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mmlw-roberta-base-klej-dyk-v0.1

Sentence Similarity
sentence-transformers
Safetensors
English
roberta
feature-extraction
dataset_size:1K<n<10K
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
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mmlw-roberta-base-klej-dyk-v0.1

This is a sentence-transformers model finetuned from sdadas/mmlw-roberta-base. It maps sentences & paragraphs to a 768-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: sdadas/mmlw-roberta-base
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, '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': False, 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Dalsze losy relikwii',
    'Losy relikwii świętego',
    'czemu gra The Saboteur wywołała wiele kontrowersji?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.1899
cosine_accuracy@3 0.5865
cosine_accuracy@5 0.7692
cosine_accuracy@10 0.8534
cosine_precision@1 0.1899
cosine_precision@3 0.1955
cosine_precision@5 0.1538
cosine_precision@10 0.0853
cosine_recall@1 0.1899
cosine_recall@3 0.5865
cosine_recall@5 0.7692
cosine_recall@10 0.8534
cosine_ndcg@10 0.5205
cosine_mrr@10 0.4128
cosine_map@100 0.4182

Information Retrieval

Metric Value
cosine_accuracy@1 0.1875
cosine_accuracy@3 0.5889
cosine_accuracy@5 0.7596
cosine_accuracy@10 0.863
cosine_precision@1 0.1875
cosine_precision@3 0.1963
cosine_precision@5 0.1519
cosine_precision@10 0.0863
cosine_recall@1 0.1875
cosine_recall@3 0.5889
cosine_recall@5 0.7596
cosine_recall@10 0.863
cosine_ndcg@10 0.5204
cosine_mrr@10 0.4101
cosine_map@100 0.4148

Information Retrieval

Metric Value
cosine_accuracy@1 0.1947
cosine_accuracy@3 0.5649
cosine_accuracy@5 0.7452
cosine_accuracy@10 0.8462
cosine_precision@1 0.1947
cosine_precision@3 0.1883
cosine_precision@5 0.149
cosine_precision@10 0.0846
cosine_recall@1 0.1947
cosine_recall@3 0.5649
cosine_recall@5 0.7452
cosine_recall@10 0.8462
cosine_ndcg@10 0.5145
cosine_mrr@10 0.4078
cosine_map@100 0.4131

Information Retrieval

Metric Value
cosine_accuracy@1 0.1827
cosine_accuracy@3 0.5192
cosine_accuracy@5 0.7163
cosine_accuracy@10 0.8293
cosine_precision@1 0.1827
cosine_precision@3 0.1731
cosine_precision@5 0.1433
cosine_precision@10 0.0829
cosine_recall@1 0.1827
cosine_recall@3 0.5192
cosine_recall@5 0.7163
cosine_recall@10 0.8293
cosine_ndcg@10 0.4955
cosine_mrr@10 0.3889
cosine_map@100 0.394

Information Retrieval

Metric Value
cosine_accuracy@1 0.1779
cosine_accuracy@3 0.4832
cosine_accuracy@5 0.6514
cosine_accuracy@10 0.774
cosine_precision@1 0.1779
cosine_precision@3 0.1611
cosine_precision@5 0.1303
cosine_precision@10 0.0774
cosine_recall@1 0.1779
cosine_recall@3 0.4832
cosine_recall@5 0.6514
cosine_recall@10 0.774
cosine_ndcg@10 0.4639
cosine_mrr@10 0.3654
cosine_map@100 0.3728

Training Details

Training Dataset

Unnamed Dataset

  • Size: 3,738 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 5 tokens
    • mean: 50.1 tokens
    • max: 466 tokens
    • min: 6 tokens
    • mean: 16.62 tokens
    • max: 49 tokens
  • Samples:
    positive anchor
    Zespół Blaua (zespół Jabsa, ang. Blau syndrome, BS) – rzadka choroba genetyczna o dziedziczeniu autosomalnym dominującym, charakteryzująca się ziarniniakowym zapaleniem stawów o wczesnym początku, zapaleniem jagodówki (uveitis) i wysypką skórną, a także kamptodaktylią. jakie choroby genetyczne dziedziczą się autosomalnie dominująco?
    Gorgippia Gorgippia – starożytne miasto bosporańskie nad Morzem Czarnym, którego pozostałości znajdują się obecnie pod współczesną zabudową centralnej części miasta Anapa w Kraju Krasnodarskim w Rosji. gdzie obecnie znajduje się starożytne miasto Gorgippia?
    Ulubionym dystansem Rücker było 400 metrów i to na nim notowała największe indywidualne sukcesy : srebrny medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) 6. miejsce w Pucharze Świata w Lekkoatletyce (Hawana 1992) 5. miejsce na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) srebro podczas Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) złota medalistka mistrzostw Niemiec Duże sukcesy odnosiła także w sztafecie 4 x 400 metrów : złoto Mistrzostw Europy juniorów w lekkoatletyce (Varaždin 1989) złoty medal Mistrzostw Europy juniorów w lekkoatletyce (Saloniki 1991) brąz na Mistrzostwach Europy w Lekkoatletyce (Helsinki 1994) brązowy medal podczas Igrzysk Olimpijskich (Atlanta 1996) brąz na Halowych Mistrzostwach Świata w Lekkoatletyce (Paryż 1997) złoto Mistrzostw Świata w Lekkoatletyce (Ateny 1997) brązowy medal Mistrzostw Świata w Lekkoatletyce (Sewilla 1999) kto zaprojektował medale, które będą wręczane podczas tegorocznych mistrzostw Europy juniorów w lekkoatletyce?
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • gradient_accumulation_steps: 8
  • learning_rate: 2e-05
  • num_train_epochs: 5
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • bf16: True
  • tf32: True
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 8
  • per_device_eval_batch_size: 8
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 8
  • eval_accumulation_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 5
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: True
  • fp16: False
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • 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_fused
  • 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: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss dim_128_cosine_map@100 dim_256_cosine_map@100 dim_512_cosine_map@100 dim_64_cosine_map@100 dim_768_cosine_map@100
0 0 - 0.3475 0.3675 0.3753 0.2982 0.3798
0.0171 1 2.6683 - - - - -
0.0342 2 3.2596 - - - - -
0.0513 3 3.4541 - - - - -
0.0684 4 2.4201 - - - - -
0.0855 5 3.5911 - - - - -
0.1026 6 3.0902 - - - - -
0.1197 7 2.5999 - - - - -
0.1368 8 2.892 - - - - -
0.1538 9 2.8722 - - - - -
0.1709 10 2.3703 - - - - -
0.1880 11 2.6833 - - - - -
0.2051 12 1.9814 - - - - -
0.2222 13 1.6643 - - - - -
0.2393 14 1.8493 - - - - -
0.2564 15 1.5136 - - - - -
0.2735 16 1.9726 - - - - -
0.2906 17 1.1505 - - - - -
0.3077 18 1.3834 - - - - -
0.3248 19 1.2244 - - - - -
0.3419 20 1.2107 - - - - -
0.3590 21 0.8936 - - - - -
0.3761 22 0.8144 - - - - -
0.3932 23 0.8353 - - - - -
0.4103 24 1.572 - - - - -
0.4274 25 0.9257 - - - - -
0.4444 26 0.8405 - - - - -
0.4615 27 0.5621 - - - - -
0.4786 28 0.4241 - - - - -
0.4957 29 0.6171 - - - - -
0.5128 30 0.5989 - - - - -
0.5299 31 0.2767 - - - - -
0.5470 32 0.5599 - - - - -
0.5641 33 0.5964 - - - - -
0.5812 34 0.9778 - - - - -
0.5983 35 0.772 - - - - -
0.6154 36 1.0341 - - - - -
0.6325 37 0.3503 - - - - -
0.6496 38 0.8229 - - - - -
0.6667 39 0.969 - - - - -
0.6838 40 1.7993 - - - - -
0.7009 41 0.5542 - - - - -
0.7179 42 1.332 - - - - -
0.7350 43 1.1516 - - - - -
0.7521 44 1.3183 - - - - -
0.7692 45 1.0865 - - - - -
0.7863 46 0.6204 - - - - -
0.8034 47 0.7541 - - - - -
0.8205 48 0.9362 - - - - -
0.8376 49 0.3979 - - - - -
0.8547 50 0.7187 - - - - -
0.8718 51 0.9217 - - - - -
0.8889 52 0.4866 - - - - -
0.9060 53 0.355 - - - - -
0.9231 54 0.7172 - - - - -
0.9402 55 0.6007 - - - - -
0.9573 56 1.1547 - - - - -
0.9744 57 0.5713 - - - - -
0.9915 58 0.9089 0.3985 0.4164 0.4264 0.3642 0.4255
1.0085 59 0.594 - - - - -
1.0256 60 0.6554 - - - - -
1.0427 61 0.2794 - - - - -
1.0598 62 0.8654 - - - - -
1.0769 63 0.9698 - - - - -
1.0940 64 1.4827 - - - - -
1.1111 65 0.3159 - - - - -
1.1282 66 0.255 - - - - -
1.1453 67 0.9819 - - - - -
1.1624 68 0.7442 - - - - -
1.1795 69 0.8199 - - - - -
1.1966 70 0.2647 - - - - -
1.2137 71 0.4098 - - - - -
1.2308 72 0.1608 - - - - -
1.2479 73 0.2092 - - - - -
1.2650 74 0.1231 - - - - -
1.2821 75 0.3203 - - - - -
1.2991 76 0.1435 - - - - -
1.3162 77 0.2293 - - - - -
1.3333 78 0.131 - - - - -
1.3504 79 0.1662 - - - - -
1.3675 80 0.094 - - - - -
1.3846 81 0.1454 - - - - -
1.4017 82 0.3096 - - - - -
1.4188 83 0.3188 - - - - -
1.4359 84 0.1156 - - - - -
1.4530 85 0.0581 - - - - -
1.4701 86 0.0543 - - - - -
1.4872 87 0.0427 - - - - -
1.5043 88 0.07 - - - - -
1.5214 89 0.0451 - - - - -
1.5385 90 0.0646 - - - - -
1.5556 91 0.1152 - - - - -
1.5726 92 0.1292 - - - - -
1.5897 93 0.1591 - - - - -
1.6068 94 0.1194 - - - - -
1.6239 95 0.0876 - - - - -
1.6410 96 0.1018 - - - - -
1.6581 97 0.3309 - - - - -
1.6752 98 0.2214 - - - - -
1.6923 99 0.1536 - - - - -
1.7094 100 0.1543 - - - - -
1.7265 101 0.3663 - - - - -
1.7436 102 0.2719 - - - - -
1.7607 103 0.1379 - - - - -
1.7778 104 0.0479 - - - - -
1.7949 105 0.0757 - - - - -
1.8120 106 0.059 - - - - -
1.8291 107 0.119 - - - - -
1.8462 108 0.1295 - - - - -
1.8632 109 0.115 - - - - -
1.8803 110 0.142 - - - - -
1.8974 111 0.1064 - - - - -
1.9145 112 0.0959 - - - - -
1.9316 113 0.0839 - - - - -
1.9487 114 0.1762 - - - - -
1.9658 115 0.1986 - - - - -
1.9829 116 0.0599 - - - - -
2.0 117 0.1145 0.3869 0.4095 0.4135 0.3664 0.4195
2.0171 118 0.0815 - - - - -
2.0342 119 0.1052 - - - - -
2.0513 120 0.1348 - - - - -
2.0684 121 0.255 - - - - -
2.0855 122 0.251 - - - - -
2.1026 123 0.3033 - - - - -
2.1197 124 0.0385 - - - - -
2.1368 125 0.0687 - - - - -
2.1538 126 0.1682 - - - - -
2.1709 127 0.0774 - - - - -
2.1880 128 0.0944 - - - - -
2.2051 129 0.036 - - - - -
2.2222 130 0.0393 - - - - -
2.2393 131 0.0387 - - - - -
2.2564 132 0.0273 - - - - -
2.2735 133 0.056 - - - - -
2.2906 134 0.0279 - - - - -
2.3077 135 0.0557 - - - - -
2.3248 136 0.0197 - - - - -
2.3419 137 0.0216 - - - - -
2.3590 138 0.0212 - - - - -
2.3761 139 0.0239 - - - - -
2.3932 140 0.0526 - - - - -
2.4103 141 0.1072 - - - - -
2.4274 142 0.0347 - - - - -
2.4444 143 0.024 - - - - -
2.4615 144 0.0128 - - - - -
2.4786 145 0.0089 - - - - -
2.4957 146 0.0101 - - - - -
2.5128 147 0.0124 - - - - -
2.5299 148 0.011 - - - - -
2.5470 149 0.0182 - - - - -
2.5641 150 0.0379 - - - - -
2.5812 151 0.0395 - - - - -
2.5983 152 0.0372 - - - - -
2.6154 153 0.031 - - - - -
2.6325 154 0.0136 - - - - -
2.6496 155 0.0355 - - - - -
2.6667 156 0.0296 - - - - -
2.6838 157 0.0473 - - - - -
2.7009 158 0.0295 - - - - -
2.7179 159 0.0576 - - - - -
2.7350 160 0.0592 - - - - -
2.7521 161 0.0571 - - - - -
2.7692 162 0.0221 - - - - -
2.7863 163 0.0179 - - - - -
2.8034 164 0.0195 - - - - -
2.8205 165 0.0291 - - - - -
2.8376 166 0.024 - - - - -
2.8547 167 0.0396 - - - - -
2.8718 168 0.0352 - - - - -
2.8889 169 0.0431 - - - - -
2.9060 170 0.0222 - - - - -
2.9231 171 0.016 - - - - -
2.9402 172 0.0307 - - - - -
2.9573 173 0.0439 - - - - -
2.9744 174 0.0197 - - - - -
2.9915 175 0.0181 0.3928 0.4120 0.4152 0.3717 0.4180
3.0085 176 0.03 - - - - -
3.0256 177 0.0325 - - - - -
3.0427 178 0.0286 - - - - -
3.0598 179 0.0746 - - - - -
3.0769 180 0.0677 - - - - -
3.0940 181 0.0574 - - - - -
3.1111 182 0.0158 - - - - -
3.1282 183 0.0092 - - - - -
3.1453 184 0.0412 - - - - -
3.1624 185 0.0308 - - - - -
3.1795 186 0.022 - - - - -
3.1966 187 0.0157 - - - - -
3.2137 188 0.0109 - - - - -
3.2308 189 0.0059 - - - - -
3.2479 190 0.0206 - - - - -
3.2650 191 0.0135 - - - - -
3.2821 192 0.0199 - - - - -
3.2991 193 0.0124 - - - - -
3.3162 194 0.0081 - - - - -
3.3333 195 0.0052 - - - - -
3.3504 196 0.006 - - - - -
3.3675 197 0.0074 - - - - -
3.3846 198 0.0085 - - - - -
3.4017 199 0.0273 - - - - -
3.4188 200 0.0363 - - - - -
3.4359 201 0.0077 - - - - -
3.4530 202 0.0046 - - - - -
3.4701 203 0.0067 - - - - -
3.4872 204 0.0054 - - - - -
3.5043 205 0.0055 - - - - -
3.5214 206 0.0052 - - - - -
3.5385 207 0.004 - - - - -
3.5556 208 0.0102 - - - - -
3.5726 209 0.0228 - - - - -
3.5897 210 0.0315 - - - - -
3.6068 211 0.0095 - - - - -
3.6239 212 0.0069 - - - - -
3.6410 213 0.0066 - - - - -
3.6581 214 0.0395 - - - - -
3.6752 215 0.0176 - - - - -
3.6923 216 0.0156 - - - - -
3.7094 217 0.0168 - - - - -
3.7265 218 0.0376 - - - - -
3.7436 219 0.0149 - - - - -
3.7607 220 0.0179 - - - - -
3.7778 221 0.0059 - - - - -
3.7949 222 0.013 - - - - -
3.8120 223 0.0081 - - - - -
3.8291 224 0.0136 - - - - -
3.8462 225 0.0129 - - - - -
3.8632 226 0.0132 - - - - -
3.8803 227 0.0228 - - - - -
3.8974 228 0.0091 - - - - -
3.9145 229 0.0112 - - - - -
3.9316 230 0.0124 - - - - -
3.9487 231 0.0224 - - - - -
3.9658 232 0.0191 - - - - -
3.9829 233 0.0078 - - - - -
4.0 234 0.0145 0.3959 0.411 0.4154 0.3741 0.4179
4.0171 235 0.0089 - - - - -
4.0342 236 0.0157 - - - - -
4.0513 237 0.019 - - - - -
4.0684 238 0.0315 - - - - -
4.0855 239 0.0311 - - - - -
4.1026 240 0.0155 - - - - -
4.1197 241 0.0078 - - - - -
4.1368 242 0.0069 - - - - -
4.1538 243 0.0246 - - - - -
4.1709 244 0.011 - - - - -
4.1880 245 0.0169 - - - - -
4.2051 246 0.0065 - - - - -
4.2222 247 0.0093 - - - - -
4.2393 248 0.0059 - - - - -
4.2564 249 0.0072 - - - - -
4.2735 250 0.0114 - - - - -
4.2906 251 0.0048 - - - - -
4.3077 252 0.0099 - - - - -
4.3248 253 0.0061 - - - - -
4.3419 254 0.005 - - - - -
4.3590 255 0.0077 - - - - -
4.3761 256 0.0057 - - - - -
4.3932 257 0.0106 - - - - -
4.4103 258 0.0176 - - - - -
4.4274 259 0.0085 - - - - -
4.4444 260 0.0059 - - - - -
4.4615 261 0.0063 - - - - -
4.4786 262 0.003 - - - - -
4.4957 263 0.0041 - - - - -
4.5128 264 0.0048 - - - - -
4.5299 265 0.0037 - - - - -
4.5470 266 0.0052 - - - - -
4.5641 267 0.0084 - - - - -
4.5812 268 0.0183 - - - - -
4.5983 269 0.0065 - - - - -
4.6154 270 0.0074 - - - - -
4.6325 271 0.0046 - - - - -
4.6496 272 0.009 - - - - -
4.6667 273 0.01 - - - - -
4.6838 274 0.0158 - - - - -
4.7009 275 0.0077 - - - - -
4.7179 276 0.0259 - - - - -
4.7350 277 0.0204 - - - - -
4.7521 278 0.0155 - - - - -
4.7692 279 0.0101 - - - - -
4.7863 280 0.0062 - - - - -
4.8034 281 0.0065 - - - - -
4.8205 282 0.0115 - - - - -
4.8376 283 0.0088 - - - - -
4.8547 284 0.0157 - - - - -
4.8718 285 0.0145 - - - - -
4.8889 286 0.0122 - - - - -
4.9060 287 0.007 - - - - -
4.9231 288 0.0126 - - - - -
4.9402 289 0.0094 - - - - -
4.9573 290 0.016 0.3940 0.4131 0.4148 0.3728 0.4182
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.12.2
  • Sentence Transformers: 3.0.0
  • Transformers: 4.41.2
  • PyTorch: 2.3.1
  • Accelerate: 0.27.2
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

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",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss 

@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}
}
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