000 | 28008 | 0.7383 | 0.4539 | 0.7105 (+0.1701) | 0.4824 (+0.1574) | 0.7999 (+0.2993) | 0.6212 (+0.1838) | 0.5942 (-0.0996) | 0.7817 (+0.0718) | 0.9553 (+0.1275) | 0.7018 (+0.2130) | 0.9490 (+0.1396) | 0.7640 (+0.1496) | 0.5735 (+0.2558) | 0.4241 (+0.0889) | 0.9688 (+0.1001) | 0.7174 (+0.1429) | | 0.1250 | 35010 | 0.6972 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1500 | 42012 | 0.6661 | 0.4238 | 0.7250 (+0.1845) | 0.4786 (+0.1535) | 0.8072 (+0.3065) | 0.6234 (+0.1860) | 0.5803 (-0.1135) | 0.7793 (+0.0694) | 0.9511 (+0.1234) | 0.7110 (+0.2222) | 0.9493 (+0.1398) | 0.7668 (+0.1524) | 0.5723 (+0.2546) | 0.4183 (+0.0832) | 0.9673 (+0.0986) | 0.7177 (+0.1431) | | 0.1750 | 49014 | 0.6429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2000 | 56016 | 0.6225 | 0.3714 | 0.7289 (+0.1885) | 0.4862 (+0.1611) | 0.8099 (+0.3093) | 0.6304 (+0.1929) | 0.5761 (-0.1177) | 0.7852 (+0.0753) | 0.9538 (+0.1261) | 0.7170 (+0.2282) | 0.9457 (+0.1363) | 0.7725 (+0.1581) | 0.5751 (+0.2573) | 0.4089 (+0.0737) | 0.9654 (+0.0967) | 0.7196 (+0.1451) | | 0.2250 | 63018 | 0.6043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2500 | 70020 | 0.5909 | 0.3706 | 0.7158 (+0.1754) | 0.4799 (+0.1548) | 0.8154 (+0.3147) | 0.6383 (+0.2009) | 0.5771 (-0.1167) | 0.7927 (+0.0828) | 0.9524 (+0.1247) | 0.7125 (+0.2237) | 0.9465 (+0.1371) | 0.7703 (+0.1559) | 0.5792 (+0.2615) | 0.4053 (+0.0702) | 0.9625 (+0.0938) | 0.7191 (+0.1445) | | 0.2750 | 77022 | 0.5762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3000 | 84024 | 0.5674 | 0.3519 | 0.7079 (+0.1675) | 0.4903 (+0.1652) | 0.8032 (+0.3025) | 0.6422 (+0.2048) | 0.5937 (-0.1001) | 0.7918 (+0.0818) | 0.9541 (+0.1264) | 0.7310 (+0.2422) | 0.9504 (+0.1410) | 0.7604 (+0.1460) | 0.5713 (+0.2536) | 0.3968 (+0.0616) | 0.9661 (+0.0974) | 0.7199 (+0.1454) | | 0.3250 | 91026 | 0.5560 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3500 | 98028 | 0.5469 | 0.3438 | 0.7146 (+0.1741) | 0.4748 (+0.1498) | 0.8036 (+0.3029) | 0.6455 (+0.2080) | 0.5760 (-0.1178) | 0.7841 (+0.0742) | 0.9508 (+0.1231) | 0.7416 (+0.2528) | 0.9516 (+0.1422) | 0.7643 (+0.1499) | 0.5753 (+0.2576) | 0.4049 (+0.0697) | 0.9677 (+0.0990) | 0.7196 (+0.1450) | | 0.3750 | 105030 | 0.5390 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4000 | 112032 | 0.5311 | 0.3273 | 0.7130 (+0.1726) | 0.4694 (+0.1444) | 0.7990 (+0.2984) | 0.6438 (+0.2064) | 0.5836 (-0.1102) | 0.7971 (+0.0872) | 0.9530 (+0.1253) | 0.7557 (+0.2668) | 0.9528 (+0.1434) | 0.7634 (+0.1490) | 0.5795 (+0.2617) | 0.3944 (+0.0592) | 0.9681 (+0.0994) | 0.7210 (+0.1464) | | 0.4250 | 119034 | 0.5240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4500 | 126036 | 0.5167 | 0.3326 | 0.7020 (+0.1616) | 0.4574 (+0.1324) | 0.8008 (+0.3002) | 0.6433 (+0.2059) | 0.5939 (-0.0999) | 0.7906 (+0.0807) | 0.9541 (+0.1264) | 0.7691 (+0.2803) | 0.9556 (+0.1462) | 0.7631 (+0.1487) | 0.5739 (+0.2562) | 0.3995 (+0.0644) | 0.9681 (+0.0995) | 0.7209 (+0.1463) | | 0.4750 | 133038 | 0.5111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.5** | **140040** | **0.5055** | **0.3117** | **0.7017 (+0.1612)** | **0.4598 (+0.1347)** | **0.8083 (+0.3077)** | **0.6490 (+0.2116)** | **0.5931 (-0.1007)** | **0.7948 (+0.0849)** | **0.9508 (+0.1231)** | **0.7788 (+0.2899)** | **0.9532 (+0.1438)** | **0.7697 (+0.1554)** | **0.5750 (+0.2572)** | **0.4012 (+0.0661)** | **0.9725 (+0.1038)** | **0.7237 (+0.1491)** | | 0.5250 | 147042 | 0.5014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5500 | 154044 | 0.4959 | 0.3148 | 0.7077 (+0.1673) | 0.4558 (+0.1307) | 0.8064 (+0.3058) | 0.6637 (+0.2263) | 0.5897 (-0.1042) | 0.7815 (+0.0716) | 0.9502 (+0.1225) | 0.7543 (+0.2655) | 0.9538 (+0.1444) | 0.7640 (+0.1496) | 0.5730 (+0.2552) | 0.4001 (+0.0649) | 0.9701 (+0.1014) | 0.7208 (+0.1462) | | 0.5750 | 161046 | 0.4909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6000 | 168048 | 0.4868 | 0.3049 | 0.6990 (+0.1586) | 0.4731 (+0.1480) | 0.8101 (+0.3094) | 0.6543 (+0.2169) | 0.5765 (-0.1174) | 0.7919 (+0.0820) | 0.9529 (+0.1252) | 0.7612 (+0.2723) | 0.9617 (+0.1523) | 0.7592 (+0.1449) | 0.5760 (+0.2582) | 0.3966 (+0.0615) | 0.9675 (+0.0988) | 0.7215 (+0.1470) | | 0.6250 | 175050 | 0.4833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6500 | 182052 | 0.4803 | 0.3067 | 0.7067 (+0.1663) | 0.4568 (+0.1317) | 0.8063 (+0.3057) | 0.6528 (+0.2154) | 0.5771 (-0.1167) | 0.7817 (+0.0718) | 0.9529 (+0.1252) | 0.7536 (+0.2647) | 0.9638 (+0.1544) | 0.7658 (+0.1514) | 0.5662 (+0.2485) | 0.4023 (+0.0671) | 0.9671 (+0.0984) | 0.7195 (+0.1449) | | 0.6750 | 189054 | 0.4767 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7000 | 196056 | 0.4732 | 0.3055 | 0.7035 (+0.1631) | 0.4548 (+0.1297) | 0.8020 (+0.3013) | 0.6625 (+0.2251) | 0.5833 (-0.1105) | 0.7948 (+0.0849) | 0.9529 (+0.1252) | 0.7657 (+0.2769) | 0.9615 (+0.1521) | 0.7591 (+0.1448) | 0.5742 (+0.2565) | 0.3979 (+0.0627) | 0.9685 (+0.0998) | 0.7216 (+0.1471) | | 0.7250 | 203058 | 0.4705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7500 | 210060 | 0.4685 | 0.2933 | 0.7026 (+0.1622) | 0.4598 (+0.1347) | 0.8094 (+0.3087) | 0.6538 (+0.2164) | 0.5804 (-0.1134) | 0.7842 (+0.0743) | 0.9537 (+0.1260) | 0.7699 (+0.2811) | 0.9517 (+0.1423) | 0.7605 (+0.1461) | 0.5749 (+0.2572) | 0.3976 (+0.0625) | 0.9680 (+0.0993) | 0.7205 (+0.1460) | | 0.7750 | 217062 | 0.4658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8000 | 224064 | 0.4634 | 0.2923 | 0.7094 (+0.1690) | 0.4556 (+0.1306) | 0.7980 (+0.2973) | 0.6504 (+0.2130) | 0.5777 (-0.1161) | 0.7848 (+0.0748) | 0.9515 (+0.1237) | 0.7705 (+0.2817) | 0.9554 (+0.1460) | 0.7601 (+0.1457) | 0.5729 (+0.2552) | 0.3969 (+0.0618) | 0.9676 (+0.0989) | 0.7193 (+0.1447) | | 0.8250 | 231066 | 0.4605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8500 | 238068 | 0.4585 | 0.2897 | 0.6979 (+0.1575) | 0.4451 (+0.1200) | 0.7996 (+0.2990) | 0.6579 (+0.2205) | 0.5838 (-0.1100) | 0.7781 (+0.0682) | 0.9515 (+0.1237) | 0.7659 (+0.2771) | 0.9627 (+0.1533) | 0.7652 (+0.1509) | 0.5805 (+0.2628) | 0.3988 (+0.0637) | 0.9671 (+0.0985) | 0.7196 (+0.1450) | | 0.8750 | 245070 | 0.4572 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9000 | 252072 | 0.4551 | 0.2895 | 0.6991 (+0.1587) | 0.4488 (+0.1237) | 0.8066 (+0.3060) | 0.6568 (+0.2194) | 0.5910 (-0.1029) | 0.7835 (+0.0736) | 0.9537 (+0.1260) | 0.7627 (+0.2739) | 0.9536 (+0.1442) | 0.7642 (+0.1499) | 0.5734 (+0.2556) | 0.3988 (+0.0637) | 0.9665 (+0.0978) | 0.7199 (+0.1454) | | 0.9250 | 259074 | 0.4540 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9501 | 266076 | 0.4529 | 0.2875 | 0.6966 (+0.1562) | 0.4572 (+0.1322) | 0.7954 (+0.2948) | 0.6609 (+0.2235) | 0.5844 (-0.1094) | 0.7815 (+0.0716) | 0.9537 (+0.1260) | 0.7686 (+0.2798) | 0.9581 (+0.1487) | 0.7646 (+0.1503) | 0.5806 (+0.2629) | 0.3996 (+0.0644) | 0.9669 (+0.0982) | 0.7206 (+0.1461) | | 0.9751 | 273078 | 0.4526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 280065 | - | 0.2879 | 0.6978 (+0.1574) | 0.4485 (+0.1235) | 0.7980 (+0.2974) | 0.6605 (+0.2231) | 0.5840 (-0.1098) | 0.7775 (+0.0676) | 0.9515 (+0.1237) | 0.7637 (+0.2749) | 0.9607 (+0.1513) | 0.7631 (+0.1487) | 0.5740 (+0.2563) | 0.3991 (+0.0639) | 0.9668 (+0.0981) | 0.7189 (+0.1443) | * The bold row denotes the saved checkpoint. ### Training Time - **Training**: 20.0 hours - **Evaluation**: 46.6 minutes - **Total**: 20.7 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", } ```