--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: distilbert/distilbert-base-uncased widget: - text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower of the former World Trade Center in New York City. The introduction features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles. The rest of the video has several cuts to Durst and his bandmates hanging out of the Bentley as they drive about Manhattan. The song Ben Stiller is playing at the beginning is "My Generation" from the same album. The video also features scenes of Fred Durst with five girls dancing in a room. The video was filmed around the same time as the film Zoolander, which explains Stiller and Dorff's appearance. Fred Durst has a small cameo in that film. - text: 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release date once again, to February 9, 2018, in order to allow more time for post-production; months later, on August 25, the studio moved the release forward two weeks.[17] The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]' - text: who played the dj in the movie the warriors - text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with a growth hormone deficiency as a child. At age 13, he relocated to Spain to join Barcelona, who agreed to pay for his medical treatment. After a fast progression through Barcelona's youth academy, Messi made his competitive debut aged 17 in October 2004. Despite being injury-prone during his early career, he established himself as an integral player for the club within the next three years, finishing 2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year award, a feat he repeated the following year. His first uninterrupted campaign came in the 2008–09 season, during which he helped Barcelona achieve the first treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA World Player of the Year award by record voting margins. - text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik''s young wife runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]' 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 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 36.35355068873359 energy_consumed: 0.0935255045992395 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: 0.252 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: DistilBERT base 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: 0.4 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: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.13333333333333333 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: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.4 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: 0.48417691239896954 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.40474603174603174 name: Dot Mrr@10 - type: dot_map@100 value: 0.4165931820854422 name: Dot Map@100 - type: query_active_dims value: 68.80000305175781 name: Query Active Dims - type: query_sparsity_ratio value: 0.9977458881117962 name: Query Sparsity Ratio - type: corpus_active_dims value: 135.5758514404297 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9955580941143952 name: Corpus Sparsity Ratio - 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: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.56 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.36 name: Dot Precision@3 - type: dot_precision@5 value: 0.308 name: Dot Precision@5 - type: dot_precision@10 value: 0.234 name: Dot Precision@10 - type: dot_recall@1 value: 0.024688245739830684 name: Dot Recall@1 - type: dot_recall@3 value: 0.05757259881654739 name: Dot Recall@3 - type: dot_recall@5 value: 0.07457503506379409 name: Dot Recall@5 - type: dot_recall@10 value: 0.09455914797791706 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2854029431260111 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.46341269841269833 name: Dot Mrr@10 - type: dot_map@100 value: 0.11792914877304508 name: Dot Map@100 - type: query_active_dims value: 79.31999969482422 name: Query Active Dims - type: query_sparsity_ratio value: 0.9974012188030004 name: Query Sparsity Ratio - type: corpus_active_dims value: 184.8435516357422 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9939439240011879 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07600000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.61 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.562112822249959 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.535079365079365 name: Dot Mrr@10 - type: dot_map@100 value: 0.5164611300715877 name: Dot Map@100 - type: query_active_dims value: 54.099998474121094 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982275080769897 name: Query Sparsity Ratio - type: corpus_active_dims value: 133.11419677734375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9956387459282701 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.3666666666666667 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5133333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6866666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3666666666666667 name: Dot Precision@1 - type: dot_precision@3 value: 0.23555555555555552 name: Dot Precision@3 - type: dot_precision@5 value: 0.19200000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.12800000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.22822941524661022 name: Dot Recall@1 - type: dot_recall@3 value: 0.35585753293884914 name: Dot Recall@3 - type: dot_recall@5 value: 0.45819167835459806 name: Dot Recall@5 - type: dot_recall@10 value: 0.511519715992639 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4438975592583132 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.46774603174603174 name: Dot Mrr@10 - type: dot_map@100 value: 0.350327820310025 name: Dot Map@100 - type: query_active_dims value: 67.4066670735677 name: Query Active Dims - type: query_sparsity_ratio value: 0.9977915383305954 name: Query Sparsity Ratio - type: corpus_active_dims value: 145.78942579758726 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9952234641963965 name: Corpus Sparsity Ratio --- # DistilBERT base trained on Natural-Questions tuples This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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:** SPLADE Sparse Encoder - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) - **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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (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-distilbert-base-uncased-nq") # Run inference queries = [ "is send in the clowns from a musical", ] documents = [ 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]', 'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]', 'Money in the Bank ladder match The first match was contested in 2005 at WrestleMania 21, after being invented (in kayfabe) by Chris Jericho.[1] At the time, it was exclusive to wrestlers of the Raw brand, and Edge won the inaugural match.[1] From then until 2010, the Money in the Bank ladder match, now open to all WWE brands, became a WrestleMania mainstay. 2010 saw a second and third Money in the Bank ladder match when the Money in the Bank pay-per-view debuted in July. Unlike the matches at WrestleMania, this new event featured two such ladder matches – one each for a contract for the WWE Championship and World Heavyweight Championship, respectively.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[27.6088, 3.8288, 3.8780]]) ``` ## 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 | 0.42 | 0.4 | | dot_accuracy@3 | 0.4 | 0.5 | 0.64 | | dot_accuracy@5 | 0.62 | 0.52 | 0.72 | | dot_accuracy@10 | 0.74 | 0.56 | 0.76 | | dot_precision@1 | 0.28 | 0.42 | 0.4 | | dot_precision@3 | 0.1333 | 0.36 | 0.2133 | | dot_precision@5 | 0.124 | 0.308 | 0.144 | | dot_precision@10 | 0.074 | 0.234 | 0.076 | | dot_recall@1 | 0.28 | 0.0247 | 0.38 | | dot_recall@3 | 0.4 | 0.0576 | 0.61 | | dot_recall@5 | 0.62 | 0.0746 | 0.68 | | dot_recall@10 | 0.74 | 0.0946 | 0.7 | | **dot_ndcg@10** | **0.4842** | **0.2854** | **0.5621** | | dot_mrr@10 | 0.4047 | 0.4634 | 0.5351 | | dot_map@100 | 0.4166 | 0.1179 | 0.5165 | | query_active_dims | 68.8 | 79.32 | 54.1 | | query_sparsity_ratio | 0.9977 | 0.9974 | 0.9982 | | corpus_active_dims | 135.5759 | 184.8436 | 133.1142 | | corpus_sparsity_ratio | 0.9956 | 0.9939 | 0.9956 | #### 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 | 0.3667 | | dot_accuracy@3 | 0.5133 | | dot_accuracy@5 | 0.62 | | dot_accuracy@10 | 0.6867 | | dot_precision@1 | 0.3667 | | dot_precision@3 | 0.2356 | | dot_precision@5 | 0.192 | | dot_precision@10 | 0.128 | | dot_recall@1 | 0.2282 | | dot_recall@3 | 0.3559 | | dot_recall@5 | 0.4582 | | dot_recall@10 | 0.5115 | | **dot_ndcg@10** | **0.4439** | | dot_mrr@10 | 0.4677 | | dot_map@100 | 0.3503 | | query_active_dims | 67.4067 | | query_sparsity_ratio | 0.9978 | | corpus_active_dims | 145.7894 | | corpus_sparsity_ratio | 0.9952 | ## 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 answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who played the father in papa don't preach | Alex McArthur Alex McArthur (born March 6, 1957) is an American actor. | | where was the location of the battle of hastings | 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. | | how many puppies can a dog give birth to | 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(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### 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 answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | where is the tiber river located in italy | 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. | | what kind of car does jay gatsby drive | 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. | | who sings if i can dream about you | 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(scale=1.0, similarity_fct='dot_score')", "lambda_corpus": 3e-05, "lambda_query": 5e-05 } ``` ### 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 - `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`: 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`: 0.1 - `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 - `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 - `router_mapping`: {} - `learning_rate_mapping`: {}
### 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 | |:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:| | 0.0323 | 200 | 139.5463 | - | - | - | - | - | | 0.0646 | 400 | 0.3152 | - | - | - | - | - | | 0.0970 | 600 | 0.1291 | - | - | - | - | - | | 0.1293 | 800 | 0.0783 | - | - | - | - | - | | 0.1616 | 1000 | 0.0311 | 0.0839 | 0.4749 | 0.2698 | 0.5106 | 0.4184 | | 0.1939 | 1200 | 0.0427 | - | - | - | - | - | | 0.2262 | 1400 | 0.0368 | - | - | - | - | - | | 0.2586 | 1600 | 0.042 | - | - | - | - | - | | 0.2909 | 1800 | 0.0384 | - | - | - | - | - | | 0.3232 | 2000 | 0.0429 | 0.0632 | 0.4251 | 0.2626 | 0.5297 | 0.4058 | | 0.3555 | 2200 | 0.0304 | - | - | - | - | - | | 0.3878 | 2400 | 0.0357 | - | - | - | - | - | | 0.4202 | 2600 | 0.0294 | - | - | - | - | - | | 0.4525 | 2800 | 0.0289 | - | - | - | - | - | | 0.4848 | 3000 | 0.0287 | 0.0563 | 0.4496 | 0.2417 | 0.5590 | 0.4168 | | 0.5171 | 3200 | 0.0269 | - | - | - | - | - | | 0.5495 | 3400 | 0.0395 | - | - | - | - | - | | 0.5818 | 3600 | 0.0191 | - | - | - | - | - | | 0.6141 | 3800 | 0.0328 | - | - | - | - | - | | 0.6464 | 4000 | 0.0295 | 0.0502 | 0.4882 | 0.2537 | 0.5795 | 0.4405 | | 0.6787 | 4200 | 0.0155 | - | - | - | - | - | | 0.7111 | 4400 | 0.0274 | - | - | - | - | - | | 0.7434 | 4600 | 0.0324 | - | - | - | - | - | | 0.7757 | 4800 | 0.0197 | - | - | - | - | - | | 0.8080 | 5000 | 0.0178 | 0.0417 | 0.4871 | 0.2599 | 0.5651 | 0.4374 | | 0.8403 | 5200 | 0.0296 | - | - | - | - | - | | 0.8727 | 5400 | 0.0194 | - | - | - | - | - | | 0.9050 | 5600 | 0.0235 | - | - | - | - | - | | 0.9373 | 5800 | 0.0191 | - | - | - | - | - | | 0.9696 | 6000 | 0.0173 | 0.0390 | 0.4837 | 0.2866 | 0.5574 | 0.4425 | | -1 | -1 | - | - | 0.4842 | 0.2854 | 0.5621 | 0.4439 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.094 kWh - **Carbon Emitted**: 0.036 kg of CO2 - **Hours Used**: 0.252 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: 4.52.4 - 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}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```