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cross-encoder
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ettin-reranker-1b-v1

Text Ranking
sentence-transformers
Safetensors
English
modernbert
cross-encoder
reranker
Generated from Trainer
dataset_size:143393475
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community

ettin-reranker-1b-v1

This is a Cross Encoder model finetuned from jhu-clsp/ettin-encoder-1b on the cross-encoder/ettin-reranker-v1-data dataset using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

See the release blogpost for details on the training recipe, evaluation results, and speed benchmarks against other public rerankers. The Evaluation section below also has the headline numbers.

Model Details

Model Description

Model Sources

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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 (10 tasks, top-100 reranked) using MTEB's two-stage reranking flow, 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 MTEB(eng, v2) Retrieval with all-MiniLM-L6-v2 + reranker
MTEB(eng, v2) Retrieval with bge-small-en-v1.5 + reranker MTEB(eng, v2) Retrieval with nomic-embed-text-v1.5 + reranker
MTEB(eng, v2) Retrieval with embeddinggemma-300m + reranker MTEB(eng, v2) Retrieval with jina-embeddings-v5-text-small-retrieval + reranker
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 is underlined.

Reranker Params MTEB(eng, v2) Retrieval NDCG@10
Qwen/Qwen3-Reranker-4B† 4.02B 0.6367
mixedbread-ai/mxbai-rerank-large-v2 1.54B 0.6115
cross-encoder/ettin-reranker-1b-v1 1.00B 0.6114
cross-encoder/ettin-reranker-400m-v1 401M 0.6091
cross-encoder/ettin-reranker-150m-v1 151M 0.5994
Qwen/Qwen3-Reranker-0.6B 596M 0.5940
mixedbread-ai/mxbai-rerank-base-v2 494M 0.5920
cross-encoder/ettin-reranker-68m-v1 68.6M 0.5915
jinaai/jina-reranker-m0 2.44B 0.5856
Alibaba-NLP/gte-reranker-modernbert-base 150M 0.5843
cross-encoder/ettin-reranker-32m-v1 32.8M 0.5779
ibm-granite/granite-embedding-reranker-english-r2 150M 0.5656
cross-encoder/ettin-reranker-17m-v1 17.6M 0.5576
BAAI/bge-reranker-v2-m3 568M 0.5526
zeroentropy/zerank-2-reranker† 4.02B 0.5300
BAAI/bge-reranker-large 560M 0.5098
cross-encoder/ms-marco-MiniLM-L6-v2 22.7M 0.5082
cross-encoder/ms-marco-MiniLM-L12-v2 33.4M 0.5066
mixedbread-ai/mxbai-rerank-large-v1 435M 0.5063
cross-encoder/ms-marco-MiniLM-L4-v2 19.2M 0.4979
mixedbread-ai/mxbai-rerank-xsmall-v1 70.8M 0.4968
BAAI/bge-reranker-base 278M 0.4890
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 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 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. 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 17M FA2 7517
cross-encoder/ettin-reranker-32m-v1 32M FA2 6602
cross-encoder/ettin-reranker-68m-v1 68M FA2 4913
cross-encoder/ms-marco-MiniLM-L4-v2 19M FA2 4029
cross-encoder/ms-marco-MiniLM-L6-v2 22M FA2 3817
cross-encoder/ms-marco-MiniLM-L12-v2 33M FA2 3311
cross-encoder/ettin-reranker-150m-v1 150M FA2 3237
BAAI/bge-reranker-base 278M FA2 2858
mixedbread-ai/mxbai-rerank-xsmall-v1 70M eager 2636
mixedbread-ai/mxbai-rerank-base-v1 184M eager 1953
cross-encoder/ettin-reranker-400m-v1 400M FA2 1738
BAAI/bge-reranker-large 560M FA2 1659
BAAI/bge-reranker-v2-m3 568M FA2 1569
Alibaba-NLP/gte-reranker-modernbert-base 150M FA2 1418
ibm-granite/granite-embedding-reranker-english-r2 150M FA2 1404
cross-encoder/ettin-reranker-1b-v1 1B FA2 928
mixedbread-ai/mxbai-rerank-large-v1 435M eager 867
mixedbread-ai/mxbai-rerank-base-v2 494M FA2 809
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 17M FA2 9008
cross-encoder/ms-marco-MiniLM-L4-v2 19M FA2 5071
cross-encoder/ettin-reranker-32m-v1 32M FA2 4497
cross-encoder/ms-marco-MiniLM-L6-v2 22M FA2 4234
cross-encoder/ms-marco-MiniLM-L12-v2 33M FA2 2847
cross-encoder/ettin-reranker-68m-v1 68M FA2 1916
mixedbread-ai/mxbai-rerank-xsmall-v1 70M eager 1677
BAAI/bge-reranker-base 278M FA2 1329
cross-encoder/ettin-reranker-150m-v1 150M FA2 982
mixedbread-ai/mxbai-rerank-base-v1 184M eager 772
ibm-granite/granite-embedding-reranker-english-r2 150M FA2 598
Alibaba-NLP/gte-reranker-modernbert-base 150M FA2 586
BAAI/bge-reranker-large 560M FA2 448
BAAI/bge-reranker-v2-m3 568M FA2 436
cross-encoder/ettin-reranker-400m-v1 400M FA2 429
mixedbread-ai/mxbai-rerank-large-v1 435M eager 266
mixedbread-ai/mxbai-rerank-base-v2 494M FA2 221
cross-encoder/ettin-reranker-1b-v1 1B FA2 189
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 17M SDPA 267.4
cross-encoder/ms-marco-MiniLM-L4-v2 19M SDPA 206.2
cross-encoder/ms-marco-MiniLM-L6-v2 22M SDPA 143.9
cross-encoder/ettin-reranker-32m-v1 32M SDPA 92.5
cross-encoder/ms-marco-MiniLM-L12-v2 33M SDPA 75.9
mixedbread-ai/mxbai-rerank-xsmall-v1 70M eager 38.9
cross-encoder/ettin-reranker-68m-v1 68M SDPA 31.2
BAAI/bge-reranker-base 278M SDPA 19.2
Alibaba-NLP/gte-reranker-modernbert-base 150M SDPA 14.7
ibm-granite/granite-embedding-reranker-english-r2 150M SDPA 14.5
cross-encoder/ettin-reranker-150m-v1 150M SDPA 14.0
mixedbread-ai/mxbai-rerank-base-v1 184M eager 13.4
BAAI/bge-reranker-large 560M SDPA 6.2
BAAI/bge-reranker-v2-m3 568M SDPA 6.0
cross-encoder/ettin-reranker-400m-v1 400M SDPA 5.2
mixedbread-ai/mxbai-rerank-large-v1 435M eager 4.3
mixedbread-ai/mxbai-rerank-base-v2 494M SDPA 3.5
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 with these parameters:
    {
    "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 with these parameters:
    {
    "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)

The release blogpost 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
  • 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
    • min: 26 characters
    • mean: 55.52 characters
    • max: 249 characters
    • min: 63 characters
    • mean: 659.91 characters
    • max: 3975 characters
    • min: -2.94
    • mean: 8.51
    • max: 13.88
  • 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 with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity"
}

Evaluation Dataset

ettin-reranker-v1-data

  • Dataset: 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
    • min: 14 characters
    • mean: 52.62 characters
    • max: 168 characters
    • min: 11 characters
    • mean: 50.12 characters
    • max: 184 characters
    • min: 4.44
    • mean: 13.49
    • max: 18.62
  • 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 with these parameters:
    {
    "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 

@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 

@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",
}
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