CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2

This is a Cross Encoder model finetuned from cross-encoder/ms-marco-MiniLM-L6-v2 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 Sources

Full Model Architecture

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

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("kelompoknlp2026dsindo/reranker_260511070554_FINAL")
# Get scores for pairs of inputs
pairs = [
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'Since global warming is attributed to increasing atmospheric concentrations of greenhouse gases such as CO 2 and methane, scientists closely monitor atmospheric CO 2 concentrations and their impact on the present-day biosphere.'],
    ['Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.', 'While a number of human-derived factors are recognized as contributing to rising atmospheric concentrations of CH (methane) and CO (carbon dioxide), deforestation and territorial clearance practices associated with agricultural development may be contributing most to these concentrations globally.'],
]
scores = model.predict(pairs)
print(scores)
# [ 6.3068 -1.1184 -1.8801 -5.2732 -8.7795]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.',
    [
        'Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.',
        'Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.',
        'At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.',
        'Since global warming is attributed to increasing atmospheric concentrations of greenhouse gases such as CO 2 and methane, scientists closely monitor atmospheric CO 2 concentrations and their impact on the present-day biosphere.',
        'While a number of human-derived factors are recognized as contributing to rising atmospheric concentrations of CH (methane) and CO (carbon dioxide), deforestation and territorial clearance practices associated with agricultural development may be contributing most to these concentrations globally.',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Metric Value
map 0.3398 (+0.0771)
mrr@5 0.4296 (+0.0643)
ndcg@5 0.3415 (+0.0607)

Training Details

Training Dataset

Unnamed Dataset

  • Size: 201,705 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2 label
    type string string float
    details
    • min: 13 tokens
    • mean: 27.17 tokens
    • max: 45 tokens
    • min: 6 tokens
    • mean: 34.3 tokens
    • max: 112 tokens
    • min: 0.0
    • mean: 0.02
    • max: 1.0
  • Samples:
    sentence1 sentence2 label
    Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. Higher carbon dioxide concentrations will favourably affect plant growth and demand for water. 1.0
    Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients. 1.0
    Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life. At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse. 1.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
        "activation_fn": "torch.nn.modules.linear.Identity",
        "pos_weight": 64.0
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 16
  • num_train_epochs: 20
  • learning_rate: 1e-05
  • warmup_steps: 100
  • per_device_eval_batch_size: 16
  • load_best_model_at_end: True
  • data_seed: 42

All Hyperparameters

Click to expand
  • per_device_train_batch_size: 16
  • num_train_epochs: 20
  • max_steps: -1
  • learning_rate: 1e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 100
  • 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: trackio
  • per_device_eval_batch_size: 16
  • 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: 42
  • 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
  • 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_backend: None
  • ddp_timeout: 1800
  • fsdp: []
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • deepspeed: None
  • debug: []
  • skip_memory_metrics: True
  • do_predict: False
  • resume_from_checkpoint: None
  • warmup_ratio: None
  • local_rank: -1
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss dev_ndcg@5
0.0040 50 6.4821 -
0.0079 100 7.7075 -
0.0119 150 5.6951 -
0.0159 200 10.5163 -
0.0198 250 5.7443 -
0.0238 300 9.2841 -
0.0278 350 7.3247 -
0.0317 400 7.5603 -
0.0357 450 6.0052 -
0.0397 500 6.7443 -
0.0436 550 7.0512 -
0.0476 600 6.1199 -
0.0516 650 5.4322 -
0.0555 700 5.1746 -
0.0595 750 4.4309 -
0.0635 800 4.8644 -
0.0674 850 6.8323 -
0.0714 900 6.3006 -
0.0754 950 7.8413 -
0.0793 1000 7.4447 -
0.0833 1050 4.6336 -
0.0873 1100 5.6150 -
0.0912 1150 5.1068 -
0.0952 1200 6.6468 -
0.0992 1250 4.7122 -
0.1031 1300 5.8467 -
0.1071 1350 3.5123 -
0.1110 1400 5.4432 -
0.1150 1450 4.5448 -
0.1190 1500 4.1719 -
0.1229 1550 4.8506 -
0.1269 1600 5.6615 -
0.1309 1650 7.3819 -
0.1348 1700 3.3712 -
0.1388 1750 6.2910 -
0.1428 1800 4.3081 -
0.1467 1850 7.4247 -
0.1507 1900 4.0344 -
0.1547 1950 3.9636 -
0.1586 2000 6.0031 -
0.1626 2050 6.0784 -
0.1666 2100 4.1477 -
0.1705 2150 5.8603 -
0.1745 2200 5.6526 -
0.1785 2250 6.8740 -
0.1824 2300 3.4526 -
0.1864 2350 4.9571 -
0.1904 2400 6.5555 -
0.1943 2450 4.2477 -
0.1983 2500 4.6370 -
0.2023 2550 3.4837 -
0.2062 2600 5.1507 -
0.2102 2650 5.5580 -
0.2142 2700 5.0514 -
0.2181 2750 3.7364 -
0.2221 2800 5.0521 -
0.2261 2850 4.5580 -
0.2300 2900 4.6881 -
0.2340 2950 5.9068 -
0.2380 3000 4.3156 -
0.2419 3050 5.3075 -
0.2459 3100 4.2023 -
0.2499 3150 6.6939 -
0.2538 3200 4.8161 -
0.2578 3250 3.2366 -
0.2618 3300 6.1945 -
0.2657 3350 3.8320 -
0.2697 3400 4.8402 -
0.2737 3450 4.1905 -
0.2776 3500 4.3157 -
0.2816 3550 7.2777 -
0.2856 3600 4.5789 -
0.2895 3650 4.7533 -
0.2935 3700 4.6230 -
0.2975 3750 6.1926 -
0.3014 3800 3.6712 -
0.3054 3850 5.5557 -
0.3094 3900 3.0784 -
0.3133 3950 6.3366 -
0.3173 4000 4.6509 -
0.3213 4050 8.0496 -
0.3252 4100 4.2992 -
0.3292 4150 5.4489 -
0.3331 4200 4.3801 -
0.3371 4250 6.7844 -
0.3411 4300 3.6825 -
0.3450 4350 3.9890 -
0.3490 4400 4.3086 -
0.3530 4450 3.6097 -
0.3569 4500 4.6685 -
0.3609 4550 6.4267 -
0.3649 4600 4.0074 -
0.3688 4650 4.4316 -
0.3728 4700 4.8316 -
0.3768 4750 7.0559 -
0.3807 4800 4.0149 -
0.3847 4850 4.4566 -
0.3887 4900 6.1702 -
0.3926 4950 4.7792 -
0.3966 5000 5.1080 -
0.4006 5050 3.8074 -
0.4045 5100 4.8827 -
0.4085 5150 4.9390 -
0.4125 5200 6.6034 -
0.4164 5250 6.0701 -
0.4204 5300 7.2862 -
0.4244 5350 4.2337 -
0.4283 5400 5.1675 -
0.4323 5450 6.6641 -
0.4363 5500 4.5362 -
0.4402 5550 5.1396 -
0.4442 5600 4.6001 -
0.4482 5650 6.3244 -
0.4521 5700 5.9137 -
0.4561 5750 5.4712 -
0.4601 5800 4.6245 -
0.4640 5850 3.4703 -
0.4680 5900 4.9829 -
0.4720 5950 4.6653 -
0.4759 6000 5.6183 -
0.4799 6050 4.8219 -
0.4839 6100 4.7788 -
0.4878 6150 5.2348 -
0.4918 6200 3.9210 -
0.4958 6250 3.9270 -
0.4997 6300 6.1260 -
0.5037 6350 2.5234 -
0.5077 6400 5.2665 -
0.5116 6450 5.7745 -
0.5156 6500 3.7769 -
0.5196 6550 3.8054 -
0.5235 6600 6.1702 -
0.5275 6650 4.3505 -
0.5315 6700 3.9904 -
0.5354 6750 4.2700 -
0.5394 6800 3.9557 -
0.5433 6850 4.7736 -
0.5473 6900 6.1473 -
0.5513 6950 5.2114 -
0.5552 7000 2.8830 -
0.5592 7050 4.5842 -
0.5632 7100 4.4275 -
0.5671 7150 3.9822 -
0.5711 7200 4.9718 -
0.5751 7250 4.7388 -
0.5790 7300 5.8294 -
0.5830 7350 3.3903 -
0.5870 7400 4.5565 -
0.5909 7450 4.9556 -
0.5949 7500 5.5714 -
0.5989 7550 6.3889 -
0.6028 7600 5.8972 -
0.6068 7650 5.1257 -
0.6108 7700 6.0912 -
0.6147 7750 4.7975 -
0.6187 7800 5.0368 -
0.6227 7850 4.8840 -
0.6266 7900 5.8869 -
0.6306 7950 3.0794 -
0.6346 8000 4.4715 -
0.6385 8050 4.9903 -
0.6425 8100 4.5976 -
0.6465 8150 6.4560 -
0.6504 8200 6.0488 -
0.6544 8250 4.7396 -
0.6584 8300 4.4041 -
0.6623 8350 4.3758 -
0.6663 8400 5.0536 -
0.6703 8450 3.3633 -
0.6742 8500 3.8285 -
0.6782 8550 6.1229 -
0.6822 8600 6.6120 -
0.6861 8650 5.6673 -
0.6901 8700 3.1354 -
0.6941 8750 6.2316 -
0.6980 8800 5.5706 -
0.7020 8850 3.8595 -
0.7060 8900 3.2719 -
0.7099 8950 6.3340 -
0.7139 9000 4.0074 -
0.7179 9050 6.2992 -
0.7218 9100 4.6812 -
0.7258 9150 6.1453 -
0.7298 9200 6.1280 -
0.7337 9250 3.0419 -
0.7377 9300 3.4481 -
0.7417 9350 6.3920 -
0.7456 9400 6.5825 -
0.7496 9450 4.0374 -
0.7535 9500 3.6124 -
0.7575 9550 3.9780 -
0.7615 9600 4.7214 -
0.7654 9650 5.9004 -
0.7694 9700 3.2530 -
0.7734 9750 5.6337 -
0.7773 9800 5.7635 -
0.7813 9850 6.2136 -
0.7853 9900 6.8707 -
0.7892 9950 4.1602 -
0.7932 10000 5.5243 -
0.7972 10050 5.8999 -
0.8011 10100 5.4308 -
0.8051 10150 4.2716 -
0.8091 10200 5.1036 -
0.8130 10250 4.1576 -
0.8170 10300 5.3179 -
0.8210 10350 3.9488 -
0.8249 10400 4.9290 -
0.8289 10450 5.9599 -
0.8329 10500 4.1004 -
0.8368 10550 7.0388 -
0.8408 10600 4.5892 -
0.8448 10650 5.7796 -
0.8487 10700 4.8321 -
0.8527 10750 3.2001 -
0.8567 10800 5.1913 -
0.8606 10850 4.2873 -
0.8646 10900 6.6408 -
0.8686 10950 2.9361 -
0.8725 11000 6.4077 -
0.8765 11050 4.0997 -
0.8805 11100 4.9250 -
0.8844 11150 4.0123 -
0.8884 11200 7.3905 -
0.8924 11250 3.9799 -
0.8963 11300 5.3688 -
0.9003 11350 5.7663 -
0.9043 11400 3.8526 -
0.9082 11450 3.3329 -
0.9122 11500 8.4383 -
0.9162 11550 5.9832 -
0.9201 11600 5.6354 -
0.9241 11650 5.2467 -
0.9281 11700 6.6728 -
0.9320 11750 4.4470 -
0.9360 11800 6.1355 -
0.9400 11850 7.2139 -
0.9439 11900 6.8020 -
0.9479 11950 5.9080 -
0.9519 12000 3.6145 -
0.9558 12050 5.7505 -
0.9598 12100 5.1652 -
0.9638 12150 3.0675 -
0.9677 12200 6.3900 -
0.9717 12250 3.9269 -
0.9756 12300 3.4955 -
0.9796 12350 5.1035 -
0.9836 12400 5.6212 -
0.9875 12450 4.1978 -
0.9915 12500 6.0696 -
0.9955 12550 4.8799 -
0.9994 12600 5.4902 -
1.0 12607 - 0.3860 (+0.1052)
1.0034 12650 3.1473 -
1.0074 12700 3.6294 -
1.0113 12750 3.9964 -
1.0153 12800 3.8462 -
1.0193 12850 6.3427 -
1.0232 12900 6.8488 -
1.0272 12950 3.7193 -
1.0312 13000 4.9732 -
1.0351 13050 4.8782 -
1.0391 13100 3.0684 -
1.0431 13150 4.0655 -
1.0470 13200 3.2872 -
1.0510 13250 5.6441 -
1.0550 13300 5.8195 -
1.0589 13350 4.5267 -
1.0629 13400 3.1802 -
1.0669 13450 2.9212 -
1.0708 13500 2.7601 -
1.0748 13550 2.7879 -
1.0788 13600 6.5307 -
1.0827 13650 4.8398 -
1.0867 13700 4.1978 -
1.0907 13750 5.5264 -
1.0946 13800 5.1138 -
1.0986 13850 5.0489 -
1.1026 13900 3.7149 -
1.1065 13950 5.7103 -
1.1105 14000 3.4166 -
1.1145 14050 3.6008 -
1.1184 14100 4.9199 -
1.1224 14150 3.9131 -
1.1264 14200 6.0467 -
1.1303 14250 5.1233 -
1.1343 14300 4.6286 -
1.1383 14350 4.1228 -
1.1422 14400 7.6400 -
1.1462 14450 3.3461 -
1.1502 14500 5.6852 -
1.1541 14550 3.7182 -
1.1581 14600 5.2190 -
1.1621 14650 4.8189 -
1.1660 14700 5.2080 -
1.1700 14750 4.2439 -
1.1740 14800 2.2412 -
1.1779 14850 7.6880 -
1.1819 14900 4.5218 -
1.1858 14950 4.4288 -
1.1898 15000 4.5590 -
1.1938 15050 4.5896 -
1.1977 15100 5.5480 -
1.2017 15150 2.4528 -
1.2057 15200 6.3474 -
1.2096 15250 4.1114 -
1.2136 15300 4.2226 -
1.2176 15350 5.7683 -
1.2215 15400 5.2831 -
1.2255 15450 4.2796 -
1.2295 15500 4.8112 -
1.2334 15550 3.8737 -
1.2374 15600 7.2412 -
1.2414 15650 3.0813 -
1.2453 15700 5.4860 -
1.2493 15750 3.2688 -
1.2533 15800 4.4923 -
1.2572 15850 6.5222 -
1.2612 15900 5.9631 -
1.2652 15950 5.0729 -
1.2691 16000 5.6545 -
1.2731 16050 2.8809 -
1.2771 16100 6.1022 -
1.2810 16150 4.9460 -
1.2850 16200 4.6006 -
1.2890 16250 3.9895 -
1.2929 16300 4.4194 -
1.2969 16350 5.8219 -
1.3009 16400 6.3086 -
1.3048 16450 4.6904 -
1.3088 16500 4.0247 -
1.3128 16550 6.4729 -
1.3167 16600 4.1359 -
1.3207 16650 4.8131 -
1.3247 16700 5.0250 -
1.3286 16750 5.3243 -
1.3326 16800 6.2515 -
1.3366 16850 3.8675 -
1.3405 16900 4.7660 -
1.3445 16950 4.3193 -
1.3485 17000 6.3244 -
1.3524 17050 3.8366 -
1.3564 17100 4.4866 -
1.3604 17150 5.9731 -
1.3643 17200 4.9215 -
1.3683 17250 4.6363 -
1.3723 17300 4.1654 -
1.3762 17350 5.1810 -
1.3802 17400 6.8538 -
1.3842 17450 6.2454 -
1.3881 17500 4.9535 -
1.3921 17550 4.6337 -
1.3960 17600 4.6240 -
1.4000 17650 5.0683 -
1.4040 17700 5.0473 -
1.4079 17750 4.2704 -
1.4119 17800 3.2880 -
1.4159 17850 6.1430 -
1.4198 17900 5.1836 -
1.4238 17950 5.7621 -
1.4278 18000 7.4428 -
1.4317 18050 7.7264 -
1.4357 18100 3.2844 -
1.4397 18150 3.3061 -
1.4436 18200 4.7022 -
1.4476 18250 4.4779 -
1.4516 18300 6.4412 -
1.4555 18350 4.7373 -
1.4595 18400 4.6429 -
1.4635 18450 3.3968 -
1.4674 18500 4.4043 -
1.4714 18550 4.6066 -
1.4754 18600 5.7126 -
1.4793 18650 5.6637 -
1.4833 18700 3.9226 -
1.4873 18750 3.2268 -
1.4912 18800 2.6664 -
1.4952 18850 4.2593 -
1.4992 18900 5.9987 -
1.5031 18950 4.3925 -
1.5071 19000 4.8747 -
1.5111 19050 4.4443 -
1.5150 19100 2.1540 -
1.5190 19150 3.0945 -
1.5230 19200 2.8467 -
1.5269 19250 5.3955 -
1.5309 19300 5.0872 -
1.5349 19350 5.2779 -
1.5388 19400 1.5735 -
1.5428 19450 3.8178 -
1.5468 19500 4.2289 -
1.5507 19550 5.1452 -
1.5547 19600 4.2395 -
1.5587 19650 4.8533 -
1.5626 19700 4.7204 -
1.5666 19750 5.3427 -
1.5706 19800 4.2772 -
1.5745 19850 6.0442 -
1.5785 19900 4.4541 -
1.5825 19950 5.3928 -
1.5864 20000 6.0732 -
1.5904 20050 4.0754 -
1.5944 20100 4.4296 -
1.5983 20150 6.0932 -
1.6023 20200 4.1781 -
1.6063 20250 5.5353 -
1.6102 20300 4.3653 -
1.6142 20350 7.0458 -
1.6181 20400 4.9245 -
1.6221 20450 4.3144 -
1.6261 20500 5.0363 -
1.6300 20550 3.5105 -
1.6340 20600 3.6458 -
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5.0 63035 - 0.3402 (+0.0594)
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5.8817 74150 4.2529 -
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5.8896 74250 3.0206 -
5.8936 74300 1.4614 -
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5.9054 74450 4.4862 -
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5.9173 74600 1.9585 -
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5.9610 75150 1.8819 -
5.9649 75200 3.4180 -
5.9689 75250 4.3376 -
5.9729 75300 3.7332 -
5.9768 75350 4.2718 -
5.9808 75400 1.7943 -
5.9848 75450 2.6804 -
5.9887 75500 1.8139 -
5.9927 75550 2.6343 -
5.9967 75600 2.0074 -
6.0 75642 - 0.3415 (+0.0607)
  • The bold row denotes the saved checkpoint.

Training Time

  • Training: 33.3 minutes

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 5.4.1
  • Transformers: 5.5.4
  • PyTorch: 2.11.0+cu130
  • Accelerate: 1.13.0
  • Datasets: 4.8.4
  • Tokenizers: 0.22.2

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",
}
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