SentenceTransformer based on mixedbread-ai/mxbai-embed-large-v1
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
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.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Mollel/MultiLinguSwahili-mxbai-embed-large-v1-nli-matryoshka")
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)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Semantic Similarity
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
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
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
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
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:{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
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:{
"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
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
@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}
}