Starkdoorstep12/entity-resolution-crossencoder

CrossEncoder based on cross-encoder/mmarco-mMiniLMv2-L12-H384-v1

This is a Cross Encoder model finetuned from cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

• Model Type: Cross Encoder
• Base model: cross-encoder/mmarco-mMiniLMv2-L12-H384-v1
• Maximum Sequence Length: 512 tokens
• Number of Output Labels: 1 label
• Supported Modality: Text

Model Sources

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

Full Model Architecture

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("cross_encoder_model_id")
# Get scores for pairs of inputs
pairs = [
    ['Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka', 'Bangalore South Automotive Infratech Private  Ltd | C-905, GOYALCO ORCHID, LAKEVIEW, BANGALORE SOUTH, Karnataka'],
    ['Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka', 'OWA Cafe LLP | A-1206, 12TH FLOOR, GOYALCO ORCHID LAKEVIEW, BANGALORE SOUTH, ಕರ್ನಾಟಕ'],
    ['IMY India Company | Plot No. 3, 3Rd Floor, Road No G7, Dlf Phase-1, Gurgaon Kty., Gurgaon, Haryana', 'IMY  India Company | PLTO NO. ##3 , 3RD FLOOR, ROAD NO G7, DLF PHASE-1, GURGAON KTY., हरियाणा'],
    ['IMY India Company | Plot No. 3, 3Rd Floor, Road No G7, Dlf Phase-1, Gurgaon Kty., Gurgaon, Haryana', 'IMY India Co | SHOP302, FORTUNEALEXA, OPP., KADAMHEIGHT, 150FTRINGRD-2, RAJKOT, Gujarat'],
    ['Software Pinnacle Tourism | 3016/5, 1St Floor, Ranjit Nagar, New Delhi, Central Delhi, Delhi', 'Software Pinnacle Tóurism | A-3016/5, 1ST FLOOR, RANJIT NAGAR, NEW DELHI, दिल्ली'],
]
scores = model.predict(pairs)
print(scores)
# [-11.7818 -11.8389  11.1288 -11.8149  11.103 ]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka',
    [
        'Bangalore South Automotive Infratech Private  Ltd | C-905, GOYALCO ORCHID, LAKEVIEW, BANGALORE SOUTH, Karnataka',
        'OWA Cafe LLP | A-1206, 12TH FLOOR, GOYALCO ORCHID LAKEVIEW, BANGALORE SOUTH, ಕರ್ನಾಟಕ',
        'IMY  India Company | PLTO NO. ##3 , 3RD FLOOR, ROAD NO G7, DLF PHASE-1, GURGAON KTY., हरियाणा',
        'IMY India Co | SHOP302, FORTUNEALEXA, OPP., KADAMHEIGHT, 150FTRINGRD-2, RAJKOT, Gujarat',
        'Software Pinnacle Tóurism | A-3016/5, 1ST FLOOR, RANJIT NAGAR, NEW DELHI, दिल्ली',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Classification

• Dataset: ce-val
• Evaluated with CrossEncoderClassificationEvaluator

Metric	Value
accuracy	0.9969
accuracy_threshold	0.2028
f1	0.9918
f1_threshold	0.1996
precision	0.9876
recall	0.996
average_precision	0.9996

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,299,295 training samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 100 samples:

  	text1	text2	label
  type	string	string	float
  modality	text	text
  details	min: 24 tokens mean: 36.35 tokens max: 45 tokens	min: 12 tokens mean: 44.4 tokens max: 72 tokens	min: 0.0 mean: 0.2 max: 1.0

• Samples:

  text1	text2	label
  Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka	Bangalore South Automotive Limited Private | C-901., GOYALCO ORCHID, LAKEVIEW, BANGALORE SOUTH, ಕರ್ನಾಟಕ	1.0
  Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka	Bangalore Sóuth Automotive Private Limited |	1.0
  Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka	SOUTH AUTOMOTIVE PRIVATE LIMITED | A-71/2, HARI NAGAR, ASHRAM, NEW DELHI, Delhi	0.0

• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 366,241 evaluation samples
• Columns: text1, text2, and label
• Approximate statistics based on the first 100 samples:

  	text1	text2	label
  type	string	string	float
  modality	text	text
  details	min: 16 tokens mean: 36.73 tokens max: 63 tokens	min: 12 tokens mean: 43.76 tokens max: 74 tokens	min: 0.0 mean: 0.14 max: 1.0

• Samples:

  text1	text2	label
  Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka	Bangalore South Automotive Infratech Private Ltd | C-905, GOYALCO ORCHID, LAKEVIEW, BANGALORE SOUTH, Karnataka	0.0
  Bangalore South Automotive Private Limited | C-901, Goyalco Orchid, Lakeview, Bangalore South, Bangalore, Karnataka	OWA Cafe LLP | A-1206, 12TH FLOOR, GOYALCO ORCHID LAKEVIEW, BANGALORE SOUTH, ಕರ್ನಾಟಕ	0.0
  IMY India Company | Plot No. 3, 3Rd Floor, Road No G7, Dlf Phase-1, Gurgaon Kty., Gurgaon, Haryana	IMY India Company | PLTO NO. ##3 , 3RD FLOOR, ROAD NO G7, DLF PHASE-1, GURGAON KTY., हरियाणा	1.0

• Loss: BinaryCrossEntropyLoss with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• num_train_epochs: 2
• learning_rate: 2e-05
• warmup_steps: 0.1
• per_device_eval_batch_size: 64
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• per_device_train_batch_size: 64
• num_train_epochs: 2
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 64
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: True
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• dataloader_multiprocessing_context: None
• dataloader_in_order: True
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}
• warmup_ratio: None

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	ce-val_average_precision
0.0010	50	1.3570	-	-
0.0019	100	1.1736	-	-
0.0029	150	0.8991	-	-
0.0039	200	0.6422	-	-
0.0048	250	0.4525	-	-
0.0058	300	0.3296	-	-
0.0068	350	0.2117	-	-
0.0078	400	0.2189	-	-
0.0087	450	0.2137	-	-
0.0097	500	0.1730	-	-
0.0107	550	0.1698	-	-
0.0116	600	0.1623	-	-
0.0126	650	0.1294	-	-
0.0136	700	0.1223	-	-
0.0145	750	0.1257	-	-
0.0155	800	0.1120	-	-
0.0165	850	0.1136	-	-
0.0175	900	0.1011	-	-
0.0184	950	0.0982	-	-
0.0194	1000	0.1092	-	-
0.0204	1050	0.0936	-	-
0.0213	1100	0.0992	-	-
0.0223	1150	0.0908	-	-
0.0233	1200	0.0777	-	-
0.0242	1250	0.0813	-	-
0.0252	1300	0.0724	-	-
0.0262	1350	0.0728	-	-
0.0272	1400	0.0711	-	-
0.0281	1450	0.0753	-	-
0.0291	1500	0.0671	-	-
0.0301	1550	0.0790	-	-
0.0310	1600	0.0685	-	-
0.0320	1650	0.0671	-	-
0.0330	1700	0.0684	-	-
0.0339	1750	0.0596	-	-
0.0349	1800	0.0524	-	-
0.0359	1850	0.0661	-	-
0.0369	1900	0.0608	-	-
0.0378	1950	0.0696	-	-
0.0388	2000	0.0621	-	-
0.0398	2050	0.0460	-	-
0.0407	2100	0.0532	-	-
0.0417	2150	0.0513	-	-
0.0427	2200	0.0507	-	-
0.0436	2250	0.0652	-	-
0.0446	2300	0.0541	-	-
0.0456	2350	0.0583	-	-
0.0466	2400	0.0462	-	-
0.0475	2450	0.0493	-	-
0.0485	2500	0.0534	-	-
0.0495	2550	0.0554	-	-
0.0504	2600	0.0439	-	-
0.0514	2650	0.0465	-	-
0.0524	2700	0.0472	-	-
0.0533	2750	0.0525	-	-
0.0543	2800	0.0429	-	-
0.0553	2850	0.0533	-	-
0.0563	2900	0.0486	-	-
0.0572	2950	0.0474	-	-
0.0582	3000	0.0396	-	-
0.0592	3050	0.0424	-	-
0.0601	3100	0.0463	-	-
0.0611	3150	0.0425	-	-
0.0621	3200	0.0484	-	-
0.0630	3250	0.0411	-	-
0.0640	3300	0.0429	-	-
0.0650	3350	0.0292	-	-
0.0660	3400	0.0389	-	-
0.0669	3450	0.0373	-	-
0.0679	3500	0.0466	-	-
0.0689	3550	0.0344	-	-
0.0698	3600	0.0371	-	-
0.0708	3650	0.0370	-	-
0.0718	3700	0.0450	-	-
0.0727	3750	0.0317	-	-
0.0737	3800	0.0454	-	-
0.0747	3850	0.0445	-	-
0.0757	3900	0.0465	-	-
0.0766	3950	0.0347	-	-
0.0776	4000	0.0412	-	-
0.0786	4050	0.0323	-	-
0.0795	4100	0.0368	-	-
0.0805	4150	0.0440	-	-
0.0815	4200	0.0328	-	-
0.0824	4250	0.0368	-	-
0.0834	4300	0.0389	-	-
0.0844	4350	0.0421	-	-
0.0854	4400	0.0331	-	-
0.0863	4450	0.0332	-	-
0.0873	4500	0.0407	-	-
0.0883	4550	0.0276	-	-
0.0892	4600	0.0361	-	-
0.0902	4650	0.0299	-	-
0.0912	4700	0.0253	-	-
0.0921	4750	0.0370	-	-
0.0931	4800	0.0304	-	-
0.0941	4850	0.0348	-	-
0.0950	4900	0.0302	-	-
0.0960	4950	0.0322	-	-
0.0970	5000	0.0315	-	-
0.0980	5050	0.0402	-	-
0.0989	5100	0.0320	-	-
0.0999	5150	0.0369	-	-
0.1009	5200	0.0271	-	-
0.1018	5250	0.0372	-	-
0.1028	5300	0.0347	-	-
0.1038	5350	0.0331	-	-
0.1047	5400	0.0342	-	-
0.1057	5450	0.0365	-	-
0.1067	5500	0.0318	-	-
0.1077	5550	0.0171	-	-
0.1086	5600	0.0322	-	-
0.1096	5650	0.0368	-	-
0.1106	5700	0.0304	-	-
0.1115	5750	0.0291	-	-
0.1125	5800	0.0254	-	-
0.1135	5850	0.0323	-	-
0.1144	5900	0.0333	-	-
0.1154	5950	0.0342	-	-
0.1164	6000	0.0329	-	-
0.1174	6050	0.0201	-	-
0.1183	6100	0.0342	-	-
0.1193	6150	0.0323	-	-
0.1203	6200	0.0304	-	-
0.1212	6250	0.0326	-	-
0.1222	6300	0.0259	-	-
0.1232	6350	0.0418	-	-
0.1241	6400	0.0272	-	-
0.1251	6450	0.0226	-	-
0.1261	6500	0.0237	-	-
0.1271	6550	0.0271	-	-
0.1280	6600	0.0216	-	-
0.1290	6650	0.0244	-	-
0.1300	6700	0.0310	-	-
0.1309	6750	0.0259	-	-
0.1319	6800	0.0257	-	-
0.1329	6850	0.0236	-	-
0.1338	6900	0.0278	-	-
0.1348	6950	0.0291	-	-
0.1358	7000	0.0265	-	-
0.1368	7050	0.0224	-	-
0.1377	7100	0.0161	-	-
0.1387	7150	0.0320	-	-
0.1397	7200	0.0347	-	-
0.1406	7250	0.0271	-	-
0.1416	7300	0.0286	-	-
0.1426	7350	0.0307	-	-
0.1435	7400	0.0327	-	-
0.1445	7450	0.0279	-	-
0.1455	7500	0.0212	-	-
0.1465	7550	0.0323	-	-
0.1474	7600	0.0271	-	-
0.1484	7650	0.0296	-	-
0.1494	7700	0.0334	-	-
0.1503	7750	0.0228	-	-
0.1513	7800	0.0232	-	-
0.1523	7850	0.0248	-	-
0.1532	7900	0.0310	-	-
0.1542	7950	0.0333	-	-
0.1552	8000	0.0293	-	-
0.1562	8050	0.0272	-	-
0.1571	8100	0.0244	-	-
0.1581	8150	0.0292	-	-
0.1591	8200	0.0257	-	-
0.1600	8250	0.0150	-	-
0.1610	8300	0.0166	-	-
0.1620	8350	0.0225	-	-
0.1629	8400	0.0225	-	-
0.1639	8450	0.0261	-	-
0.1649	8500	0.0242	-	-
0.1659	8550	0.0231	-	-
0.1668	8600	0.0256	-	-
0.1678	8650	0.0248	-	-
0.1688	8700	0.0344	-	-
0.1697	8750	0.0273	-	-
0.1707	8800	0.0262	-	-
0.1717	8850	0.0256	-	-
0.1726	8900	0.0278	-	-
0.1736	8950	0.0186	-	-
0.1746	9000	0.0316	-	-
0.1756	9050	0.0244	-	-
0.1765	9100	0.0198	-	-
0.1775	9150	0.0261	-	-
0.1785	9200	0.0257	-	-
0.1794	9250	0.0254	-	-
0.1804	9300	0.0171	-	-
0.1814	9350	0.0283	-	-
0.1823	9400	0.0242	-	-
0.1833	9450	0.0236	-	-
0.1843	9500	0.0263	-	-
0.1852	9550	0.0267	-	-
0.1862	9600	0.0201	-	-
0.1872	9650	0.0193	-	-
0.1882	9700	0.0264	-	-
0.1891	9750	0.0247	-	-
0.1901	9800	0.0230	-	-
0.1911	9850	0.0176	-	-
0.1920	9900	0.0232	-	-
0.1930	9950	0.0177	-	-
0.1940	10000	0.0241	-	-
0.1949	10050	0.0166	-	-
0.1959	10100	0.0213	-	-
0.1969	10150	0.0232	-	-
0.1979	10200	0.0292	-	-
0.1988	10250	0.0216	-	-
0.1998	10300	0.0203	-	-
0.2008	10350	0.0180	-	-
0.2017	10400	0.0228	-	-
0.2027	10450	0.0215	-	-
0.2037	10500	0.0244	-	-
0.2046	10550	0.0268	-	-
0.2056	10600	0.0279	-	-
0.2066	10650	0.0227	-	-
0.2076	10700	0.0279	-	-
0.2085	10750	0.0195	-	-
0.2095	10800	0.0237	-	-
0.2105	10850	0.0222	-	-
0.2114	10900	0.0174	-	-
0.2124	10950	0.0131	-	-
0.2134	11000	0.0420	-	-
0.2143	11050	0.0204	-	-
0.2153	11100	0.0186	-	-
0.2163	11150	0.0359	-	-
0.2173	11200	0.0275	-	-
0.2182	11250	0.0270	-	-
0.2192	11300	0.0316	-	-
0.2202	11350	0.0254	-	-
0.2211	11400	0.0256	-	-
0.2221	11450	0.0210	-	-
0.2231	11500	0.0175	-	-
0.2240	11550	0.0255	-	-
0.2250	11600	0.0183	-	-
0.2260	11650	0.0187	-	-
0.2270	11700	0.0250	-	-
0.2279	11750	0.0198	-	-
0.2289	11800	0.0228	-	-
0.2299	11850	0.0230	-	-
0.2308	11900	0.0277	-	-
0.2318	11950	0.0222	-	-
0.2328	12000	0.0243	-	-
0.2337	12050	0.0241	-	-
0.2347	12100	0.0213	-	-
0.2357	12150	0.0120	-	-
0.2367	12200	0.0291	-	-
0.2376	12250	0.0207	-	-
0.2386	12300	0.0262	-	-
0.2396	12350	0.0232	-	-
0.2405	12400	0.0173	-	-
0.2415	12450	0.0306	-	-
0.2425	12500	0.0126	-	-
0.2434	12550	0.0323	-	-
0.2444	12600	0.0195	-	-
0.2454	12650	0.0153	-	-
0.2464	12700	0.0282	-	-
0.2473	12750	0.0252	-	-
0.2483	12800	0.0214	-	-
0.2493	12850	0.0234	-	-
0.2502	12900	0.0215	-	-
0.2512	12950	0.0172	-	-
0.2522	13000	0.0192	-	-
0.2531	13050	0.0160	-	-
0.2541	13100	0.0254	-	-
0.2551	13150	0.0163	-	-
0.2561	13200	0.0150	-	-
0.2570	13250	0.0231	-	-
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1.0000	51550	0.0110	-	-
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1.0310	53150	0.0090	-	-
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1.0640	54850	0.0141	-	-
1.0649	54900	0.0103	-	-
1.0659	54950	0.0090	-	-
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1.0960	56500	0.0101	-	-
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1.0999	56700	0.0111	-	-
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1.1047	56950	0.0096	-	-
1.1057	57000	0.0101	-	-
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1.1076	57100	0.0097	-	-
1.1086	57150	0.0097	-	-
1.1096	57200	0.0065	-	-
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1.1115	57300	0.0101	-	-
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1.1144	57450	0.0086	-	-
1.1154	57500	0.0125	-	-
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1.1338	58450	0.0104	-	-
1.1348	58500	0.0074	-	-
1.1357	58550	0.0101	-	-
1.1367	58600	0.0129	-	-
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1.2027	62000	0.0063	-	-
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1.4364	74050	0.0125	-	-
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1.4393	74200	0.0090	-	-
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1.4723	75900	0.0090	-	-
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1.4752	76050	0.0090	-	-
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1.4849	76550	0.0090	-	-
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1.5480	79800	0.0090	-	-
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1.5751	81200	0.0060	-	-
1.5761	81250	0.0111	-	-
1.5770	81300	0.0134	-	-
1.5780	81350	0.0089	-	-
1.5790	81400	0.0109	-	-
1.5800	81450	0.0089	-	-
1.5809	81500	0.0108	-	-
1.5819	81550	0.0098	-	-
1.5829	81600	0.0145	-	-
1.5838	81650	0.0096	-	-
1.5848	81700	0.0099	-	-
1.5858	81750	0.0110	-	-
1.5867	81800	0.0074	-	-
1.5877	81850	0.0070	-	-
1.5887	81900	0.0138	-	-
1.5897	81950	0.0099	-	-
1.5906	82000	0.0113	-	-
1.5916	82050	0.0106	-	-
1.5926	82100	0.0078	-	-
1.5935	82150	0.0061	-	-
1.5945	82200	0.0048	-	-
1.5955	82250	0.0097	-	-
1.5964	82300	0.0095	-	-
1.5974	82350	0.0144	-	-
1.5984	82400	0.0081	-	-
1.5994	82450	0.0119	-	-
1.6003	82500	0.0117	-	-
1.6013	82550	0.0114	-	-
1.6023	82600	0.0086	-	-
1.6032	82650	0.0098	-	-
1.6042	82700	0.0118	-	-
1.6052	82750	0.0088	-	-
1.6061	82800	0.0062	-	-
1.6071	82850	0.0128	-	-
1.6081	82900	0.0100	-	-
1.6091	82950	0.0093	-	-
1.6100	83000	0.0119	-	-
1.6110	83050	0.0114	-	-
1.6120	83100	0.0063	-	-
1.6129	83150	0.0086	-	-
1.6139	83200	0.0082	-	-
1.6149	83250	0.0090	-	-
1.6158	83300	0.0087	-	-
1.6168	83350	0.0093	-	-
1.6178	83400	0.0079	-	-
1.6188	83450	0.0081	-	-
1.6197	83500	0.0073	-	-
1.6207	83550	0.0081	-	-
1.6217	83600	0.0102	-	-
1.6226	83650	0.0102	-	-
1.6236	83700	0.0106	-	-
1.6246	83750	0.0083	-	-
1.6255	83800	0.0122	-	-
1.6265	83850	0.0078	-	-
1.6275	83900	0.0086	-	-
1.6285	83950	0.0098	-	-
1.6294	84000	0.0071	-	-
1.6304	84050	0.0068	-	-
1.6314	84100	0.0074	-	-
1.6323	84150	0.0070	-	-
1.6333	84200	0.0149	-	-
1.6343	84250	0.0103	-	-
1.6352	84300	0.0083	-	-
1.6362	84350	0.0084	-	-
1.6372	84400	0.0117	-	-
1.6382	84450	0.0089	-	-
1.6391	84500	0.0120	-	-
1.6401	84550	0.0117	-	-
1.6411	84600	0.0085	-	-
1.6420	84650	0.0067	-	-
1.6430	84700	0.0107	-	-
1.6440	84750	0.0101	-	-
1.6449	84800	0.0087	-	-
1.6459	84850	0.0085	-	-
1.6469	84900	0.0078	-	-
1.6479	84950	0.0074	-	-
1.6488	85000	0.0115	-	-
1.6498	85050	0.0074	-	-
1.6508	85100	0.0081	-	-
1.6517	85150	0.0103	-	-
1.6527	85200	0.0084	-	-
1.6537	85250	0.0095	-	-
1.6546	85300	0.0100	-	-
1.6556	85350	0.0076	-	-
1.6566	85400	0.0069	-	-
1.6575	85450	0.0097	-	-
1.6585	85500	0.0087	-	-
1.6595	85550	0.0102	-	-
1.6605	85600	0.0095	-	-
1.6614	85650	0.0077	-	-
1.6624	85700	0.0063	-	-
1.6634	85750	0.0075	-	-
1.6643	85800	0.0052	-	-
1.6653	85850	0.0085	-	-
1.6663	85900	0.0082	-	-
1.6672	85950	0.0115	-	-
1.6682	86000	0.0059	-	-
1.6692	86050	0.0057	-	-
1.6702	86100	0.0090	-	-
1.6711	86150	0.0105	-	-
1.6721	86200	0.0104	-	-
1.6731	86250	0.0077	-	-
1.6740	86300	0.0119	-	-
1.6750	86350	0.0051	-	-
1.6760	86400	0.0070	-	-
1.6769	86450	0.0065	-	-
1.6779	86500	0.0104	-	-
1.6789	86550	0.0092	-	-
1.6799	86600	0.0076	-	-
1.6808	86650	0.0090	-	-
1.6818	86700	0.0115	-	-
1.6828	86750	0.0133	-	-
1.6837	86800	0.0074	-	-
1.6847	86850	0.0081	-	-
1.6857	86900	0.0068	-	-
1.6866	86950	0.0060	-	-
1.6876	87000	0.0063	-	-
1.6886	87050	0.0097	-	-
1.6896	87100	0.0135	-	-
1.6905	87150	0.0078	-	-
1.6915	87200	0.0094	-	-
1.6925	87250	0.0111	-	-
1.6934	87300	0.0095	-	-
1.6944	87350	0.0101	-	-
1.6954	87400	0.0076	-	-
1.6963	87450	0.0081	-	-
1.6973	87500	0.0090	-	-
1.6983	87550	0.0090	-	-
1.6993	87600	0.0088	-	-
1.7002	87650	0.0076	-	-
1.7012	87700	0.0064	-	-
1.7022	87750	0.0122	-	-
1.7031	87800	0.0108	-	-
1.7041	87850	0.0086	-	-
1.7051	87900	0.0114	-	-
1.7060	87950	0.0119	-	-
1.7070	88000	0.0070	-	-
1.7080	88050	0.0105	-	-
1.7090	88100	0.0103	-	-
1.7099	88150	0.0080	-	-
1.7109	88200	0.0121	-	-
1.7119	88250	0.0068	-	-
1.7128	88300	0.0087	-	-
1.7138	88350	0.0080	-	-
1.7148	88400	0.0091	-	-
1.7157	88450	0.0084	-	-
1.7167	88500	0.0076	-	-
1.7177	88550	0.0047	-	-
1.7187	88600	0.0182	-	-
1.7196	88650	0.0126	-	-
1.7206	88700	0.0121	-	-
1.7216	88750	0.0081	-	-
1.7225	88800	0.0092	-	-
1.7235	88850	0.0088	-	-
1.7245	88900	0.0079	-	-
1.7254	88950	0.0093	-	-
1.7264	89000	0.0093	-	-
1.7274	89050	0.0081	-	-
1.7284	89100	0.0072	-	-
1.7293	89150	0.0066	-	-
1.7303	89200	0.0078	-	-
1.7313	89250	0.0087	-	-
1.7322	89300	0.0067	-	-
1.7332	89350	0.0082	-	-
1.7342	89400	0.0066	-	-
1.7351	89450	0.0094	-	-
1.7361	89500	0.0083	-	-
1.7371	89550	0.0054	-	-
1.7381	89600	0.0107	-	-
1.7390	89650	0.0086	-	-
1.7400	89700	0.0101	-	-
1.7410	89750	0.0061	-	-
1.7419	89800	0.0079	-	-
1.7429	89850	0.0057	-	-
1.7439	89900	0.0095	-	-
1.7448	89950	0.0056	-	-
1.7458	90000	0.0064	-	-
1.7468	90050	0.0106	-	-
1.7477	90100	0.0126	-	-
1.7487	90150	0.0053	-	-
1.7497	90200	0.0044	-	-
1.7507	90250	0.0054	-	-
1.7516	90300	0.0103	-	-
1.7526	90350	0.0151	-	-
1.7536	90400	0.0079	-	-
1.7545	90450	0.0071	-	-
1.7555	90500	0.0073	-	-
1.7565	90550	0.0118	-	-
1.7574	90600	0.0054	-	-
1.7584	90650	0.0083	-	-
1.7594	90700	0.0108	-	-
1.7604	90750	0.0132	-	-
1.7613	90800	0.0066	-	-
1.7623	90850	0.0104	-	-
1.7633	90900	0.0063	-	-
1.7642	90950	0.0074	-	-
1.7652	91000	0.0091	-	-
1.7662	91050	0.0122	-	-
1.7671	91100	0.0098	-	-
1.7681	91150	0.0051	-	-
1.7691	91200	0.0079	-	-
1.7701	91250	0.0060	-	-
1.7710	91300	0.0088	-	-
1.7720	91350	0.0056	-	-
1.7730	91400	0.0116	-	-
1.7739	91450	0.0093	-	-
1.7749	91500	0.0132	-	-
1.7759	91550	0.0075	-	-
1.7768	91600	0.0065	-	-
1.7778	91650	0.0165	-	-
1.7788	91700	0.0092	-	-
1.7798	91750	0.0067	-	-
1.7807	91800	0.0038	-	-
1.7817	91850	0.0086	-	-
1.7827	91900	0.0105	-	-
1.7836	91950	0.0075	-	-
1.7846	92000	0.0104	-	-
1.7856	92050	0.0063	-	-
1.7865	92100	0.0096	-	-
1.7875	92150	0.0105	-	-
1.7885	92200	0.0063	-	-
1.7895	92250	0.0101	-	-
1.7904	92300	0.0102	-	-
1.7914	92350	0.0087	-	-
1.7924	92400	0.0121	-	-
1.7933	92450	0.0087	-	-
1.7943	92500	0.0070	-	-
1.7953	92550	0.0053	-	-
1.7962	92600	0.0102	-	-
1.7972	92650	0.0155	-	-
1.7982	92700	0.0088	-	-
1.7992	92750	0.0100	-	-
1.8001	92800	0.0063	-	-
1.8011	92850	0.0096	-	-
1.8021	92900	0.0150	-	-
1.8030	92950	0.0082	-	-
1.8040	93000	0.0083	-	-
1.8050	93050	0.0106	-	-
1.8059	93100	0.0073	-	-
1.8069	93150	0.0121	-	-
1.8079	93200	0.0055	-	-
1.8089	93250	0.0101	-	-
1.8098	93300	0.0088	-	-
1.8108	93350	0.0084	-	-
1.8118	93400	0.0062	-	-
1.8127	93450	0.0034	-	-
1.8137	93500	0.0060	-	-
1.8147	93550	0.0113	-	-
1.8156	93600	0.0087	-	-
1.8166	93650	0.0092	-	-
1.8176	93700	0.0104	-	-
1.8186	93750	0.0131	-	-
1.8195	93800	0.0085	-	-
1.8205	93850	0.0089	-	-
1.8215	93900	0.0081	-	-
1.8224	93950	0.0084	-	-
1.8234	94000	0.0102	-	-
1.8244	94050	0.0065	-	-
1.8253	94100	0.0088	-	-
1.8263	94150	0.0120	-	-
1.8273	94200	0.0098	-	-
1.8283	94250	0.0114	-	-
1.8292	94300	0.0116	-	-
1.8302	94350	0.0100	-	-
1.8312	94400	0.0133	-	-
1.8321	94450	0.0064	-	-
1.8331	94500	0.0066	-	-
1.8341	94550	0.0095	-	-
1.8350	94600	0.0086	-	-
1.8360	94650	0.0105	-	-
1.8370	94700	0.0073	-	-
1.8380	94750	0.0089	-	-
1.8389	94800	0.0090	-	-
1.8399	94850	0.0079	-	-
1.8409	94900	0.0073	-	-
1.8418	94950	0.0057	-	-
1.8428	95000	0.0120	-	-
1.8438	95050	0.0101	-	-
1.8447	95100	0.0060	-	-
1.8457	95150	0.0112	-	-
1.8467	95200	0.0036	-	-
1.8476	95250	0.0086	-	-
1.8486	95300	0.0051	-	-
1.8496	95350	0.0114	-	-
1.8506	95400	0.0117	-	-
1.8515	95450	0.0051	-	-
1.8525	95500	0.0077	-	-
1.8535	95550	0.0065	-	-
1.8544	95600	0.0086	-	-
1.8554	95650	0.0080	-	-
1.8564	95700	0.0072	-	-
1.8573	95750	0.0071	-	-
1.8583	95800	0.0080	-	-
1.8593	95850	0.0126	-	-
1.8603	95900	0.0115	-	-
1.8612	95950	0.0073	-	-
1.8622	96000	0.0079	-	-
1.8632	96050	0.0111	-	-
1.8641	96100	0.0089	-	-
1.8651	96150	0.0127	-	-
1.8661	96200	0.0052	-	-
1.8670	96250	0.0065	-	-
1.8680	96300	0.0106	-	-
1.8690	96350	0.0074	-	-
1.8700	96400	0.0091	-	-
1.8709	96450	0.0079	-	-
1.8719	96500	0.0090	-	-
1.8729	96550	0.0059	-	-
1.8738	96600	0.0081	-	-
1.8748	96650	0.0088	-	-
1.8758	96700	0.0063	-	-
1.8767	96750	0.0057	-	-
1.8777	96800	0.0092	-	-
1.8787	96850	0.0070	-	-
1.8797	96900	0.0045	-	-
1.8806	96950	0.0051	-	-
1.8816	97000	0.0078	-	-
1.8826	97050	0.0061	-	-
1.8835	97100	0.0126	-	-
1.8845	97150	0.0058	-	-
1.8855	97200	0.0100	-	-
1.8864	97250	0.0067	-	-
1.8874	97300	0.0122	-	-
1.8884	97350	0.0078	-	-
1.8894	97400	0.0069	-	-
1.8903	97450	0.0105	-	-
1.8913	97500	0.0116	-	-
1.8923	97550	0.0106	-	-
1.8932	97600	0.0121	-	-
1.8942	97650	0.0126	-	-
1.8952	97700	0.0051	-	-
1.8961	97750	0.0117	-	-
1.8971	97800	0.0055	-	-
1.8981	97850	0.0093	-	-
1.8991	97900	0.0069	-	-
1.9000	97950	0.0094	-	-
1.9010	98000	0.0056	-	-
1.9020	98050	0.0085	-	-
1.9029	98100	0.0106	-	-
1.9039	98150	0.0125	-	-
1.9049	98200	0.0074	-	-
1.9058	98250	0.0077	-	-
1.9068	98300	0.0092	-	-
1.9078	98350	0.0087	-	-
1.9088	98400	0.0109	-	-
1.9097	98450	0.0103	-	-
1.9107	98500	0.0066	-	-
1.9117	98550	0.0132	-	-
1.9126	98600	0.0079	-	-
1.9136	98650	0.0115	-	-
1.9146	98700	0.0114	-	-
1.9155	98750	0.0118	-	-
1.9165	98800	0.0069	-	-
1.9175	98850	0.0079	-	-
1.9185	98900	0.0082	-	-
1.9194	98950	0.0065	-	-
1.9204	99000	0.0073	-	-
1.9214	99050	0.0097	-	-
1.9223	99100	0.0064	-	-
1.9233	99150	0.0114	-	-
1.9243	99200	0.0098	-	-
1.9252	99250	0.0086	-	-
1.9262	99300	0.0058	-	-
1.9272	99350	0.0092	-	-
1.9282	99400	0.0076	-	-
1.9291	99450	0.0071	-	-
1.9301	99500	0.0076	-	-
1.9311	99550	0.0087	-	-
1.9320	99600	0.0052	-	-
1.9330	99650	0.0094	-	-
1.9340	99700	0.0084	-	-
1.9349	99750	0.0102	-	-
1.9359	99800	0.0117	-	-
1.9369	99850	0.0092	-	-
1.9378	99900	0.0106	-	-
1.9388	99950	0.0112	-	-
1.9398	100000	0.0086	-	-
1.9408	100050	0.0059	-	-
1.9417	100100	0.0086	-	-
1.9427	100150	0.0067	-	-
1.9437	100200	0.0097	-	-
1.9446	100250	0.0076	-	-
1.9456	100300	0.0094	-	-
1.9466	100350	0.0087	-	-
1.9475	100400	0.0108	-	-
1.9485	100450	0.0064	-	-
1.9495	100500	0.0070	-	-
1.9505	100550	0.0066	-	-
1.9514	100600	0.0071	-	-
1.9524	100650	0.0081	-	-
1.9534	100700	0.0086	-	-
1.9543	100750	0.0085	-	-
1.9553	100800	0.0059	-	-
1.9563	100850	0.0106	-	-
1.9572	100900	0.0089	-	-
1.9582	100950	0.0090	-	-
1.9592	101000	0.0074	-	-
1.9602	101050	0.0125	-	-
1.9611	101100	0.0084	-	-
1.9621	101150	0.0080	-	-
1.9631	101200	0.0069	-	-
1.9640	101250	0.0091	-	-
1.9650	101300	0.0128	-	-
1.9660	101350	0.0100	-	-
1.9669	101400	0.0098	-	-
1.9679	101450	0.0117	-	-
1.9689	101500	0.0108	-	-
1.9699	101550	0.0097	-	-
1.9708	101600	0.0106	-	-
1.9718	101650	0.0085	-	-
1.9728	101700	0.0110	-	-
1.9737	101750	0.0124	-	-
1.9747	101800	0.0092	-	-
1.9757	101850	0.0102	-	-
1.9766	101900	0.0080	-	-
1.9776	101950	0.0073	-	-
1.9786	102000	0.0105	-	-
1.9796	102050	0.0048	-	-
1.9805	102100	0.0090	-	-
1.9815	102150	0.0071	-	-
1.9825	102200	0.0056	-	-
1.9834	102250	0.0070	-	-
1.9844	102300	0.0113	-	-
1.9854	102350	0.0082	-	-
1.9863	102400	0.0065	-	-
1.9873	102450	0.0092	-	-
1.9883	102500	0.0075	-	-
1.9893	102550	0.0080	-	-
1.9902	102600	0.0067	-	-
1.9912	102650	0.0059	-	-
1.9922	102700	0.0067	-	-
1.9931	102750	0.0092	-	-
1.9941	102800	0.0144	-	-
1.9951	102850	0.0086	-	-
1.9960	102900	0.0109	-	-
1.9970	102950	0.0090	-	-
1.9980	103000	0.0079	-	-
1.9990	103050	0.0099	-	-
1.9999	103100	0.0069	-	-
2.0	103104	-	0.0086	0.9996

• The bold row denotes the saved checkpoint.

Training Time

• Training: 2.3 hours
• Evaluation: 10.5 minutes
• Total: 2.5 hours

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 6.1.0
• Transformers: 5.17.0
• PyTorch: 2.14.0+cu130
• Accelerate: 1.15.0
• Datasets: 5.0.1
• Tokenizers: 0.23.2

Additional Resources

• Training and Finetuning Reranker Models with Sentence Transformers: the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video reranker models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: training multimodal Cross Encoders.

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",
}