Hugging Face's logo

ve88ifz2
/
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
Model card Files Community
Edit model card

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}
}
Downloads last month
3
Safetensors
Model size
124M params
Tensor type
F32
·
Inference Examples
Sentence Similarity
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for ve88ifz2/mmlw-roberta-base-klej-dyk-v0.1

Base model

sdadas/mmlw-roberta-base
Finetuned
(1)
this model

Evaluation results

View on Papers With Code