--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:3011496 - loss:SpladeLoss base_model: Luyu/co-condenser-marco widget: - source_sentence: how much percent of alcohol is in scotch? sentences: - Our 24-hour day comes from the ancient Egyptians who divided day-time into 10 hours they measured with devices such as shadow clocks, and added a twilight hour at the beginning and another one at the end of the day-time, says Lomb. "Night-time was divided in 12 hours, based on the observations of stars. - After distillation, a Scotch Whisky can be anywhere between 60-75% ABV, with American Whiskey rocketing right into the 90% region. Before being placed in casks, Scotch is usually diluted to around 63.5% ABV (68% for grain); welcome to the stage cask strength Whisky. - Money For Nothing. In season four Dominic West, the ostensible star of the series, requested a reduced role so that he could spend more time with his family in London. On the show it was explained that Jimmy McNulty had taken a patrol job which required less strenuous work. - source_sentence: what are the major causes of poor listening? sentences: - The four main causes of poor listening are due to not concentrating, listening too hard, jumping to conclusions and focusing on delivery and personal appearance. Sometimes we just don't feel attentive enough and hence don't concentrate. - That's called being idle. “System Idle Process” is the software that runs when the computer has absolutely nothing better to do. It has the lowest possible priority and uses as few resources as possible, so that if anything at all comes along for the CPU to work on, it can. - 'No alcohol wine: how it''s made It''s not easy. There are three main methods currently in use. Vacuum distillation sees alcohol and other volatiles removed at a relatively low temperature (25°C-30°C), with aromatics blended back in afterwards.' - source_sentence: are jess and justin still together? sentences: - Download photos and videos to your device On your iPhone, iPad, or iPod touch, tap Settings > [your name] > iCloud > Photos. Then select Download and Keep Originals and import the photos to your computer. On your Mac, open the Photos app. Select the photos and videos you want to copy. - Later, Justin reunites with Jessica at prom and the two get back together. ... After a tearful goodbye to Jessica, the Jensens, and his friends, Justin dies just before graduation. - Incumbent president Muhammadu Buhari won his reelection bid, defeating his closest rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return, and was sworn in on May 29, 2019, the former date of Democracy Day (Nigeria). - source_sentence: when humans are depicted in hindu art? sentences: - 'Answer: Humans are depicted in Hindu art often in sensuous and erotic postures.' - Bettas are carnivores. They require foods high in animal protein. Their preferred diet in nature includes insects and insect larvae. In captivity, they thrive on a varied diet of pellets or flakes made from fish meal, as well as frozen or freeze-dried bloodworms. - An active continental margin is found on the leading edge of the continent where it is crashing into an oceanic plate. ... Passive continental margins are found along the remaining coastlines. - source_sentence: what is the difference between 18 and 20 inch tires? sentences: - '[''Alienware m17 R3. The best gaming laptop overall offers big power in slim, redesigned chassis. ... '', ''Dell G3 15. ... '', ''Asus ROG Zephyrus G14. ... '', ''Lenovo Legion Y545. ... '', ''Alienware Area 51m. ... '', ''Asus ROG Mothership. ... '', ''Asus ROG Strix Scar III. ... '', ''HP Omen 17 (2019)'']' - So extracurricular activities are just activities that you do outside of class. The Common App says that extracurricular activities "include arts, athletics, clubs, employment, personal commitments, and other pursuits." - The only real difference is a 20" rim would be more likely to be damaged, as you pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the availability of tires will likely be much more limited for the larger rim. ... Tire selection is better for 18" wheels than 20" wheels. datasets: - sentence-transformers/gooaq pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 co2_eq_emissions: emissions: 1032.3672234821006 energy_consumed: 2.655934941117104 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 9.368 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: splade-cocondenser trained on GooAQ results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.18 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.18 name: Dot Precision@1 - type: dot_precision@3 value: 0.14 name: Dot Precision@3 - type: dot_precision@5 value: 0.12 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.115 name: Dot Recall@1 - type: dot_recall@3 value: 0.19833333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.2683333333333333 name: Dot Recall@5 - type: dot_recall@10 value: 0.33233333333333326 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.25936082036566754 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3062460317460317 name: Dot Mrr@10 - type: dot_map@100 value: 0.20719224548153503 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.6 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.84 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6 name: Dot Precision@1 - type: dot_precision@3 value: 0.52 name: Dot Precision@3 - type: dot_precision@5 value: 0.5120000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.452 name: Dot Precision@10 - type: dot_recall@1 value: 0.05522786915214328 name: Dot Recall@1 - type: dot_recall@3 value: 0.11018533697480869 name: Dot Recall@3 - type: dot_recall@5 value: 0.1586380992797861 name: Dot Recall@5 - type: dot_recall@10 value: 0.28717168510493385 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5254041819320687 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6988888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.3939831545534725 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.74 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.88 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.74 name: Dot Precision@1 - type: dot_precision@3 value: 0.29333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.102 name: Dot Precision@10 - type: dot_recall@1 value: 0.7166666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8266666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9166666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9233333333333333 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8283955451135206 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8096666666666669 name: Dot Mrr@10 - type: dot_map@100 value: 0.7933820346320346 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.1855793650793651 name: Dot Recall@1 - type: dot_recall@3 value: 0.31376984126984125 name: Dot Recall@3 - type: dot_recall@5 value: 0.35210317460317453 name: Dot Recall@5 - type: dot_recall@10 value: 0.42468253968253966 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3556599197720009 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4181904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.3060313184828012 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.72 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.86 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.72 name: Dot Precision@1 - type: dot_precision@3 value: 0.4266666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.2799999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.14999999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.7 name: Dot Recall@5 - type: dot_recall@10 value: 0.75 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6950779198152243 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7948333333333334 name: Dot Mrr@10 - type: dot_map@100 value: 0.6244374457422245 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.1733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.124 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.52 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4858300241520006 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.40521428571428575 name: Dot Mrr@10 - type: dot_map@100 value: 0.42175748899886834 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.3466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.28200000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.046687705999640026 name: Dot Recall@1 - type: dot_recall@3 value: 0.09790588476953502 name: Dot Recall@3 - type: dot_recall@5 value: 0.12000426530930396 name: Dot Recall@5 - type: dot_recall@10 value: 0.16155008782514965 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3519230892919392 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5292380952380953 name: Dot Mrr@10 - type: dot_map@100 value: 0.16799707461195798 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.09 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.56 name: Dot Recall@3 - type: dot_recall@5 value: 0.65 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5968041069603208 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5449682539682539 name: Dot Mrr@10 - type: dot_map@100 value: 0.5360944900687548 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.94 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.98 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.37999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.24799999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.13399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.6973333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.8946666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.946 name: Dot Recall@5 - type: dot_recall@10 value: 0.99 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8922979605477963 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8785238095238094 name: Dot Mrr@10 - type: dot_map@100 value: 0.8493405677655678 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.16599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.074 name: Dot Recall@1 - type: dot_recall@3 value: 0.16666666666666669 name: Dot Recall@3 - type: dot_recall@5 value: 0.23166666666666663 name: Dot Recall@5 - type: dot_recall@10 value: 0.34266666666666656 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.32123645548157265 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5074126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.23675914234249176 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.14 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.14 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.12 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.14 name: Dot Recall@1 - type: dot_recall@3 value: 0.5 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4389511719056823 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3431904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.35224302854950346 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.2333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.455 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.76 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.61443063378869 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5731666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.5696919873212444 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.6326530612244898 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7959183673469388 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8367346938775511 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9591836734693877 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6326530612244898 name: Dot Precision@1 - type: dot_precision@3 value: 0.5374149659863945 name: Dot Precision@3 - type: dot_precision@5 value: 0.5020408163265306 name: Dot Precision@5 - type: dot_precision@10 value: 0.4326530612244897 name: Dot Precision@10 - type: dot_recall@1 value: 0.043721411012674946 name: Dot Recall@1 - type: dot_recall@3 value: 0.11111388641462987 name: Dot Recall@3 - type: dot_recall@5 value: 0.1725353206760411 name: Dot Recall@5 - type: dot_recall@10 value: 0.28394925382833736 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4877365323610393 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7339002267573695 name: Dot Mrr@10 - type: dot_map@100 value: 0.3590109302813293 name: Dot Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.46866562009419144 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6627629513343798 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7305180533751963 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8137833594976451 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.46866562009419144 name: Dot Precision@1 - type: dot_precision@3 value: 0.29928833071690214 name: Dot Precision@3 - type: dot_precision@5 value: 0.23861852433281003 name: Dot Precision@5 - type: dot_precision@10 value: 0.1706656200941915 name: Dot Precision@10 - type: dot_recall@1 value: 0.27301664240337103 name: Dot Recall@1 - type: dot_recall@3 value: 0.4299467909817038 name: Dot Recall@3 - type: dot_recall@5 value: 0.4935344251180747 name: Dot Recall@5 - type: dot_recall@10 value: 0.5788989922903304 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5271621816528863 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5802646084074655 name: Dot Mrr@10 - type: dot_map@100 value: 0.447532377602445 name: Dot Map@100 --- # splade-cocondenser trained on GooAQ This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## 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 SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/splade-cocondenser-gooaq") # Run inference sentences = [ 'what is the difference between 18 and 20 inch tires?', 'The only real difference is a 20" rim would be more likely to be damaged, as you pointed out. Beyond looks, there is zero benefit for the 20" rim. Also, just the availability of tires will likely be much more limited for the larger rim. ... Tire selection is better for 18" wheels than 20" wheels.', 'So extracurricular activities are just activities that you do outside of class. The Common App says that extracurricular activities "include arts, athletics, clubs, employment, personal commitments, and other pursuits."', ] embeddings = model.encode(sentences) print(embeddings.shape) # (3, 30522) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:-----------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.18 | 0.6 | 0.74 | 0.34 | 0.72 | 0.24 | 0.42 | 0.44 | 0.8 | 0.36 | 0.14 | 0.48 | 0.6327 | | dot_accuracy@3 | 0.38 | 0.78 | 0.88 | 0.46 | 0.86 | 0.52 | 0.6 | 0.6 | 0.94 | 0.62 | 0.5 | 0.68 | 0.7959 | | dot_accuracy@5 | 0.52 | 0.84 | 0.94 | 0.52 | 0.9 | 0.62 | 0.66 | 0.68 | 0.98 | 0.7 | 0.6 | 0.7 | 0.8367 | | dot_accuracy@10 | 0.62 | 0.92 | 0.94 | 0.62 | 0.94 | 0.74 | 0.76 | 0.82 | 1.0 | 0.76 | 0.74 | 0.76 | 0.9592 | | dot_precision@1 | 0.18 | 0.6 | 0.74 | 0.34 | 0.72 | 0.24 | 0.42 | 0.44 | 0.8 | 0.36 | 0.14 | 0.48 | 0.6327 | | dot_precision@3 | 0.14 | 0.52 | 0.2933 | 0.2067 | 0.4267 | 0.1733 | 0.3467 | 0.2 | 0.38 | 0.2667 | 0.1667 | 0.2333 | 0.5374 | | dot_precision@5 | 0.12 | 0.512 | 0.2 | 0.152 | 0.28 | 0.124 | 0.328 | 0.144 | 0.248 | 0.224 | 0.12 | 0.148 | 0.502 | | dot_precision@10 | 0.08 | 0.452 | 0.102 | 0.096 | 0.15 | 0.074 | 0.282 | 0.09 | 0.134 | 0.166 | 0.074 | 0.086 | 0.4327 | | dot_recall@1 | 0.115 | 0.0552 | 0.7167 | 0.1856 | 0.36 | 0.24 | 0.0467 | 0.42 | 0.6973 | 0.074 | 0.14 | 0.455 | 0.0437 | | dot_recall@3 | 0.1983 | 0.1102 | 0.8267 | 0.3138 | 0.64 | 0.52 | 0.0979 | 0.56 | 0.8947 | 0.1667 | 0.5 | 0.65 | 0.1111 | | dot_recall@5 | 0.2683 | 0.1586 | 0.9167 | 0.3521 | 0.7 | 0.62 | 0.12 | 0.65 | 0.946 | 0.2317 | 0.6 | 0.68 | 0.1725 | | dot_recall@10 | 0.3323 | 0.2872 | 0.9233 | 0.4247 | 0.75 | 0.74 | 0.1616 | 0.79 | 0.99 | 0.3427 | 0.74 | 0.76 | 0.2839 | | **dot_ndcg@10** | **0.2594** | **0.5254** | **0.8284** | **0.3557** | **0.6951** | **0.4858** | **0.3519** | **0.5968** | **0.8923** | **0.3212** | **0.439** | **0.6144** | **0.4877** | | dot_mrr@10 | 0.3062 | 0.6989 | 0.8097 | 0.4182 | 0.7948 | 0.4052 | 0.5292 | 0.545 | 0.8785 | 0.5074 | 0.3432 | 0.5732 | 0.7339 | | dot_map@100 | 0.2072 | 0.394 | 0.7934 | 0.306 | 0.6244 | 0.4218 | 0.168 | 0.5361 | 0.8493 | 0.2368 | 0.3522 | 0.5697 | 0.359 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:-----------------|:-----------| | dot_accuracy@1 | 0.4687 | | dot_accuracy@3 | 0.6628 | | dot_accuracy@5 | 0.7305 | | dot_accuracy@10 | 0.8138 | | dot_precision@1 | 0.4687 | | dot_precision@3 | 0.2993 | | dot_precision@5 | 0.2386 | | dot_precision@10 | 0.1707 | | dot_recall@1 | 0.273 | | dot_recall@3 | 0.4299 | | dot_recall@5 | 0.4935 | | dot_recall@10 | 0.5789 | | **dot_ndcg@10** | **0.5272** | | dot_mrr@10 | 0.5803 | | dot_map@100 | 0.4475 | ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,011,496 training samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the difference between clay and mud mask? | The main difference between the two is that mud is a skin-healing agent, while clay is a cosmetic, drying agent. Clay masks are most useful for someone who has oily skin and is prone to breakouts of acne and blemishes. | | myki how much on card? | A full fare myki card costs $6 and a concession, seniors or child myki costs $3. For more information about how to use your myki, visit ptv.vic.gov.au or call 1800 800 007. | | how to find out if someone blocked your phone number on iphone? | If you get a notification like "Message Not Delivered" or you get no notification at all, that's a sign of a potential block. Next, you could try calling the person. If the call goes right to voicemail or rings once (or a half ring) then goes to voicemail, that's further evidence you may have been blocked. | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) ), 'query_regularizer': FlopsLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) )} ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 1,000 evaluation samples * Columns: question and answer * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how do i program my directv remote with my tv? | ['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.'] | | are rodrigues fruit bats nocturnal? | Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night. | | why does your heart rate increase during exercise bbc bitesize? | During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it. | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 3e-05, 'lambda_query': 5e-05, 'corpus_regularizer': FlopsLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) ), 'query_regularizer': FlopsLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) )} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:----------:|:-------------:|:---------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:| | 0.0213 | 4000 | 0.3968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0425 | 8000 | 0.054 | 0.0224 | 0.2847 | 0.5628 | 0.8027 | 0.3260 | 0.6627 | 0.5252 | 0.3028 | 0.5467 | 0.7301 | 0.2563 | 0.3150 | 0.5072 | 0.4771 | 0.4846 | | 0.0638 | 12000 | 0.0468 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0850 | 16000 | 0.0394 | 0.0137 | 0.1908 | 0.5269 | 0.7778 | 0.3464 | 0.6510 | 0.5374 | 0.3086 | 0.5719 | 0.7901 | 0.2900 | 0.3661 | 0.5473 | 0.4839 | 0.4914 | | 0.1063 | 20000 | 0.035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1275 | 24000 | 0.0402 | 0.0142 | 0.1971 | 0.5098 | 0.6363 | 0.3715 | 0.6979 | 0.5442 | 0.3555 | 0.5223 | 0.7881 | 0.3008 | 0.3401 | 0.5963 | 0.4795 | 0.4877 | | 0.1488 | 28000 | 0.0286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1700 | 32000 | 0.0289 | 0.0209 | 0.2097 | 0.5169 | 0.7501 | 0.3622 | 0.6629 | 0.5151 | 0.3239 | 0.5322 | 0.8189 | 0.3121 | 0.3045 | 0.5318 | 0.4748 | 0.4858 | | 0.1913 | 36000 | 0.0241 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2125 | 40000 | 0.0243 | 0.0166 | 0.2150 | 0.4990 | 0.6614 | 0.3184 | 0.6564 | 0.5499 | 0.2924 | 0.5506 | 0.8177 | 0.2755 | 0.3214 | 0.5292 | 0.4605 | 0.4729 | | 0.2338 | 44000 | 0.021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2550 | 48000 | 0.0205 | 0.0045 | 0.2210 | 0.5328 | 0.5836 | 0.3180 | 0.6990 | 0.5365 | 0.2860 | 0.5529 | 0.8704 | 0.2860 | 0.4025 | 0.6107 | 0.4314 | 0.4870 | | 0.2763 | 52000 | 0.0181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2975 | 56000 | 0.018 | 0.0129 | 0.2131 | 0.5543 | 0.7181 | 0.3645 | 0.6852 | 0.5199 | 0.3232 | 0.5970 | 0.8914 | 0.2980 | 0.4618 | 0.5037 | 0.4592 | 0.5069 | | 0.3188 | 60000 | 0.0176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3400 | 64000 | 0.018 | 0.0141 | 0.2607 | 0.4594 | 0.7357 | 0.3597 | 0.6538 | 0.5082 | 0.3070 | 0.4944 | 0.8569 | 0.3252 | 0.4125 | 0.5243 | 0.4489 | 0.4882 | | 0.3613 | 68000 | 0.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3825 | 72000 | 0.0143 | 0.0082 | 0.2737 | 0.5459 | 0.7570 | 0.3845 | 0.6806 | 0.5035 | 0.3408 | 0.5338 | 0.8608 | 0.2888 | 0.3096 | 0.6163 | 0.4709 | 0.5051 | | 0.4038 | 76000 | 0.0148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4250 | 80000 | 0.0135 | 0.0211 | 0.2267 | 0.4964 | 0.7829 | 0.3579 | 0.6758 | 0.4954 | 0.3195 | 0.5164 | 0.8698 | 0.2745 | 0.3012 | 0.6260 | 0.4426 | 0.4912 | | 0.4463 | 84000 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4675 | 88000 | 0.012 | 0.0270 | 0.2442 | 0.5741 | 0.8005 | 0.3372 | 0.7019 | 0.5064 | 0.3109 | 0.6238 | 0.8988 | 0.2805 | 0.3875 | 0.5590 | 0.4396 | 0.5126 | | 0.4888 | 92000 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5100 | 96000 | 0.0127 | 0.0201 | 0.2948 | 0.5384 | 0.7822 | 0.3800 | 0.6947 | 0.5237 | 0.3674 | 0.5646 | 0.8843 | 0.2873 | 0.3825 | 0.5898 | 0.4812 | 0.5208 | | 0.5313 | 100000 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5525 | 104000 | 0.0112 | 0.0057 | 0.2318 | 0.5091 | 0.8362 | 0.3649 | 0.6829 | 0.4695 | 0.3442 | 0.5403 | 0.8920 | 0.2696 | 0.3787 | 0.6109 | 0.4384 | 0.5053 | | 0.5738 | 108000 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5951 | 112000 | 0.0095 | 0.0101 | 0.2325 | 0.5184 | 0.7349 | 0.3672 | 0.6673 | 0.4474 | 0.3196 | 0.5647 | 0.8866 | 0.2938 | 0.3345 | 0.5744 | 0.4609 | 0.4925 | | 0.6163 | 116000 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6376 | 120000 | 0.01 | 0.0084 | 0.2362 | 0.4989 | 0.8299 | 0.3595 | 0.6820 | 0.5200 | 0.3286 | 0.6138 | 0.8959 | 0.3088 | 0.4139 | 0.5808 | 0.4833 | 0.5194 | | 0.6588 | 124000 | 0.0103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6801 | 128000 | 0.0082 | 0.0115 | 0.2402 | 0.5127 | 0.7943 | 0.3828 | 0.6796 | 0.4925 | 0.3337 | 0.5848 | 0.8956 | 0.2880 | 0.3962 | 0.5981 | 0.4634 | 0.5124 | | 0.7013 | 132000 | 0.0085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7226 | 136000 | 0.0087 | 0.0125 | 0.2444 | 0.5258 | 0.7659 | 0.3397 | 0.6939 | 0.4942 | 0.3330 | 0.5573 | 0.8866 | 0.2789 | 0.3829 | 0.5305 | 0.4699 | 0.5002 | | 0.7438 | 140000 | 0.0092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7651 | 144000 | 0.0084 | 0.0071 | 0.2376 | 0.5247 | 0.8359 | 0.3551 | 0.6987 | 0.4440 | 0.3230 | 0.5973 | 0.8875 | 0.3052 | 0.4243 | 0.5601 | 0.4865 | 0.5138 | | 0.7863 | 148000 | 0.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8076 | 152000 | 0.0073 | 0.0036 | 0.2379 | 0.5045 | 0.8240 | 0.3389 | 0.7027 | 0.4895 | 0.3373 | 0.5893 | 0.8878 | 0.2870 | 0.3998 | 0.5728 | 0.4735 | 0.5112 | | 0.8288 | 156000 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.8501** | **160000** | **0.0076** | **0.0024** | **0.2594** | **0.5254** | **0.8284** | **0.3557** | **0.6951** | **0.4858** | **0.3519** | **0.5968** | **0.8923** | **0.3212** | **0.439** | **0.6144** | **0.4877** | **0.5272** | | 0.8713 | 164000 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8926 | 168000 | 0.0061 | 0.0084 | 0.2580 | 0.5068 | 0.8307 | 0.3629 | 0.7095 | 0.5132 | 0.3373 | 0.5577 | 0.8803 | 0.3041 | 0.4438 | 0.5802 | 0.4668 | 0.5193 | | 0.9138 | 172000 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9351 | 176000 | 0.0072 | 0.0076 | 0.2627 | 0.4988 | 0.8192 | 0.3587 | 0.7072 | 0.4968 | 0.3488 | 0.5746 | 0.8794 | 0.3049 | 0.4671 | 0.5872 | 0.4739 | 0.5215 | | 0.9563 | 180000 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9776 | 184000 | 0.0056 | 0.0067 | 0.2672 | 0.4954 | 0.8207 | 0.3473 | 0.7148 | 0.4997 | 0.3479 | 0.5798 | 0.8778 | 0.3115 | 0.4557 | 0.5884 | 0.4753 | 0.5216 | | 0.9988 | 188000 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.2594 | 0.5254 | 0.8284 | 0.3557 | 0.6951 | 0.4858 | 0.3519 | 0.5968 | 0.8923 | 0.3212 | 0.4390 | 0.6144 | 0.4877 | 0.5272 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 2.656 kWh - **Carbon Emitted**: 1.032 kg of CO2 - **Hours Used**: 9.368 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### 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", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ```