sparse-encoder/splade-ModernBERT-nq-fresh-lq0.05-lc0.003_scale1_lr-1e-4_bs64

SparseEncoder

This is a Sparse Encoder model trained on the natural-questions dataset using the sentence-transformers library. It maps sentences & paragraphs to a 50368-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

• Model Type: Sparse Encoder
• Maximum Sequence Length: 512 tokens
• Training Dataset:
  • natural-questions
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Sparse Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sparse Encoders on Hugging Face

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("sparse-embedding/splade-ModernBERT-nq-fresh-lq0.05-lc0.003_scale1_lr-1e-4_bs64")
# Run inference
sentences = [
    'is send in the clowns from a musical',
    '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]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 50368)

# 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

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
dot_accuracy@1	0.22	0.66	0.48	0.38	0.74	0.32	0.38	0.38	0.76	0.36	0.22	0.44	0.6122
dot_accuracy@3	0.46	0.76	0.82	0.52	0.8	0.6	0.5	0.62	0.86	0.62	0.56	0.66	0.8571
dot_accuracy@5	0.56	0.84	0.88	0.6	0.88	0.7	0.54	0.74	0.92	0.66	0.7	0.72	0.898
dot_accuracy@10	0.72	0.94	0.92	0.7	0.92	0.82	0.56	0.8	1.0	0.84	0.82	0.8	0.9592
dot_precision@1	0.22	0.66	0.48	0.38	0.74	0.32	0.38	0.38	0.76	0.36	0.22	0.44	0.6122
dot_precision@3	0.16	0.5267	0.28	0.2333	0.38	0.2	0.3267	0.2133	0.3333	0.28	0.1867	0.2333	0.5918
dot_precision@5	0.124	0.488	0.18	0.176	0.256	0.14	0.3	0.152	0.216	0.236	0.14	0.16	0.5143
dot_precision@10	0.086	0.448	0.094	0.106	0.142	0.082	0.238	0.084	0.128	0.174	0.082	0.09	0.4429
dot_recall@1	0.0667	0.0667	0.47	0.1839	0.37	0.32	0.0409	0.35	0.6673	0.0757	0.22	0.425	0.0435
dot_recall@3	0.2383	0.1197	0.79	0.3052	0.57	0.6	0.0705	0.59	0.808	0.1727	0.56	0.63	0.1286
dot_recall@5	0.28	0.1792	0.85	0.3767	0.64	0.7	0.0909	0.69	0.8613	0.2417	0.7	0.7	0.1792
dot_recall@10	0.37	0.3035	0.89	0.4737	0.71	0.82	0.1157	0.75	0.966	0.3567	0.82	0.79	0.294
dot_ndcg@10	0.2677	0.5343	0.7041	0.3885	0.6641	0.558	0.2972	0.5726	0.8419	0.3329	0.515	0.6152	0.5011
dot_mrr@10	0.3606	0.7355	0.6527	0.4707	0.7923	0.475	0.4452	0.5327	0.8272	0.5093	0.4177	0.5621	0.7391
dot_map@100	0.1851	0.4049	0.6419	0.3248	0.5976	0.4854	0.1283	0.5141	0.7951	0.253	0.4241	0.5632	0.3876

Sparse Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with SparseNanoBEIREvaluator with these parameters:

  {
      "dataset_names": [
          "climatefever",
          "dbpedia",
          "fever",
          "fiqa2018",
          "hotpotqa",
          "msmarco",
          "nfcorpus",
          "nq",
          "quoraretrieval",
          "scidocs",
          "arguana",
          "scifact",
          "touche2020"
      ]
  }
  

Metric	Value
dot_accuracy@1	0.4579
dot_accuracy@3	0.6644
dot_accuracy@5	0.7414
dot_accuracy@10	0.8307
dot_precision@1	0.4579
dot_precision@3	0.3035
dot_precision@5	0.2371
dot_precision@10	0.169
dot_recall@1	0.2538
dot_recall@3	0.4295
dot_recall@5	0.4992
dot_recall@10	0.5892
dot_ndcg@10	0.5225
dot_mrr@10	0.5785
dot_map@100	0.4389

Training Details

Training Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 99,000 training samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 29 characters mean: 46.96 characters max: 93 characters	min: 10 characters mean: 582.13 characters max: 2141 characters

• 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 with these parameters:

  {'lambda_corpus': 0.003, 'lambda_query': 0.05, 'main_loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: ModernBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'word_embedding_dimension': None})
    )
    (cross_entropy_loss): CrossEntropyLoss()
  )}
  

Evaluation Dataset

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 1,000 evaluation samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 30 characters mean: 47.2 characters max: 96 characters	min: 58 characters mean: 598.96 characters max: 2480 characters

• 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 with these parameters:

  {'lambda_corpus': 0.003, 'lambda_query': 0.05, 'main_loss': SparseMultipleNegativesRankingLoss(
    (model): SparseEncoder(
      (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: ModernBertForMaskedLM 
      (1): SpladePooling({'pooling_strategy': 'max', 'word_embedding_dimension': None})
    )
    (cross_entropy_loss): CrossEntropyLoss()
  )}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 0.0001
• 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: 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: 0.0001
• 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

Click to expand

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
0.0065	10	7184.1445	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0129	20	1903.0385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0194	30	909.2298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0259	40	316.7136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0323	50	244.4539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0388	60	294.2571	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0452	70	353.676	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0517	80	152.3629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0582	90	104.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	100	45.3187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0711	110	22.4178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0776	120	13.1608	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0840	130	8.5385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0905	140	6.384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	150	4.6373	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1034	160	4.4281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1099	170	3.8958	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1164	180	2.9138	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1228	190	2.0	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	200	1.3596	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1357	210	0.931	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1422	220	0.8304	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1487	230	0.5943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1551	240	0.4164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1616	250	0.3703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1681	260	0.3452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1745	270	0.3224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1810	280	0.2795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1875	290	0.2597	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	300	0.3003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1997	309	-	0.2614	0.2510	0.4642	0.7084	0.2488	0.5736	0.4460	0.2437	0.3954	0.6417	0.2529	0.3068	0.5133	0.3905	0.4182
0.2004	310	0.2549	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2069	320	0.2208	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2133	330	0.215	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2198	340	0.2113	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2262	350	0.198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2327	360	0.2177	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2392	370	0.3034	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2456	380	0.2184	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2521	390	0.211	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	400	0.1726	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2650	410	0.1745	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2715	420	0.1978	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2780	430	0.1966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2844	440	0.1961	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	450	0.1705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2973	460	0.2358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3038	470	0.1643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3103	480	0.1824	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3167	490	0.2357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	500	0.1341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3297	510	0.1786	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3361	520	0.1392	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3426	530	0.1434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3491	540	0.1684	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3555	550	0.1827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3620	560	0.1296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3685	570	0.1731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3749	580	0.182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3814	590	0.1587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	600	0.1519	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	610	0.1944	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3995	618	-	0.1768	0.2776	0.4818	0.7670	0.3444	0.5941	0.4696	0.2791	0.5422	0.7823	0.2977	0.4389	0.5579	0.4978	0.4869
0.4008	620	0.1778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4072	630	0.1595	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4137	640	0.1268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4202	650	0.1361	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4266	660	0.1416	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4331	670	0.1139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4396	680	0.1734	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4460	690	0.1082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	700	0.1198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4590	710	0.0981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4654	720	0.0943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4719	730	0.1421	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4783	740	0.0903	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	750	0.1339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4913	760	0.1109	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4977	770	0.1245	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5042	780	0.0949	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5107	790	0.0954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	800	0.1136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5236	810	0.1206	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5301	820	0.101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5365	830	0.1372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5430	840	0.1123	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5495	850	0.1358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5559	860	0.1303	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5624	870	0.1339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5688	880	0.1096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5753	890	0.079	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	900	0.0988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5882	910	0.1042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5947	920	0.0905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5992	927	-	0.1155	0.2490	0.5054	0.7070	0.3375	0.6075	0.4871	0.2843	0.5344	0.7616	0.3309	0.4869	0.6136	0.4983	0.4926
0.6012	930	0.0841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6076	940	0.0946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6141	950	0.086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6206	960	0.118	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6270	970	0.0981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6335	980	0.117	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6399	990	0.0984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	1000	0.1235	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6529	1010	0.1026	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6593	1020	0.0919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6658	1030	0.0891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6723	1040	0.1363	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6787	1050	0.0765	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6852	1060	0.0918	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6917	1070	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6981	1080	0.076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7046	1090	0.0851	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	1100	0.0811	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7175	1110	0.0775	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7240	1120	0.1029	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7304	1130	0.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7369	1140	0.0961	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7434	1150	0.1159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7498	1160	0.0919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7563	1170	0.0849	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7628	1180	0.1021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7692	1190	0.065	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	1200	0.0858	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7822	1210	0.0826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7886	1220	0.069	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7951	1230	0.0718	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.799	1236	-	0.0956	0.2677	0.5343	0.7041	0.3885	0.6641	0.558	0.2972	0.5726	0.8419	0.3329	0.515	0.6152	0.5011	0.5225
0.8016	1240	0.076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8080	1250	0.0703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8145	1260	0.0615	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8209	1270	0.0969	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8274	1280	0.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8339	1290	0.0616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	1300	0.0752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8468	1310	0.0762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8533	1320	0.0691	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8597	1330	0.102	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8662	1340	0.0778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	1350	0.0619	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8791	1360	0.0865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8856	1370	0.0546	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8920	1380	0.0705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8985	1390	0.0713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	1400	0.0669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9114	1410	0.0742	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9179	1420	0.0714	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9244	1430	0.0753	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9308	1440	0.0536	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9373	1450	0.0765	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9438	1460	0.0665	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9502	1470	0.0736	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9567	1480	0.0559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9632	1490	0.0587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	1500	0.0798	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9761	1510	0.0819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9825	1520	0.1039	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9890	1530	0.0617	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9955	1540	0.062	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9987	1545	-	0.0789	0.2818	0.5012	0.6833	0.3634	0.5937	0.5268	0.2913	0.5805	0.8237	0.3454	0.5020	0.6028	0.4961	0.5071
1	-1	-	-	0.2677	0.5343	0.7041	0.3885	0.6641	0.5580	0.2972	0.5726	0.8419	0.3329	0.5150	0.6152	0.5011	0.5225

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.10
• Sentence Transformers: 4.2.0.dev0
• Transformers: 4.48.3
• PyTorch: 2.7.0+cu126
• Accelerate: 1.6.0
• Datasets: 2.21.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

SpladeLoss

@inproceedings{10.1145/3477495.3531857,
author = {Formal, Thibault and Lassance, Carlos and Piwowarski, Benjamin and Clinchant, St'{e}phane},
title = {From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531857},
doi = {10.1145/3477495.3531857},
abstract = {Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2353–2359},
numpages = {7},
keywords = {neural networks, indexing, sparse representations, regularization},
location = {Madrid, Spain},
series = {SIGIR '22}
}
}