metadata

language:
  - ru
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:904
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
  - source_sentence: Какой у тебя план на будущее?
    sentences:
      - >-
        Работа — это скучно, если не считать, что Уголовный розыск считает меня
        своим работником.
      - Я дам вам парабеллум, если дружба станет слишком серьезной!
      - >-
        План? Из Васюков полетят сигналы на Марс, а я буду на Земле собирать
        деньги на билет.
  - source_sentence: Какой у тебя любимый фильм?
    sentences:
      - Может быть, тебе дать еще список фильмов, где много денег?
      - Вам нужно путешествовать так, чтобы потом не забыть, где памятник.
      - А доисторические спортсмены в матрацах не тренируются?
  - source_sentence: Как ты проводишь свободное время?
    sentences:
      - Напиток? Командовать парадом буду я!
      - Нас топят — мы выплываем, а свободное время — это для плавания!
      - От мертвого осла уши получишь у Пушкина, а от фильмов — только кадры.
  - source_sentence: Как ты проводишь свободное время?
    sentences:
      - Картина битвы мне ясна, но я предпочитаю не сражаться с скукой.
      - Спорт — это для тех, кто не знает, что они произошли от коровы!
      - >-
        Тайный союз меча и орала! Свободное время — это когда можно ничего не
        делать и не переживать!
  - source_sentence: Какой у тебя любимый фильм?
    sentences:
      - А что, разве я похож на человека, который не любит читать между строк?
      - У нас хотя и не Париж, но кино у нас всегда с интригой!
      - Фильм? Знойная женщина, мечта поэта — вот мой любимый сюжет!
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: BGE m3 for Ostap project
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 1024
          type: dim_1024
        metrics:
          - type: cosine_accuracy@1
            value: 0.14933628318584072
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2665929203539823
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.34292035398230086
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4856194690265487
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14933628318584072
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08886430678466074
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06858407079646017
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04856194690265486
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14933628318584072
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2665929203539823
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.34292035398230086
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4856194690265487
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2942645243659726
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.23620329400196635
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2600956714540916
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.14601769911504425
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.26548672566371684
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.3473451327433628
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.48672566371681414
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14601769911504425
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08849557522123894
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06946902654867257
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.048672566371681415
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14601769911504425
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.26548672566371684
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3473451327433628
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.48672566371681414
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2931785163867407
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2343512958280655
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2581995173126666
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.14823008849557523
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.26548672566371684
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.34513274336283184
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4944690265486726
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14823008849557523
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08849557522123894
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06902654867256636
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04944690265486726
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14823008849557523
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.26548672566371684
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.34513274336283184
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4944690265486726
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2965536225707287
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.23654261483354377
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2597641504609653
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.14491150442477876
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2688053097345133
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.34845132743362833
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4911504424778761
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14491150442477876
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08960176991150441
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06969026548672566
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.049115044247787606
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14491150442477876
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2688053097345133
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.34845132743362833
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4911504424778761
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2942530832557106
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2342999367888746
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2580055991240585
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.14712389380530974
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2665929203539823
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.34623893805309736
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4944690265486726
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14712389380530974
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08886430678466076
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06924778761061946
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04944690265486726
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14712389380530974
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2665929203539823
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.34623893805309736
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.4944690265486726
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2963702071144291
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2362221695462843
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.25976571809408944
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.14601769911504425
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.26991150442477874
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.3473451327433628
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.497787610619469
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.14601769911504425
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08997050147492625
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.06946902654867257
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.049778761061946904
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.14601769911504425
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.26991150442477874
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3473451327433628
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.497787610619469
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.29684044099735196
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.23588767734232302
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2592174386566743
            name: Cosine Map@100

BGE m3 for Ostap project

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Language: ru
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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("fitlemon/bge-m3-ru-ostap")
# Run inference
sentences = [
    'Какой у тебя любимый фильм?',
    'У нас хотя и не Париж, но кино у нас всегда с интригой!',
    'Фильм? Знойная женщина, мечта поэта — вот мой любимый сюжет!',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Datasets: dim_1024, dim_768, dim_512, dim_256, dim_128 and dim_64
Evaluated with InformationRetrievalEvaluator

Metric	dim_1024	dim_768	dim_512	dim_256	dim_128	dim_64
cosine_accuracy@1	0.1493	0.146	0.1482	0.1449	0.1471	0.146
cosine_accuracy@3	0.2666	0.2655	0.2655	0.2688	0.2666	0.2699
cosine_accuracy@5	0.3429	0.3473	0.3451	0.3485	0.3462	0.3473
cosine_accuracy@10	0.4856	0.4867	0.4945	0.4912	0.4945	0.4978
cosine_precision@1	0.1493	0.146	0.1482	0.1449	0.1471	0.146
cosine_precision@3	0.0889	0.0885	0.0885	0.0896	0.0889	0.09
cosine_precision@5	0.0686	0.0695	0.069	0.0697	0.0692	0.0695
cosine_precision@10	0.0486	0.0487	0.0494	0.0491	0.0494	0.0498
cosine_recall@1	0.1493	0.146	0.1482	0.1449	0.1471	0.146
cosine_recall@3	0.2666	0.2655	0.2655	0.2688	0.2666	0.2699
cosine_recall@5	0.3429	0.3473	0.3451	0.3485	0.3462	0.3473
cosine_recall@10	0.4856	0.4867	0.4945	0.4912	0.4945	0.4978
cosine_ndcg@10	0.2943	0.2932	0.2966	0.2943	0.2964	0.2968
cosine_mrr@10	0.2362	0.2344	0.2365	0.2343	0.2362	0.2359
cosine_map@100	0.2601	0.2582	0.2598	0.258	0.2598	0.2592

Training Details

Training Dataset

Unnamed Dataset

Size: 904 training samples
Columns: question and answer
Approximate statistics based on the first 904 samples:
question answer
type string string
details
min: 6 tokens
mean: 10.16 tokens
max: 14 tokens

min: 8 tokens
mean: 20.91 tokens
max: 43 tokens

	question	answer
type	string	string
details	min: 6 tokens mean: 10.16 tokens max: 14 tokens	min: 8 tokens mean: 20.91 tokens max: 43 tokens

Samples:

question	answer
`Как ты проводишь свободное время?`	`Любителя бьют, а время — не ждет!`
`Какой у тебя план на будущее?`	`План на будущее? Широкие массы миллиардеров уже составили его за меня.`
`Какой у тебя любимый цвет?`	`Вы мне в конце концов не художник, не дизайнер и не стилист.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
learning_rate: 2e-05
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: True
tf32: False
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: 1
eval_accumulation_steps: None
torch_empty_cache_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: 4
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: False
fp16: True
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
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: None
hub_always_push: False
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
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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_1024_cosine_ndcg@10	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.0885	10	6.8669	-	-	-	-	-	-
0.1770	20	4.9384	-	-	-	-	-	-
0.2655	30	3.1491	-	-	-	-	-	-
0.3540	40	2.5456	-	-	-	-	-	-
0.4425	50	3.6943	-	-	-	-	-	-
0.5310	60	1.8947	-	-	-	-	-	-
0.6195	70	2.1762	-	-	-	-	-	-
0.7080	80	1.9446	-	-	-	-	-	-
0.7965	90	1.5278	-	-	-	-	-	-
0.8850	100	2.0417	-	-	-	-	-	-
0.9735	110	3.7804	-	-	-	-	-	-
1.0	113	-	0.2751	0.2747	0.2761	0.2786	0.2764	0.2715
1.0619	120	1.9706	-	-	-	-	-	-
1.1504	130	1.7073	-	-	-	-	-	-
1.2389	140	1.3279	-	-	-	-	-	-
1.3274	150	1.2724	-	-	-	-	-	-
1.4159	160	2.4455	-	-	-	-	-	-
1.5044	170	0.5255	-	-	-	-	-	-
1.5929	180	2.5764	-	-	-	-	-	-
1.6814	190	1.56	-	-	-	-	-	-
1.7699	200	0.9105	-	-	-	-	-	-
1.8584	210	1.9859	-	-	-	-	-	-
1.9469	220	1.6355	-	-	-	-	-	-
2.0088	227	-	0.2837	0.2852	0.2880	0.2899	0.2926	0.2902
2.0265	230	0.6769	-	-	-	-	-	-
2.1150	240	0.764	-	-	-	-	-	-
2.2035	250	1.0598	-	-	-	-	-	-
2.2920	260	0.9267	-	-	-	-	-	-
2.3805	270	0.9687	-	-	-	-	-	-
2.4690	280	0.7875	-	-	-	-	-	-
2.5575	290	1.3853	-	-	-	-	-	-
2.6460	300	0.8114	-	-	-	-	-	-
2.7345	310	1.6069	-	-	-	-	-	-
2.8230	320	0.8149	-	-	-	-	-	-
2.9115	330	0.8858	-	-	-	-	-	-
3.0	340	0.7858	0.2920	0.2917	0.2929	0.2927	0.2967	0.2969
3.0885	350	0.5889	-	-	-	-	-	-
3.1770	360	0.3542	-	-	-	-	-	-
3.2655	370	0.5868	-	-	-	-	-	-
3.3540	380	0.4988	-	-	-	-	-	-
3.4425	390	0.4577	-	-	-	-	-	-
3.5310	400	0.4735	-	-	-	-	-	-
3.6195	410	1.2588	-	-	-	-	-	-
3.7080	420	0.6346	-	-	-	-	-	-
3.7965	430	0.3013	-	-	-	-	-	-
3.8850	440	0.6734	-	-	-	-	-	-
3.9735	450	0.3469	-	-	-	-	-	-
3.9912	452	-	0.2943	0.2932	0.2966	0.2943	0.2964	0.2968

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

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}
}