--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - asymmetric - inference-free - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss widget: - source_sentence: where is the tiber river located in italy sentences: - Sales taxes in British Columbia On 1 July 2010, the PST and GST were combined into the Harmonized Sales Tax (HST) levied according to the provisions of the GST. The conversion to HST was controversial. Popular opposition led to a referendum on the tax system, the first such referendum in the Commonwealth of Nations, resulting in the province reverting to the former PST/GST model on 1 April 2013. - 'Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.' - 'Water in California California''s limited water supply comes from two main sources: surface water, or water that travels or gathers on the ground, like rivers, streams, and lakes; and groundwater, which is water that is pumped out from the ground. California has also begun producing a small amount of desalinated water, water that was once sea water, but has been purified.' - source_sentence: what kind of car does jay gatsby drive sentences: - Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry. - 'Panama Canal The Panama Canal (Spanish: Canal de Panamá) is an artificial 77 km (48 mi) waterway in Panama that connects the Atlantic Ocean with the Pacific Ocean. The canal cuts across the Isthmus of Panama and is a conduit for maritime trade. Canal locks are at each end to lift ships up to Gatun Lake, an artificial lake created to reduce the amount of excavation work required for the canal, 26 m (85 ft) above sea level, and then lower the ships at the other end. The original locks are 34 m (110 ft) wide. A third, wider lane of locks was constructed between September 2007 and May 2016. The expanded canal began commercial operation on June 26, 2016. The new locks allow transit of larger, post-Panamax ships, capable of handling more cargo.[1]' - Solar maximum Predictions of a future maximum's timing and strength are very difficult; predictions vary widely. There was a solar maximum in 2000. In 2006 NASA initially expected a solar maximum in 2010 or 2011, and thought that it could be the strongest since 1958.[3] However, the solar maximum was not declared to have occurred until 2014, and even then was ranked among the weakest on record.[4] - source_sentence: who sings if i can dream about you sentences: - Wesley Jonathan Wesley Jonathan Waples (born October 18, 1978), known professionally as Wesley Jonathan, is an American actor. He is best known for his starring role as Jamal Grant on the NBC Saturday morning comedy-drama series City Guys, Sweetness in the 2005 film Roll Bounce, as well as Burrell "Stamps" Ballentine on TV Land's The Soul Man. - I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1] - Blood is thicker than water In modern society, the proverb "blood is thicker than water" is used to imply that family relationships are always more important than friends. - source_sentence: who did jesse palmer end up with on the bachelor sentences: - Jesse Palmer In 2004, Palmer was the first professional athlete to appear on The Bachelor television program and the first non-American bachelor, in which he was given his choice of eligible single women. He eventually selected Jessica Bowlin, but their courtship lasted for only a few months after the end of the show.[19][20] - Wave base In seawater, the water particles are moved in a circular orbital motion when a wave passes. The radius of the circle of motion for any given water molecule decreases exponentially with increasing depth. The wave base, which is the depth of influence of a water wave, is about half the wavelength. - Do You Remember the First Time? (The Vampire Diaries) Elena, after everyone continues to convince her that she had once loved damon decides to run through the magic free, mystic falls border. So she does, and she gets glimpses of her and Damon but never fully remembers yet that she loves him. Damon pulls her back across the line and she asks about a kiss in the rain. He continues to try to get her to remember. - source_sentence: when did the american civil rights movement end sentences: - 'A Sunday Afternoon on the Island of La Grande Jatte A Sunday Afternoon on the Island of La Grande Jatte (French: Un dimanche après-midi à l''Île de la Grande Jatte) painted in 1884, is one of Georges Seurat''s most famous works. It is a leading example of pointillist technique, executed on a large canvas. Seurat''s composition includes a number of Parisians at a park on the banks of the River Seine.' - Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic dating and magnetostratigraphy, the sites can be firmly dated to 2.6 million years ago. Evidence shows these early hominins intentionally selected raw materials with good flaking qualities and chose appropriate sized stones for their needs to produce sharp-edged tools for cutting.[29] - African-American civil rights movement (1954–1968) The Civil Rights Movement (also known as the American civil rights movement, African-American civil rights movement, and other terms,[b]) was a human rights movement from 1954–1968 that encompassed strategies, groups, and social movements to accomplish its goal of ending legalized racial segregation and discrimination laws in the United States. The movement secured the legal recognition and federal protection of black Americans in the United States Constitution and federal law. datasets: - sentence-transformers/natural-questions 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: 11.776380098641885 energy_consumed: 0.030296679972425883 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: 0.096 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.54 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5066725139399298 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4317460317460317 name: Dot Mrr@10 - type: dot_map@100 value: 0.4432974611015074 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.29600000000000004 name: Dot Precision@5 - type: dot_precision@10 value: 0.248 name: Dot Precision@10 - type: dot_recall@1 value: 0.043253729866814 name: Dot Recall@1 - type: dot_recall@3 value: 0.07701448892020092 name: Dot Recall@3 - type: dot_recall@5 value: 0.0882103437254049 name: Dot Recall@5 - type: dot_recall@10 value: 0.11441879984163104 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3142746286394966 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5100555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.13631231221886872 name: Dot Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.14 name: Dot Precision@5 - type: dot_precision@10 value: 0.078 name: Dot Precision@10 - type: dot_recall@1 value: 0.27 name: Dot Recall@1 - type: dot_recall@3 value: 0.58 name: Dot Recall@3 - type: dot_recall@5 value: 0.66 name: Dot Recall@5 - type: dot_recall@10 value: 0.72 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5073424422892974 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4506666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.4421389971626089 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: 0.3333333333333333 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5733333333333334 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6533333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7200000000000001 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3333333333333333 name: Dot Precision@1 - type: dot_precision@3 value: 0.25555555555555554 name: Dot Precision@3 - type: dot_precision@5 value: 0.19066666666666668 name: Dot Precision@5 - type: dot_precision@10 value: 0.13333333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.197751243288938 name: Dot Recall@1 - type: dot_recall@3 value: 0.399004829640067 name: Dot Recall@3 - type: dot_recall@5 value: 0.476070114575135 name: Dot Recall@5 - type: dot_recall@10 value: 0.524806266613877 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.44276319495624133 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4641560846560846 name: Dot Mrr@10 - type: dot_map@100 value: 0.34058292349432834 name: Dot Map@100 --- # Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model trained on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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:** Asymmetric Inference-free SPLADE Sparse Encoder - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **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): Asym( (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast) (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (corpus_1_SpladePooling): 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/inference-free-splade-bert-tiny-nq-3e-6-lambda-corpus") # Run inference sentences = [ 'when did the american civil rights movement end', 'African-American civil rights movement (1954–1968) The Civil Rights Movement (also known as the American civil rights movement, African-American civil rights movement, and other terms,[b]) was a human rights movement from 1954–1968 that encompassed strategies, groups, and social movements to accomplish its goal of ending legalized racial segregation and discrimination laws in the United States. The movement secured the legal recognition and federal protection of black Americans in the United States Constitution and federal law.', 'Paleolithic Paleolithic humans made tools of stone, bone, and wood.[23] The early paleolithic hominins, Australopithecus, were the first users of stone tools. Excavations in Gona, Ethiopia have produced thousands of artifacts, and through radioisotopic dating and magnetostratigraphy, the sites can be firmly dated to 2.6Â\xa0million years ago. Evidence shows these early hominins intentionally selected raw materials with good flaking qualities and chose appropriate sized stones for their needs to produce sharp-edged tools for cutting.[29]', ] 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: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | |:-----------------|:------------|:-------------|:-----------| | dot_accuracy@1 | 0.28 | 0.44 | 0.28 | | dot_accuracy@3 | 0.54 | 0.58 | 0.6 | | dot_accuracy@5 | 0.68 | 0.58 | 0.7 | | dot_accuracy@10 | 0.74 | 0.64 | 0.78 | | dot_precision@1 | 0.28 | 0.44 | 0.28 | | dot_precision@3 | 0.18 | 0.3867 | 0.2 | | dot_precision@5 | 0.136 | 0.296 | 0.14 | | dot_precision@10 | 0.074 | 0.248 | 0.078 | | dot_recall@1 | 0.28 | 0.0433 | 0.27 | | dot_recall@3 | 0.54 | 0.077 | 0.58 | | dot_recall@5 | 0.68 | 0.0882 | 0.66 | | dot_recall@10 | 0.74 | 0.1144 | 0.72 | | **dot_ndcg@10** | **0.5067** | **0.3143** | **0.5073** | | dot_mrr@10 | 0.4317 | 0.5101 | 0.4507 | | dot_map@100 | 0.4433 | 0.1363 | 0.4421 | #### 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": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-----------------|:-----------| | dot_accuracy@1 | 0.3333 | | dot_accuracy@3 | 0.5733 | | dot_accuracy@5 | 0.6533 | | dot_accuracy@10 | 0.72 | | dot_precision@1 | 0.3333 | | dot_precision@3 | 0.2556 | | dot_precision@5 | 0.1907 | | dot_precision@10 | 0.1333 | | dot_recall@1 | 0.1978 | | dot_recall@3 | 0.399 | | dot_recall@5 | 0.4761 | | dot_recall@10 | 0.5248 | | **dot_ndcg@10** | **0.4428** | | dot_mrr@10 | 0.4642 | | dot_map@100 | 0.3406 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: query and corpus * Approximate statistics based on the first 1000 samples: | | query | corpus | |:--------|:-------------------|:-------------------| | type | dict | dict | | details | | | * Samples: | query | corpus | |:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | {'query': "who played the father in papa don't preach"} | {'corpus': 'Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.'} | | {'query': 'where was the location of the battle of hastings'} | {'corpus': 'Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.'} | | {'query': 'how many puppies can a dog give birth to'} | {'corpus': 'Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]'} | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): Asym( (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast) (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 3e-06, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss( (model): SparseEncoder( (0): Asym( (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast) (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ) ), 'query_regularizer': None} ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: query and corpus * Approximate statistics based on the first 1000 samples: | | query | corpus | |:--------|:-------------------|:-------------------| | type | dict | dict | | details | | | * Samples: | query | corpus | |:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | {'query': 'where is the tiber river located in italy'} | {'corpus': 'Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252\xa0mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709\xa0sq\xa0mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.'} | | {'query': 'what kind of car does jay gatsby drive'} | {'corpus': 'Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby\'s yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.'} | | {'query': 'who sings if i can dream about you'} | {'corpus': 'I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman\'s album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]'} | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): Asym( (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast) (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 3e-06, 'lambda_query': 0, 'corpus_regularizer': FlopsLoss( (model): SparseEncoder( (0): Asym( (query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast) (corpus_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (corpus_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ) ), 'query_regularizer': None} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: 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`: 64 - `per_device_eval_batch_size`: 64 - `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`: 0.1 - `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`: False - `fp16`: True - `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`: False - `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 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:| | 0.0129 | 20 | 1.5408 | - | - | - | - | - | | 0.0259 | 40 | 1.4811 | - | - | - | - | - | | 0.0388 | 60 | 1.2964 | - | - | - | - | - | | 0.0517 | 80 | 0.9822 | - | - | - | - | - | | 0.0646 | 100 | 0.6764 | - | - | - | - | - | | 0.0776 | 120 | 0.547 | - | - | - | - | - | | 0.0905 | 140 | 0.4755 | - | - | - | - | - | | 0.1034 | 160 | 0.4212 | - | - | - | - | - | | 0.1164 | 180 | 0.4562 | - | - | - | - | - | | 0.1293 | 200 | 0.4057 | 0.3378 | 0.4848 | 0.3101 | 0.4742 | 0.4230 | | 0.1422 | 220 | 0.3772 | - | - | - | - | - | | 0.1551 | 240 | 0.3516 | - | - | - | - | - | | 0.1681 | 260 | 0.3768 | - | - | - | - | - | | 0.1810 | 280 | 0.3357 | - | - | - | - | - | | 0.1939 | 300 | 0.3209 | - | - | - | - | - | | 0.2069 | 320 | 0.3221 | - | - | - | - | - | | 0.2198 | 340 | 0.3183 | - | - | - | - | - | | 0.2327 | 360 | 0.3182 | - | - | - | - | - | | 0.2456 | 380 | 0.333 | - | - | - | - | - | | 0.2586 | 400 | 0.2946 | 0.2770 | 0.5115 | 0.3062 | 0.4842 | 0.4340 | | 0.2715 | 420 | 0.295 | - | - | - | - | - | | 0.2844 | 440 | 0.3019 | - | - | - | - | - | | 0.2973 | 460 | 0.2882 | - | - | - | - | - | | 0.3103 | 480 | 0.3203 | - | - | - | - | - | | 0.3232 | 500 | 0.3215 | - | - | - | - | - | | 0.3361 | 520 | 0.3018 | - | - | - | - | - | | 0.3491 | 540 | 0.2918 | - | - | - | - | - | | 0.3620 | 560 | 0.3365 | - | - | - | - | - | | 0.3749 | 580 | 0.2847 | - | - | - | - | - | | 0.3878 | 600 | 0.3382 | 0.2605 | 0.5192 | 0.3093 | 0.5002 | 0.4429 | | 0.4008 | 620 | 0.2845 | - | - | - | - | - | | 0.4137 | 640 | 0.2529 | - | - | - | - | - | | 0.4266 | 660 | 0.2885 | - | - | - | - | - | | 0.4396 | 680 | 0.2853 | - | - | - | - | - | | 0.4525 | 700 | 0.2711 | - | - | - | - | - | | 0.4654 | 720 | 0.2331 | - | - | - | - | - | | 0.4783 | 740 | 0.2612 | - | - | - | - | - | | 0.4913 | 760 | 0.2751 | - | - | - | - | - | | 0.5042 | 780 | 0.2458 | - | - | - | - | - | | 0.5171 | 800 | 0.2829 | 0.2475 | 0.5167 | 0.3117 | 0.5019 | 0.4434 | | 0.5301 | 820 | 0.2698 | - | - | - | - | - | | 0.5430 | 840 | 0.2455 | - | - | - | - | - | | 0.5559 | 860 | 0.2769 | - | - | - | - | - | | 0.5688 | 880 | 0.2569 | - | - | - | - | - | | 0.5818 | 900 | 0.2404 | - | - | - | - | - | | 0.5947 | 920 | 0.2538 | - | - | - | - | - | | 0.6076 | 940 | 0.2449 | - | - | - | - | - | | 0.6206 | 960 | 0.2649 | - | - | - | - | - | | 0.6335 | 980 | 0.271 | - | - | - | - | - | | 0.6464 | 1000 | 0.2081 | 0.2382 | 0.5087 | 0.3114 | 0.5082 | 0.4427 | | 0.6593 | 1020 | 0.2627 | - | - | - | - | - | | 0.6723 | 1040 | 0.2519 | - | - | - | - | - | | 0.6852 | 1060 | 0.2463 | - | - | - | - | - | | 0.6981 | 1080 | 0.2565 | - | - | - | - | - | | 0.7111 | 1100 | 0.2586 | - | - | - | - | - | | 0.7240 | 1120 | 0.2521 | - | - | - | - | - | | 0.7369 | 1140 | 0.2441 | - | - | - | - | - | | 0.7498 | 1160 | 0.2595 | - | - | - | - | - | | 0.7628 | 1180 | 0.2612 | - | - | - | - | - | | 0.7757 | 1200 | 0.2623 | 0.2324 | 0.5019 | 0.3129 | 0.5073 | 0.4407 | | 0.7886 | 1220 | 0.2393 | - | - | - | - | - | | 0.8016 | 1240 | 0.2606 | - | - | - | - | - | | 0.8145 | 1260 | 0.2328 | - | - | - | - | - | | 0.8274 | 1280 | 0.271 | - | - | - | - | - | | 0.8403 | 1300 | 0.2556 | - | - | - | - | - | | 0.8533 | 1320 | 0.2468 | - | - | - | - | - | | 0.8662 | 1340 | 0.2389 | - | - | - | - | - | | 0.8791 | 1360 | 0.2354 | - | - | - | - | - | | 0.8920 | 1380 | 0.2331 | - | - | - | - | - | | 0.9050 | 1400 | 0.2345 | 0.2303 | 0.5073 | 0.3139 | 0.5072 | 0.4428 | | 0.9179 | 1420 | 0.2364 | - | - | - | - | - | | 0.9308 | 1440 | 0.2125 | - | - | - | - | - | | 0.9438 | 1460 | 0.2634 | - | - | - | - | - | | 0.9567 | 1480 | 0.259 | - | - | - | - | - | | 0.9696 | 1500 | 0.2496 | - | - | - | - | - | | 0.9825 | 1520 | 0.2563 | - | - | - | - | - | | 0.9955 | 1540 | 0.2475 | - | - | - | - | - | | -1 | -1 | - | - | 0.5067 | 0.3143 | 0.5073 | 0.4428 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.030 kWh - **Carbon Emitted**: 0.012 kg of CO2 - **Hours Used**: 0.096 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}, } ```