Hugging Face's logo

praphul555
/
ohai_gte_qwen_1.5b_instruct

Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:204376
loss:LoggingMNR
Eval Results (legacy)
Model card Files
xet
 Community
ohai_gte_qwen_1.5b_instruct / README.md
praphul555's picture
praphul555
Add new SentenceTransformer model
a5fd59b verified
preview code
|
raw
history blame contribute delete
41 kB
metadata 
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:204376
  - loss:LoggingMNR
base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
widget:
  - source_sentence: Start kub stat.
    sentences:
      - IV Push Ea Addtl Same Drug - Nursing
      - KUB
      - ROPivacaine (ROPivacaine 0.5% 30 mL)
  - source_sentence: 'Add V: cardiac rehab benefits video.'
    sentences:
      - traMADol (Ultram)
      - insulin aspart(NOVOLOG Pen) 100 unit/mL Inj,3 mL
      - V:Cardiac Rehab, The Benefits of Cardiac Rehabilitation
  - source_sentence: Continue the Vitamin B12 shots.
    sentences:
      - Patient is on Anticoagulant
      - Miscellaneous Medication (Suction machine)
      - cyanocobalamin (Vitamin B12 DoT)
  - source_sentence: Start medication exercise pls.
    sentences:
      - Misc. Medication (Exercise)
      - Small Bore Feeding Tube Insertion
      - Consult to Registered Dietitian LTC
  - source_sentence: |-
      <user> Hi, I need to cancel a test.
      <assistant> Of course, which test needs cancellation?
      <user> The reflex lactic acid that we ordered.
      <assistant> Got it, I'll cancel it.
      <user> Perfect, cancel it now.
    sentences:
      - pt may return to room in 30 min if vital signs stable
      - Reflex Lactic Acid w/ Reflex, Plasma
      - BH Social Services Assessment
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: ir val
          type: ir-val
        metrics:
          - type: cosine_accuracy@1
            value: 0.7728813559322034
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.9491525423728814
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9670702179176756
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.97772397094431
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7728813559322034
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3163841807909604
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19341404358353514
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09777239709443102
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7728813559322034
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.9491525423728814
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9670702179176756
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.97772397094431
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8902698394508296
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8605843806449124
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8609602517901379
            name: Cosine Map@100

SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-Qwen2-1.5B-instruct. It maps sentences & paragraphs to a 1536-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
  • Maximum Sequence Length: 32768 tokens
  • Output Dimensionality: 1536 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("praphul555/ohai_gte_qwen_1.5b_instruct")
# Run inference
queries = [
    "\u003cuser\u003e Hi, I need to cancel a test.\n\u003cassistant\u003e Of course, which test needs cancellation?\n\u003cuser\u003e The reflex lactic acid that we ordered.\n\u003cassistant\u003e Got it, I\u0027ll cancel it.\n\u003cuser\u003e Perfect, cancel it now.",
]
documents = [
    'Reflex Lactic Acid w/ Reflex, Plasma',
    'pt may return to room in 30 min if vital signs stable',
    'BH Social Services Assessment',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1536] [3, 1536]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7656,  0.0259, -0.0052]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.7729
cosine_accuracy@3 0.9492
cosine_accuracy@5 0.9671
cosine_accuracy@10 0.9777
cosine_precision@1 0.7729
cosine_precision@3 0.3164
cosine_precision@5 0.1934
cosine_precision@10 0.0978
cosine_recall@1 0.7729
cosine_recall@3 0.9492
cosine_recall@5 0.9671
cosine_recall@10 0.9777
cosine_ndcg@10 0.8903
cosine_mrr@10 0.8606
cosine_map@100 0.861

Training Details

Training Dataset

Unnamed Dataset

  • Size: 204,376 training samples
  • Columns: sentence_0 and sentence_1
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1
    type string string
    details
    • min: 2 tokens
    • mean: 20.11 tokens
    • max: 71 tokens
    • min: 2 tokens
    • mean: 13.66 tokens
    • max: 59 tokens
  • Samples:
    sentence_0 sentence_1
    Please renew the calcium gluconate tabs. calcium gluconate (calcium gluconate 500 mg oral tablet)
    r Measured Weight
    Order celiac antibodies tTG IgA and total IgA w/ reflex test. Celiac Antibodies tTG IgA + Total IgA w/Rflx to tTG IgG and DGP IgG
  • Loss: main.LoggingMNR with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 64
  • per_device_eval_batch_size: 64
  • fp16: True
  • multi_dataset_batch_sampler: round_robin

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: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 3
  • 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: 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
  • hub_revision: None
  • 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
  • liger_kernel_config: None
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss ir-val_cosine_ndcg@10
0.0063 10 - 0.7871
0.0125 20 - 0.7863
0.0188 30 - 0.7846
0.0250 40 - 0.7848
0.0313 50 - 0.7875
0.0376 60 - 0.7887
0.0438 70 - 0.7912
0.0501 80 - 0.7922
0.0564 90 - 0.7989
0.0626 100 - 0.8035
0.0689 110 - 0.8112
0.0751 120 - 0.8138
0.0814 130 - 0.8232
0.0877 140 - 0.8265
0.0939 150 - 0.8310
0.1002 160 - 0.8340
0.1064 170 - 0.8402
0.1127 180 - 0.8442
0.1190 190 - 0.8453
0.1252 200 - 0.8469
0.1315 210 - 0.8498
0.1378 220 - 0.8500
0.1440 230 - 0.8502
0.1503 240 - 0.8537
0.1565 250 - 0.8544
0.1628 260 - 0.8544
0.1691 270 - 0.8562
0.1753 280 - 0.8560
0.1816 290 - 0.8572
0.1879 300 - 0.8597
0.1941 310 - 0.8617
0.2004 320 - 0.8621
0.2066 330 - 0.8624
0.2129 340 - 0.8638
0.2192 350 - 0.8631
0.2254 360 - 0.8628
0.2317 370 - 0.8632
0.2379 380 - 0.8678
0.2442 390 - 0.8653
0.2505 400 - 0.8655
0.2567 410 - 0.8671
0.2630 420 - 0.8668
0.2693 430 - 0.8674
0.2755 440 - 0.8675
0.2818 450 - 0.8678
0.2880 460 - 0.8675
0.2943 470 - 0.8679
0.3006 480 - 0.8685
0.3068 490 - 0.8674
0.3131 500 0.3129 0.8686
0.3193 510 - 0.8698
0.3256 520 - 0.8695
0.3319 530 - 0.8693
0.3381 540 - 0.8713
0.3444 550 - 0.8707
0.3507 560 - 0.8713
0.3569 570 - 0.8692
0.3632 580 - 0.8716
0.3694 590 - 0.8728
0.3757 600 - 0.8716
0.3820 610 - 0.8747
0.3882 620 - 0.8730
0.3945 630 - 0.8736
0.4008 640 - 0.8744
0.4070 650 - 0.8746
0.4133 660 - 0.8751
0.4195 670 - 0.8746
0.4258 680 - 0.8727
0.4321 690 - 0.8735
0.4383 700 - 0.8737
0.4446 710 - 0.8726
0.4508 720 - 0.8714
0.4571 730 - 0.8735
0.4634 740 - 0.8735
0.4696 750 - 0.8719
0.4759 760 - 0.8721
0.4822 770 - 0.8734
0.4884 780 - 0.8729
0.4947 790 - 0.8732
0.5009 800 - 0.8739
0.5072 810 - 0.8731
0.5135 820 - 0.8740
0.5197 830 - 0.8723
0.5260 840 - 0.8715
0.5322 850 - 0.8742
0.5385 860 - 0.8738
0.5448 870 - 0.8742
0.5510 880 - 0.8727
0.5573 890 - 0.8718
0.5636 900 - 0.8735
0.5698 910 - 0.8747
0.5761 920 - 0.8743
0.5823 930 - 0.8725
0.5886 940 - 0.8741
0.5949 950 - 0.8726
0.6011 960 - 0.8724
0.6074 970 - 0.8740
0.6137 980 - 0.8751
0.6199 990 - 0.8751
0.6262 1000 0.1567 0.8760
0.6324 1010 - 0.8745
0.6387 1020 - 0.8729
0.6450 1030 - 0.8737
0.6512 1040 - 0.8778
0.6575 1050 - 0.8771
0.6637 1060 - 0.8765
0.6700 1070 - 0.8787
0.6763 1080 - 0.8780
0.6825 1090 - 0.8775
0.6888 1100 - 0.8761
0.6951 1110 - 0.8762
0.7013 1120 - 0.8770
0.7076 1130 - 0.8768
0.7138 1140 - 0.8778
0.7201 1150 - 0.8770
0.7264 1160 - 0.8777
0.7326 1170 - 0.8792
0.7389 1180 - 0.8796
0.7451 1190 - 0.8793
0.7514 1200 - 0.8795
0.7577 1210 - 0.8794
0.7639 1220 - 0.8770
0.7702 1230 - 0.8771
0.7765 1240 - 0.8770
0.7827 1250 - 0.8766
0.7890 1260 - 0.8772
0.7952 1270 - 0.8780
0.8015 1280 - 0.8796
0.8078 1290 - 0.8787
0.8140 1300 - 0.8793
0.8203 1310 - 0.8784
0.8265 1320 - 0.8794
0.8328 1330 - 0.8774
0.8391 1340 - 0.8805
0.8453 1350 - 0.8807
0.8516 1360 - 0.8793
0.8579 1370 - 0.8805
0.8641 1380 - 0.8792
0.8704 1390 - 0.8799
0.8766 1400 - 0.8789
0.8829 1410 - 0.8789
0.8892 1420 - 0.8805
0.8954 1430 - 0.8792
0.9017 1440 - 0.8822
0.9080 1450 - 0.8797
0.9142 1460 - 0.8793
0.9205 1470 - 0.8796
0.9267 1480 - 0.8791
0.9330 1490 - 0.8802
0.9393 1500 0.147 0.8804
0.9455 1510 - 0.8806
0.9518 1520 - 0.8786
0.9580 1530 - 0.8794
0.9643 1540 - 0.8799
0.9706 1550 - 0.8812
0.9768 1560 - 0.8802
0.9831 1570 - 0.8808
0.9894 1580 - 0.8808
0.9956 1590 - 0.8805
1.0 1597 - 0.8805
1.0019 1600 - 0.8806
1.0081 1610 - 0.8806
1.0144 1620 - 0.8804
1.0207 1630 - 0.8791
1.0269 1640 - 0.8804
1.0332 1650 - 0.8802
1.0394 1660 - 0.8823
1.0457 1670 - 0.8805
1.0520 1680 - 0.8808
1.0582 1690 - 0.8824
1.0645 1700 - 0.8801
1.0708 1710 - 0.8810
1.0770 1720 - 0.8812
1.0833 1730 - 0.8817
1.0895 1740 - 0.8812
1.0958 1750 - 0.8806
1.1021 1760 - 0.8820
1.1083 1770 - 0.8826
1.1146 1780 - 0.8825
1.1209 1790 - 0.8800
1.1271 1800 - 0.8805
1.1334 1810 - 0.8801
1.1396 1820 - 0.8819
1.1459 1830 - 0.8810
1.1522 1840 - 0.8800
1.1584 1850 - 0.8813
1.1647 1860 - 0.8817
1.1709 1870 - 0.8801
1.1772 1880 - 0.8809
1.1835 1890 - 0.8821
1.1897 1900 - 0.8838
1.1960 1910 - 0.8819
1.2023 1920 - 0.8821
1.2085 1930 - 0.8826
1.2148 1940 - 0.8841
1.2210 1950 - 0.8845
1.2273 1960 - 0.8829
1.2336 1970 - 0.8817
1.2398 1980 - 0.8833
1.2461 1990 - 0.8861
1.2523 2000 0.1343 0.8825
1.2586 2010 - 0.8821
1.2649 2020 - 0.8840
1.2711 2030 - 0.8836
1.2774 2040 - 0.8832
1.2837 2050 - 0.8806
1.2899 2060 - 0.8820
1.2962 2070 - 0.8805
1.3024 2080 - 0.8818
1.3087 2090 - 0.8834
1.3150 2100 - 0.8819
1.3212 2110 - 0.8854
1.3275 2120 - 0.8824
1.3338 2130 - 0.8811
1.3400 2140 - 0.8823
1.3463 2150 - 0.8810
1.3525 2160 - 0.8819
1.3588 2170 - 0.8816
1.3651 2180 - 0.8828
1.3713 2190 - 0.8828
1.3776 2200 - 0.8850
1.3838 2210 - 0.8833
1.3901 2220 - 0.8849
1.3964 2230 - 0.8834
1.4026 2240 - 0.8815
1.4089 2250 - 0.8821
1.4152 2260 - 0.8830
1.4214 2270 - 0.8822
1.4277 2280 - 0.8809
1.4339 2290 - 0.8831
1.4402 2300 - 0.8838
1.4465 2310 - 0.8840
1.4527 2320 - 0.8836
1.4590 2330 - 0.8827
1.4652 2340 - 0.8833
1.4715 2350 - 0.8836
1.4778 2360 - 0.8823
1.4840 2370 - 0.8823
1.4903 2380 - 0.8829
1.4966 2390 - 0.8823
1.5028 2400 - 0.8826
1.5091 2410 - 0.8839
1.5153 2420 - 0.8833
1.5216 2430 - 0.8830
1.5279 2440 - 0.8829
1.5341 2450 - 0.8828
1.5404 2460 - 0.8849
1.5466 2470 - 0.8827
1.5529 2480 - 0.8833
1.5592 2490 - 0.8832
1.5654 2500 0.1315 0.8841
1.5717 2510 - 0.8835
1.5780 2520 - 0.8839
1.5842 2530 - 0.8834
1.5905 2540 - 0.8847
1.5967 2550 - 0.8829
1.6030 2560 - 0.8815
1.6093 2570 - 0.8815
1.6155 2580 - 0.8815
1.6218 2590 - 0.8828
1.6281 2600 - 0.8839
1.6343 2610 - 0.8831
1.6406 2620 - 0.8848
1.6468 2630 - 0.8840
1.6531 2640 - 0.8821
1.6594 2650 - 0.8849
1.6656 2660 - 0.8833
1.6719 2670 - 0.8824
1.6781 2680 - 0.8826
1.6844 2690 - 0.8819
1.6907 2700 - 0.8831
1.6969 2710 - 0.8831
1.7032 2720 - 0.8845
1.7095 2730 - 0.8820
1.7157 2740 - 0.8814
1.7220 2750 - 0.8813
1.7282 2760 - 0.8830
1.7345 2770 - 0.8838
1.7408 2780 - 0.8833
1.7470 2790 - 0.8825
1.7533 2800 - 0.8814
1.7595 2810 - 0.8821
1.7658 2820 - 0.8817
1.7721 2830 - 0.8829
1.7783 2840 - 0.8837
1.7846 2850 - 0.8840
1.7909 2860 - 0.8838
1.7971 2870 - 0.8842
1.8034 2880 - 0.8867
1.8096 2890 - 0.8865
1.8159 2900 - 0.8863
1.8222 2910 - 0.8857
1.8284 2920 - 0.8846
1.8347 2930 - 0.8842
1.8410 2940 - 0.8860
1.8472 2950 - 0.8857
1.8535 2960 - 0.8851
1.8597 2970 - 0.8852
1.8660 2980 - 0.8852
1.8723 2990 - 0.8862
1.8785 3000 0.1249 0.8850
1.8848 3010 - 0.8843
1.8910 3020 - 0.8845
1.8973 3030 - 0.8862
1.9036 3040 - 0.8862
1.9098 3050 - 0.8848
1.9161 3060 - 0.8847
1.9224 3070 - 0.8865
1.9286 3080 - 0.8857
1.9349 3090 - 0.8874
1.9411 3100 - 0.8855
1.9474 3110 - 0.8873
1.9537 3120 - 0.8872
1.9599 3130 - 0.8856
1.9662 3140 - 0.8857
1.9724 3150 - 0.8862
1.9787 3160 - 0.8861
1.9850 3170 - 0.8861
1.9912 3180 - 0.8872
1.9975 3190 - 0.8869
2.0 3194 - 0.8850
2.0038 3200 - 0.8865
2.0100 3210 - 0.8854
2.0163 3220 - 0.8865
2.0225 3230 - 0.8847
2.0288 3240 - 0.8860
2.0351 3250 - 0.8883
2.0413 3260 - 0.8868
2.0476 3270 - 0.8842
2.0539 3280 - 0.8829
2.0601 3290 - 0.8830
2.0664 3300 - 0.8847
2.0726 3310 - 0.8840
2.0789 3320 - 0.8866
2.0852 3330 - 0.8845
2.0914 3340 - 0.8852
2.0977 3350 - 0.8864
2.1039 3360 - 0.8873
2.1102 3370 - 0.8877
2.1165 3380 - 0.8861
2.1227 3390 - 0.8865
2.1290 3400 - 0.8857
2.1353 3410 - 0.8857
2.1415 3420 - 0.8873
2.1478 3430 - 0.8866
2.1540 3440 - 0.8851
2.1603 3450 - 0.8865
2.1666 3460 - 0.8850
2.1728 3470 - 0.8837
2.1791 3480 - 0.8869
2.1853 3490 - 0.8861
2.1916 3500 0.1171 0.8861
2.1979 3510 - 0.8862
2.2041 3520 - 0.8889
2.2104 3530 - 0.8851
2.2167 3540 - 0.8878
2.2229 3550 - 0.8868
2.2292 3560 - 0.8858
2.2354 3570 - 0.8859
2.2417 3580 - 0.8844
2.2480 3590 - 0.8876
2.2542 3600 - 0.8881
2.2605 3610 - 0.8872
2.2668 3620 - 0.8846
2.2730 3630 - 0.8848
2.2793 3640 - 0.8846
2.2855 3650 - 0.8860
2.2918 3660 - 0.8864
2.2981 3670 - 0.8867
2.3043 3680 - 0.8860
2.3106 3690 - 0.8884
2.3168 3700 - 0.8881
2.3231 3710 - 0.8869
2.3294 3720 - 0.8869
2.3356 3730 - 0.8857
2.3419 3740 - 0.8866
2.3482 3750 - 0.8860
2.3544 3760 - 0.8872
2.3607 3770 - 0.8877
2.3669 3780 - 0.8887
2.3732 3790 - 0.8875
2.3795 3800 - 0.8883
2.3857 3810 - 0.8875
2.3920 3820 - 0.8879
2.3982 3830 - 0.8853
2.4045 3840 - 0.8877
2.4108 3850 - 0.8867
2.4170 3860 - 0.8879
2.4233 3870 - 0.8883
2.4296 3880 - 0.8897
2.4358 3890 - 0.8898
2.4421 3900 - 0.8865
2.4483 3910 - 0.8867
2.4546 3920 - 0.8866
2.4609 3930 - 0.8868
2.4671 3940 - 0.8866
2.4734 3950 - 0.8854
2.4796 3960 - 0.8884
2.4859 3970 - 0.8855
2.4922 3980 - 0.8858
2.4984 3990 - 0.8854
2.5047 4000 0.117 0.8861
2.5110 4010 - 0.8865
2.5172 4020 - 0.8855
2.5235 4030 - 0.8863
2.5297 4040 - 0.8864
2.5360 4050 - 0.8898
2.5423 4060 - 0.8890
2.5485 4070 - 0.8893
2.5548 4080 - 0.8902
2.5611 4090 - 0.8886
2.5673 4100 - 0.8882
2.5736 4110 - 0.8884
2.5798 4120 - 0.8876
2.5861 4130 - 0.8877
2.5924 4140 - 0.8879
2.5986 4150 - 0.8871
2.6049 4160 - 0.8881
2.6111 4170 - 0.8870
2.6174 4180 - 0.8883
2.6237 4190 - 0.8878
2.6299 4200 - 0.8890
2.6362 4210 - 0.8878
2.6425 4220 - 0.8897
2.6487 4230 - 0.8864
2.6550 4240 - 0.8871
2.6612 4250 - 0.8876
2.6675 4260 - 0.8856
2.6738 4270 - 0.8878
2.6800 4280 - 0.8884
2.6863 4290 - 0.8891
2.6925 4300 - 0.8891
2.6988 4310 - 0.8880
2.7051 4320 - 0.8865
2.7113 4330 - 0.8877
2.7176 4340 - 0.8859
2.7239 4350 - 0.8861
2.7301 4360 - 0.8853
2.7364 4370 - 0.8851
2.7426 4380 - 0.8868
2.7489 4390 - 0.8875
2.7552 4400 - 0.8869
2.7614 4410 - 0.8903

Framework Versions

  • Python: 3.11.13
  • Sentence Transformers: 5.0.0
  • Transformers: 4.53.1
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.8.1
  • Datasets: 4.0.0
  • Tokenizers: 0.21.2

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",
}

LoggingMNR 

@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}
}