This is a sentence-transformers model finetuned from mixedbread-ai/mxbai-embed-large-v1 on the Mollel/swahili-n_li-triplet-swh-eng dataset. 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: mixedbread-ai/mxbai-embed-large-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- Mollel/swahili-n_li-triplet-swh-eng

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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mollel/MultiLinguSwahili-mxbai-embed-large-v1-nli-matryoshka")
# Run inference
sentences = [
    'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
    'mwanamume na mwanamke wenye mikoba',
    'tai huruka',
]
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

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7133
spearman_cosine	0.7052
pearson_manhattan	0.6351
spearman_manhattan	0.6245
pearson_euclidean	0.6378
spearman_euclidean	0.6266
pearson_dot	0.5704
spearman_dot	0.5603
pearson_max	0.7133
spearman_max	0.7052

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7123
spearman_cosine	0.7036
pearson_manhattan	0.6388
spearman_manhattan	0.6281
pearson_euclidean	0.6419
spearman_euclidean	0.6311
pearson_dot	0.549
spearman_dot	0.5413
pearson_max	0.7123
spearman_max	0.7036

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7078
spearman_cosine	0.7001
pearson_manhattan	0.6433
spearman_manhattan	0.6325
pearson_euclidean	0.646
spearman_euclidean	0.6344
pearson_dot	0.5115
spearman_dot	0.506
pearson_max	0.7078
spearman_max	0.7001

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7029
spearman_cosine	0.6968
pearson_manhattan	0.6497
spearman_manhattan	0.6389
pearson_euclidean	0.6506
spearman_euclidean	0.6389
pearson_dot	0.4732
spearman_dot	0.4689
pearson_max	0.7029
spearman_max	0.6968

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6904
spearman_cosine	0.6849
pearson_manhattan	0.6498
spearman_manhattan	0.636
pearson_euclidean	0.6513
spearman_euclidean	0.637
pearson_dot	0.4213
spearman_dot	0.418
pearson_max	0.6904
spearman_max	0.6849

Training Details

Training Dataset

Mollel/swahili-n_li-triplet-swh-eng

Dataset: Mollel/swahili-n_li-triplet-swh-eng
Size: 1,115,700 training samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 7 tokens
mean: 15.18 tokens
max: 80 tokens
min: 5 tokens
mean: 18.53 tokens
max: 52 tokens
min: 5 tokens
mean: 17.8 tokens
max: 53 tokens

Samples:

anchor	positive	negative
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`A person is at a diner, ordering an omelette.`
`Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika.`	`Mtu yuko nje, juu ya farasi.`	`Mtu yuko kwenye mkahawa, akiagiza omelette.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Evaluation Dataset

Mollel/swahili-n_li-triplet-swh-eng

Dataset: Mollel/swahili-n_li-triplet-swh-eng
Size: 13,168 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 6 tokens
mean: 26.43 tokens
max: 94 tokens
min: 5 tokens
mean: 13.37 tokens
max: 65 tokens
min: 5 tokens
mean: 14.7 tokens
max: 54 tokens

Samples:

anchor	positive	negative
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`The men are fighting outside a deli.`
`Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda.`	`Wanawake wawili wanashikilia vifurushi.`	`Wanaume hao wanapigana nje ya duka la vyakula vitamu.`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`Two kids in numbered jerseys wash their hands.`	`Two kids in jackets walk to school.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
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: 1
max_steps: -1
lr_scheduler_type: linear
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
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: 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, '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: 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_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	sts-test-128_spearman_cosine	sts-test-256_spearman_cosine	sts-test-512_spearman_cosine	sts-test-64_spearman_cosine	sts-test-768_spearman_cosine
0.0029	100	9.6293	-	-	-	-	-
0.0057	200	8.1059	-	-	-	-	-
0.0086	300	8.6054	-	-	-	-	-
0.0115	400	6.8896	-	-	-	-	-
0.0143	500	6.9096	-	-	-	-	-
0.0172	600	6.7797	-	-	-	-	-
0.0201	700	6.8013	-	-	-	-	-
0.0229	800	7.49	-	-	-	-	-
0.0258	900	7.2888	-	-	-	-	-
0.0287	1000	7.3862	-	-	-	-	-
0.0315	1100	6.8292	-	-	-	-	-
0.0344	1200	6.2505	-	-	-	-	-
0.0373	1300	4.8736	-	-	-	-	-
0.0402	1400	4.7668	-	-	-	-	-
0.0430	1500	5.0843	-	-	-	-	-
0.0459	1600	3.8507	-	-	-	-	-
0.0488	1700	5.1235	-	-	-	-	-
0.0516	1800	4.6187	-	-	-	-	-
0.0545	1900	3.8704	-	-	-	-	-
0.0574	2000	3.3635	-	-	-	-	-
0.0602	2100	3.4204	-	-	-	-	-
0.0631	2200	3.5258	-	-	-	-	-
0.0660	2300	3.6726	-	-	-	-	-
0.0688	2400	3.8007	-	-	-	-	-
0.0717	2500	3.5593	-	-	-	-	-
0.0746	2600	3.3407	-	-	-	-	-
0.0774	2700	4.6645	-	-	-	-	-
0.0803	2800	4.5431	-	-	-	-	-
0.0832	2900	4.0496	-	-	-	-	-
0.0860	3000	3.8313	-	-	-	-	-
0.0889	3100	3.6324	-	-	-	-	-
0.0918	3200	3.3442	-	-	-	-	-
0.0946	3300	2.9437	-	-	-	-	-
0.0975	3400	2.8352	-	-	-	-	-
0.1004	3500	2.8069	-	-	-	-	-
0.1033	3600	2.9686	-	-	-	-	-
0.1061	3700	2.8355	-	-	-	-	-
0.1090	3800	2.9827	-	-	-	-	-
0.1119	3900	3.1181	-	-	-	-	-
0.1147	4000	4.1636	-	-	-	-	-
0.1176	4100	5.4112	-	-	-	-	-
0.1205	4200	5.3505	-	-	-	-	-
0.1233	4300	3.8779	-	-	-	-	-
0.1262	4400	3.7439	-	-	-	-	-
0.1291	4500	3.3232	-	-	-	-	-
0.1319	4600	3.6257	-	-	-	-	-
0.1348	4700	3.8231	-	-	-	-	-
0.1377	4800	3.4048	-	-	-	-	-
0.1405	4900	3.0996	-	-	-	-	-
0.1434	5000	3.386	-	-	-	-	-
0.1463	5100	2.8902	-	-	-	-	-
0.1491	5200	3.2461	-	-	-	-	-
0.1520	5300	2.6888	-	-	-	-	-
0.1549	5400	3.2005	-	-	-	-	-
0.1577	5500	3.1291	-	-	-	-	-
0.1606	5600	2.993	-	-	-	-	-
0.1635	5700	3.3405	-	-	-	-	-
0.1664	5800	3.3929	-	-	-	-	-
0.1692	5900	4.0071	-	-	-	-	-
0.1721	6000	3.8775	-	-	-	-	-
0.1750	6100	4.0725	-	-	-	-	-
0.1778	6200	4.3434	-	-	-	-	-
0.1807	6300	4.0734	-	-	-	-	-
0.1836	6400	3.805	-	-	-	-	-
0.1864	6500	3.9273	-	-	-	-	-
0.1893	6600	3.9514	-	-	-	-	-
0.1922	6700	3.8316	-	-	-	-	-
0.1950	6800	3.2888	-	-	-	-	-
0.1979	6900	3.4367	-	-	-	-	-
0.2008	7000	3.0205	-	-	-	-	-
0.2036	7100	3.404	-	-	-	-	-
0.2065	7200	3.225	-	-	-	-	-
0.2094	7300	3.8446	-	-	-	-	-
0.2122	7400	3.2551	-	-	-	-	-
0.2151	7500	3.35	-	-	-	-	-
0.2180	7600	3.5524	-	-	-	-	-
0.2208	7700	3.7775	-	-	-	-	-
0.2237	7800	3.2797	-	-	-	-	-
0.2266	7900	3.96	-	-	-	-	-
0.2294	8000	3.7124	-	-	-	-	-
0.2323	8100	3.2713	-	-	-	-	-
0.2352	8200	3.8838	-	-	-	-	-
0.2381	8300	3.3932	-	-	-	-	-
0.2409	8400	3.3798	-	-	-	-	-
0.2438	8500	3.2386	-	-	-	-	-
0.2467	8600	3.1264	-	-	-	-	-
0.2495	8700	3.9248	-	-	-	-	-
0.2524	8800	3.5402	-	-	-	-	-
0.2553	8900	3.688	-	-	-	-	-
0.2581	9000	4.0903	-	-	-	-	-
0.2610	9100	4.4358	-	-	-	-	-
0.2639	9200	4.1334	-	-	-	-	-
0.2667	9300	3.4894	-	-	-	-	-
0.2696	9400	4.0032	-	-	-	-	-
0.2725	9500	4.1421	-	-	-	-	-
0.2753	9600	3.6995	-	-	-	-	-
0.2782	9700	3.8307	-	-	-	-	-
0.2811	9800	3.7448	-	-	-	-	-
0.2839	9900	3.6962	-	-	-	-	-
0.2868	10000	3.3733	-	-	-	-	-
0.2897	10100	3.4597	-	-	-	-	-
0.2925	10200	3.6834	-	-	-	-	-
0.2954	10300	3.7873	-	-	-	-	-
0.2983	10400	3.1388	-	-	-	-	-
0.3012	10500	3.9492	-	-	-	-	-
0.3040	10600	3.5991	-	-	-	-	-
0.3069	10700	4.2448	-	-	-	-	-
0.3098	10800	3.92	-	-	-	-	-
0.3126	10900	3.8442	-	-	-	-	-
0.3155	11000	4.3227	-	-	-	-	-
0.3184	11100	3.6447	-	-	-	-	-
0.3212	11200	3.8106	-	-	-	-	-
0.3241	11300	3.3499	-	-	-	-	-
0.3270	11400	3.8586	-	-	-	-	-
0.3298	11500	3.4284	-	-	-	-	-
0.3327	11600	3.2439	-	-	-	-	-
0.3356	11700	3.6645	-	-	-	-	-
0.3384	11800	3.9315	-	-	-	-	-
0.3413	11900	3.6439	-	-	-	-	-
0.3442	12000	3.6706	-	-	-	-	-
0.3470	12100	3.5084	-	-	-	-	-
0.3499	12200	3.9352	-	-	-	-	-
0.3528	12300	3.7615	-	-	-	-	-
0.3556	12400	3.7642	-	-	-	-	-
0.3585	12500	3.8085	-	-	-	-	-
0.3614	12600	3.411	-	-	-	-	-
0.3643	12700	3.8521	-	-	-	-	-
0.3671	12800	3.5473	-	-	-	-	-
0.3700	12900	3.5322	-	-	-	-	-
0.3729	13000	3.1496	-	-	-	-	-
0.3757	13100	3.5285	-	-	-	-	-
0.3786	13200	4.4428	-	-	-	-	-
0.3815	13300	3.4391	-	-	-	-	-
0.3843	13400	3.6457	-	-	-	-	-
0.3872	13500	3.2051	-	-	-	-	-
0.3901	13600	3.3738	-	-	-	-	-
0.3929	13700	3.5465	-	-	-	-	-
0.3958	13800	3.5853	-	-	-	-	-
0.3987	13900	3.297	-	-	-	-	-
0.4015	14000	3.3994	-	-	-	-	-
0.4044	14100	3.542	-	-	-	-	-
0.4073	14200	3.8516	-	-	-	-	-
0.4101	14300	3.6002	-	-	-	-	-
0.4130	14400	3.7251	-	-	-	-	-
0.4159	14500	3.4421	-	-	-	-	-
0.4187	14600	3.365	-	-	-	-	-
0.4216	14700	3.5327	-	-	-	-	-
0.4245	14800	3.1557	-	-	-	-	-
0.4274	14900	3.7096	-	-	-	-	-
0.4302	15000	3.9073	-	-	-	-	-
0.4331	15100	3.2662	-	-	-	-	-
0.4360	15200	3.3979	-	-	-	-	-
0.4388	15300	3.1515	-	-	-	-	-
0.4417	15400	3.247	-	-	-	-	-
0.4446	15500	3.3723	-	-	-	-	-
0.4474	15600	3.6837	-	-	-	-	-
0.4503	15700	3.4302	-	-	-	-	-
0.4532	15800	3.8231	-	-	-	-	-
0.4560	15900	3.1679	-	-	-	-	-
0.4589	16000	3.2766	-	-	-	-	-
0.4618	16100	3.3	-	-	-	-	-
0.4646	16200	3.557	-	-	-	-	-
0.4675	16300	3.5876	-	-	-	-	-
0.4704	16400	3.0928	-	-	-	-	-
0.4732	16500	2.9105	-	-	-	-	-
0.4761	16600	3.254	-	-	-	-	-
0.4790	16700	3.8005	-	-	-	-	-
0.4818	16800	3.1539	-	-	-	-	-
0.4847	16900	3.0174	-	-	-	-	-
0.4876	17000	3.4317	-	-	-	-	-
0.4904	17100	3.6292	-	-	-	-	-
0.4933	17200	3.7037	-	-	-	-	-
0.4962	17300	3.5144	-	-	-	-	-
0.4991	17400	3.7012	-	-	-	-	-
0.5019	17500	3.2587	-	-	-	-	-
0.5048	17600	3.1335	-	-	-	-	-
0.5077	17700	3.4027	-	-	-	-	-
0.5105	17800	3.6637	-	-	-	-	-
0.5134	17900	3.1682	-	-	-	-	-
0.5163	18000	3.2303	-	-	-	-	-
0.5191	18100	3.2155	-	-	-	-	-
0.5220	18200	3.431	-	-	-	-	-
0.5249	18300	3.1019	-	-	-	-	-
0.5277	18400	3.5245	-	-	-	-	-
0.5306	18500	3.1072	-	-	-	-	-
0.5335	18600	2.9673	-	-	-	-	-
0.5363	18700	3.0401	-	-	-	-	-
0.5392	18800	3.0617	-	-	-	-	-
0.5421	18900	3.6658	-	-	-	-	-
0.5449	19000	3.5137	-	-	-	-	-
0.5478	19100	3.5897	-	-	-	-	-
0.5507	19200	2.8309	-	-	-	-	-
0.5535	19300	3.7047	-	-	-	-	-
0.5564	19400	3.3343	-	-	-	-	-
0.5593	19500	3.3689	-	-	-	-	-
0.5622	19600	3.1783	-	-	-	-	-
0.5650	19700	3.6135	-	-	-	-	-
0.5679	19800	3.5106	-	-	-	-	-
0.5708	19900	3.8416	-	-	-	-	-
0.5736	20000	3.1559	-	-	-	-	-
0.5765	20100	3.2931	-	-	-	-	-
0.5794	20200	3.2411	-	-	-	-	-
0.5822	20300	3.5898	-	-	-	-	-
0.5851	20400	3.2916	-	-	-	-	-
0.5880	20500	3.619	-	-	-	-	-
0.5908	20600	3.8023	-	-	-	-	-
0.5937	20700	3.1023	-	-	-	-	-
0.5966	20800	3.2682	-	-	-	-	-
0.5994	20900	2.9783	-	-	-	-	-
0.6023	21000	3.1373	-	-	-	-	-
0.6052	21100	3.5358	-	-	-	-	-
0.6080	21200	3.2374	-	-	-	-	-
0.6109	21300	3.6793	-	-	-	-	-
0.6138	21400	3.388	-	-	-	-	-
0.6166	21500	3.1295	-	-	-	-	-
0.6195	21600	3.7971	-	-	-	-	-
0.6224	21700	3.4638	-	-	-	-	-
0.6253	21800	3.1254	-	-	-	-	-
0.6281	21900	3.705	-	-	-	-	-
0.6310	22000	2.9319	-	-	-	-	-
0.6339	22100	3.6908	-	-	-	-	-
0.6367	22200	3.3938	-	-	-	-	-
0.6396	22300	3.389	-	-	-	-	-
0.6425	22400	2.9946	-	-	-	-	-
0.6453	22500	3.9109	-	-	-	-	-
0.6482	22600	3.4698	-	-	-	-	-
0.6511	22700	3.1229	-	-	-	-	-
0.6539	22800	3.3769	-	-	-	-	-
0.6568	22900	3.1849	-	-	-	-	-
0.6597	23000	3.4464	-	-	-	-	-
0.6625	23100	2.9192	-	-	-	-	-
0.6654	23200	3.0796	-	-	-	-	-
0.6683	23300	3.4603	-	-	-	-	-
0.6711	23400	3.6775	-	-	-	-	-
0.6740	23500	3.5132	-	-	-	-	-
0.6769	23600	3.7764	-	-	-	-	-
0.6797	23700	3.0643	-	-	-	-	-
0.6826	23800	3.1545	-	-	-	-	-
0.6855	23900	2.997	-	-	-	-	-
0.6883	24000	3.1385	-	-	-	-	-
0.6912	24100	3.3879	-	-	-	-	-
0.6941	24200	3.5442	-	-	-	-	-
0.6970	24300	3.3687	-	-	-	-	-
0.6998	24400	3.4195	-	-	-	-	-
0.7027	24500	3.4057	-	-	-	-	-
0.7056	24600	3.2503	-	-	-	-	-
0.7084	24700	3.3703	-	-	-	-	-
0.7113	24800	3.0839	-	-	-	-	-
0.7142	24900	3.11	-	-	-	-	-
0.7170	25000	3.1105	-	-	-	-	-
0.7199	25100	2.8735	-	-	-	-	-
0.7228	25200	3.0287	-	-	-	-	-
0.7256	25300	3.2992	-	-	-	-	-
0.7285	25400	3.2015	-	-	-	-	-
0.7314	25500	3.3135	-	-	-	-	-
0.7342	25600	3.1618	-	-	-	-	-
0.7371	25700	3.5939	-	-	-	-	-
0.7400	25800	2.9016	-	-	-	-	-
0.7428	25900	3.2528	-	-	-	-	-
0.7457	26000	3.5005	-	-	-	-	-
0.7486	26100	3.2494	-	-	-	-	-
0.7514	26200	2.618	-	-	-	-	-
0.7543	26300	4.3413	-	-	-	-	-
0.7572	26400	4.0215	-	-	-	-	-
0.7601	26500	3.6406	-	-	-	-	-
0.7629	26600	3.6815	-	-	-	-	-
0.7658	26700	3.6911	-	-	-	-	-
0.7687	26800	3.3901	-	-	-	-	-
0.7715	26900	3.7262	-	-	-	-	-
0.7744	27000	3.3099	-	-	-	-	-
0.7773	27100	3.2131	-	-	-	-	-
0.7801	27200	3.1818	-	-	-	-	-
0.7830	27300	3.3306	-	-	-	-	-
0.7859	27400	3.4347	-	-	-	-	-
0.7887	27500	3.1169	-	-	-	-	-
0.7916	27600	3.2788	-	-	-	-	-
0.7945	27700	3.3876	-	-	-	-	-
0.7973	27800	3.0329	-	-	-	-	-
0.8002	27900	2.9935	-	-	-	-	-
0.8031	28000	3.0313	-	-	-	-	-
0.8059	28100	3.0293	-	-	-	-	-
0.8088	28200	3.0225	-	-	-	-	-
0.8117	28300	2.9378	-	-	-	-	-
0.8145	28400	2.8588	-	-	-	-	-
0.8174	28500	3.0936	-	-	-	-	-
0.8203	28600	2.9192	-	-	-	-	-
0.8232	28700	3.0259	-	-	-	-	-
0.8260	28800	2.76	-	-	-	-	-
0.8289	28900	3.0673	-	-	-	-	-
0.8318	29000	2.9333	-	-	-	-	-
0.8346	29100	2.9847	-	-	-	-	-
0.8375	29200	2.9882	-	-	-	-	-
0.8404	29300	2.9578	-	-	-	-	-
0.8432	29400	2.8535	-	-	-	-	-
0.8461	29500	3.012	-	-	-	-	-
0.8490	29600	2.6693	-	-	-	-	-
0.8518	29700	2.9026	-	-	-	-	-
0.8547	29800	2.7965	-	-	-	-	-
0.8576	29900	2.8402	-	-	-	-	-
0.8604	30000	2.6286	-	-	-	-	-
0.8633	30100	2.6588	-	-	-	-	-
0.8662	30200	2.6185	-	-	-	-	-
0.8690	30300	2.785	-	-	-	-	-
0.8719	30400	2.7637	-	-	-	-	-
0.8748	30500	2.8271	-	-	-	-	-
0.8776	30600	2.6788	-	-	-	-	-
0.8805	30700	2.5934	-	-	-	-	-
0.8834	30800	2.7782	-	-	-	-	-
0.8863	30900	2.7925	-	-	-	-	-
0.8891	31000	2.6091	-	-	-	-	-
0.8920	31100	2.7123	-	-	-	-	-
0.8949	31200	2.6067	-	-	-	-	-
0.8977	31300	2.65	-	-	-	-	-
0.9006	31400	2.7695	-	-	-	-	-
0.9035	31500	2.7075	-	-	-	-	-
0.9063	31600	2.5539	-	-	-	-	-
0.9092	31700	2.5283	-	-	-	-	-
0.9121	31800	2.7156	-	-	-	-	-
0.9149	31900	2.4318	-	-	-	-	-
0.9178	32000	2.7335	-	-	-	-	-
0.9207	32100	2.4435	-	-	-	-	-
0.9235	32200	2.6529	-	-	-	-	-
0.9264	32300	2.568	-	-	-	-	-
0.9293	32400	2.5639	-	-	-	-	-
0.9321	32500	2.6727	-	-	-	-	-
0.9350	32600	2.5063	-	-	-	-	-
0.9379	32700	2.5447	-	-	-	-	-
0.9407	32800	2.5767	-	-	-	-	-
0.9436	32900	2.5155	-	-	-	-	-
0.9465	33000	2.4016	-	-	-	-	-
0.9493	33100	2.7624	-	-	-	-	-
0.9522	33200	2.5887	-	-	-	-	-
0.9551	33300	2.5945	-	-	-	-	-
0.9580	33400	2.4295	-	-	-	-	-
0.9608	33500	2.6082	-	-	-	-	-
0.9637	33600	2.5034	-	-	-	-	-
0.9666	33700	2.5149	-	-	-	-	-
0.9694	33800	2.5311	-	-	-	-	-
0.9723	33900	2.6413	-	-	-	-	-
0.9752	34000	2.6304	-	-	-	-	-
0.9780	34100	2.5159	-	-	-	-	-
0.9809	34200	2.701	-	-	-	-	-
0.9838	34300	2.3928	-	-	-	-	-
0.9866	34400	2.5428	-	-	-	-	-
0.9895	34500	2.4652	-	-	-	-	-
0.9924	34600	2.7281	-	-	-	-	-
0.9952	34700	2.4693	-	-	-	-	-
0.9981	34800	2.4129	-	-	-	-	-
1.0	34866	-	0.6968	0.7001	0.7036	0.6849	0.7052

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.1
Transformers: 4.40.1
PyTorch: 2.3.0+cu121
Accelerate: 0.29.3
Datasets: 2.19.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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

bibtex

@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}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 15.18 tokens max: 80 tokens	min: 5 tokens mean: 18.53 tokens max: 52 tokens	min: 5 tokens mean: 17.8 tokens max: 53 tokens