SentenceTransformer based on Geotrend/bert-base-sw-cased
This is a sentence-transformers model finetuned from Geotrend/bert-base-sw-cased. 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: Geotrend/bert-base-sw-cased
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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/swahili-bert-base-sw-cased-nli-matryoshka")
sentences = [
'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.',
'Mwanamume amelala uso chini kwenye benchi ya bustani.',
'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.6869 |
spearman_cosine |
0.6802 |
pearson_manhattan |
0.6719 |
spearman_manhattan |
0.6653 |
pearson_euclidean |
0.6734 |
spearman_euclidean |
0.6666 |
pearson_dot |
0.554 |
spearman_dot |
0.5399 |
pearson_max |
0.6869 |
spearman_max |
0.6802 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.6828 |
spearman_cosine |
0.677 |
pearson_manhattan |
0.6729 |
spearman_manhattan |
0.6664 |
pearson_euclidean |
0.6738 |
spearman_euclidean |
0.6667 |
pearson_dot |
0.5296 |
spearman_dot |
0.5174 |
pearson_max |
0.6828 |
spearman_max |
0.677 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.6758 |
spearman_cosine |
0.6702 |
pearson_manhattan |
0.6718 |
spearman_manhattan |
0.6643 |
pearson_euclidean |
0.673 |
spearman_euclidean |
0.665 |
pearson_dot |
0.4892 |
spearman_dot |
0.4783 |
pearson_max |
0.6758 |
spearman_max |
0.6702 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.67 |
spearman_cosine |
0.6638 |
pearson_manhattan |
0.6693 |
spearman_manhattan |
0.6594 |
pearson_euclidean |
0.671 |
spearman_euclidean |
0.6601 |
pearson_dot |
0.4509 |
spearman_dot |
0.4402 |
pearson_max |
0.671 |
spearman_max |
0.6638 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.6615 |
spearman_cosine |
0.6556 |
pearson_manhattan |
0.6653 |
spearman_manhattan |
0.6533 |
pearson_euclidean |
0.6672 |
spearman_euclidean |
0.654 |
pearson_dot |
0.3868 |
spearman_dot |
0.3771 |
pearson_max |
0.6672 |
spearman_max |
0.6556 |
Training Details
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 |
0.0057 |
100 |
20.0932 |
- |
- |
- |
- |
- |
0.0115 |
200 |
16.2641 |
- |
- |
- |
- |
- |
0.0172 |
300 |
12.797 |
- |
- |
- |
- |
- |
0.0229 |
400 |
12.1927 |
- |
- |
- |
- |
- |
0.0287 |
500 |
11.0423 |
- |
- |
- |
- |
- |
0.0344 |
600 |
9.676 |
- |
- |
- |
- |
- |
0.0402 |
700 |
8.1545 |
- |
- |
- |
- |
- |
0.0459 |
800 |
7.7822 |
- |
- |
- |
- |
- |
0.0516 |
900 |
7.9352 |
- |
- |
- |
- |
- |
0.0574 |
1000 |
7.9534 |
- |
- |
- |
- |
- |
0.0631 |
1100 |
8.1006 |
- |
- |
- |
- |
- |
0.0688 |
1200 |
7.4767 |
- |
- |
- |
- |
- |
0.0746 |
1300 |
8.3747 |
- |
- |
- |
- |
- |
0.0803 |
1400 |
7.7686 |
- |
- |
- |
- |
- |
0.0860 |
1500 |
6.8076 |
- |
- |
- |
- |
- |
0.0918 |
1600 |
6.9238 |
- |
- |
- |
- |
- |
0.0975 |
1700 |
6.5503 |
- |
- |
- |
- |
- |
0.1033 |
1800 |
6.74 |
- |
- |
- |
- |
- |
0.1090 |
1900 |
7.7802 |
- |
- |
- |
- |
- |
0.1147 |
2000 |
7.2594 |
- |
- |
- |
- |
- |
0.1205 |
2100 |
7.091 |
- |
- |
- |
- |
- |
0.1262 |
2200 |
6.8677 |
- |
- |
- |
- |
- |
0.1319 |
2300 |
6.4249 |
- |
- |
- |
- |
- |
0.1377 |
2400 |
6.1512 |
- |
- |
- |
- |
- |
0.1434 |
2500 |
5.9714 |
- |
- |
- |
- |
- |
0.1491 |
2600 |
5.4914 |
- |
- |
- |
- |
- |
0.1549 |
2700 |
5.5825 |
- |
- |
- |
- |
- |
0.1606 |
2800 |
5.9456 |
- |
- |
- |
- |
- |
0.1664 |
2900 |
6.4012 |
- |
- |
- |
- |
- |
0.1721 |
3000 |
7.1999 |
- |
- |
- |
- |
- |
0.1778 |
3100 |
6.8254 |
- |
- |
- |
- |
- |
0.1836 |
3200 |
6.541 |
- |
- |
- |
- |
- |
0.1893 |
3300 |
6.5411 |
- |
- |
- |
- |
- |
0.1950 |
3400 |
5.56 |
- |
- |
- |
- |
- |
0.2008 |
3500 |
6.4692 |
- |
- |
- |
- |
- |
0.2065 |
3600 |
5.9266 |
- |
- |
- |
- |
- |
0.2122 |
3700 |
6.2055 |
- |
- |
- |
- |
- |
0.2180 |
3800 |
6.0835 |
- |
- |
- |
- |
- |
0.2237 |
3900 |
6.6112 |
- |
- |
- |
- |
- |
0.2294 |
4000 |
6.3391 |
- |
- |
- |
- |
- |
0.2352 |
4100 |
5.8379 |
- |
- |
- |
- |
- |
0.2409 |
4200 |
5.8107 |
- |
- |
- |
- |
- |
0.2467 |
4300 |
6.1473 |
- |
- |
- |
- |
- |
0.2524 |
4400 |
6.2827 |
- |
- |
- |
- |
- |
0.2581 |
4500 |
6.2299 |
- |
- |
- |
- |
- |
0.2639 |
4600 |
6.1013 |
- |
- |
- |
- |
- |
0.2696 |
4700 |
5.6491 |
- |
- |
- |
- |
- |
0.2753 |
4800 |
5.8641 |
- |
- |
- |
- |
- |
0.2811 |
4900 |
5.4278 |
- |
- |
- |
- |
- |
0.2868 |
5000 |
5.7304 |
- |
- |
- |
- |
- |
0.2925 |
5100 |
5.4652 |
- |
- |
- |
- |
- |
0.2983 |
5200 |
5.9031 |
- |
- |
- |
- |
- |
0.3040 |
5300 |
6.1014 |
- |
- |
- |
- |
- |
0.3098 |
5400 |
5.9282 |
- |
- |
- |
- |
- |
0.3155 |
5500 |
5.6618 |
- |
- |
- |
- |
- |
0.3212 |
5600 |
5.3803 |
- |
- |
- |
- |
- |
0.3270 |
5700 |
5.5759 |
- |
- |
- |
- |
- |
0.3327 |
5800 |
5.6936 |
- |
- |
- |
- |
- |
0.3384 |
5900 |
5.7249 |
- |
- |
- |
- |
- |
0.3442 |
6000 |
5.5926 |
- |
- |
- |
- |
- |
0.3499 |
6100 |
5.6329 |
- |
- |
- |
- |
- |
0.3556 |
6200 |
5.7456 |
- |
- |
- |
- |
- |
0.3614 |
6300 |
5.1638 |
- |
- |
- |
- |
- |
0.3671 |
6400 |
5.3258 |
- |
- |
- |
- |
- |
0.3729 |
6500 |
5.1216 |
- |
- |
- |
- |
- |
0.3786 |
6600 |
5.7453 |
- |
- |
- |
- |
- |
0.3843 |
6700 |
4.9906 |
- |
- |
- |
- |
- |
0.3901 |
6800 |
5.1126 |
- |
- |
- |
- |
- |
0.3958 |
6900 |
5.2389 |
- |
- |
- |
- |
- |
0.4015 |
7000 |
5.1483 |
- |
- |
- |
- |
- |
0.4073 |
7100 |
5.6072 |
- |
- |
- |
- |
- |
0.4130 |
7200 |
5.2018 |
- |
- |
- |
- |
- |
0.4187 |
7300 |
5.4083 |
- |
- |
- |
- |
- |
0.4245 |
7400 |
5.1995 |
- |
- |
- |
- |
- |
0.4302 |
7500 |
5.5787 |
- |
- |
- |
- |
- |
0.4360 |
7600 |
4.9942 |
- |
- |
- |
- |
- |
0.4417 |
7700 |
4.9196 |
- |
- |
- |
- |
- |
0.4474 |
7800 |
5.3938 |
- |
- |
- |
- |
- |
0.4532 |
7900 |
5.381 |
- |
- |
- |
- |
- |
0.4589 |
8000 |
4.908 |
- |
- |
- |
- |
- |
0.4646 |
8100 |
4.8871 |
- |
- |
- |
- |
- |
0.4704 |
8200 |
5.2298 |
- |
- |
- |
- |
- |
0.4761 |
8300 |
4.6157 |
- |
- |
- |
- |
- |
0.4818 |
8400 |
5.0344 |
- |
- |
- |
- |
- |
0.4876 |
8500 |
5.0713 |
- |
- |
- |
- |
- |
0.4933 |
8600 |
5.1952 |
- |
- |
- |
- |
- |
0.4991 |
8700 |
5.5352 |
- |
- |
- |
- |
- |
0.5048 |
8800 |
5.1556 |
- |
- |
- |
- |
- |
0.5105 |
8900 |
5.2318 |
- |
- |
- |
- |
- |
0.5163 |
9000 |
4.7887 |
- |
- |
- |
- |
- |
0.5220 |
9100 |
4.868 |
- |
- |
- |
- |
- |
0.5277 |
9200 |
4.9544 |
- |
- |
- |
- |
- |
0.5335 |
9300 |
4.816 |
- |
- |
- |
- |
- |
0.5392 |
9400 |
4.8374 |
- |
- |
- |
- |
- |
0.5449 |
9500 |
5.3242 |
- |
- |
- |
- |
- |
0.5507 |
9600 |
4.9039 |
- |
- |
- |
- |
- |
0.5564 |
9700 |
5.2907 |
- |
- |
- |
- |
- |
0.5622 |
9800 |
5.4007 |
- |
- |
- |
- |
- |
0.5679 |
9900 |
5.3016 |
- |
- |
- |
- |
- |
0.5736 |
10000 |
5.3235 |
- |
- |
- |
- |
- |
0.5794 |
10100 |
5.1566 |
- |
- |
- |
- |
- |
0.5851 |
10200 |
5.1348 |
- |
- |
- |
- |
- |
0.5908 |
10300 |
5.4583 |
- |
- |
- |
- |
- |
0.5966 |
10400 |
4.9528 |
- |
- |
- |
- |
- |
0.6023 |
10500 |
5.0073 |
- |
- |
- |
- |
- |
0.6080 |
10600 |
5.0324 |
- |
- |
- |
- |
- |
0.6138 |
10700 |
5.4107 |
- |
- |
- |
- |
- |
0.6195 |
10800 |
5.3643 |
- |
- |
- |
- |
- |
0.6253 |
10900 |
5.1267 |
- |
- |
- |
- |
- |
0.6310 |
11000 |
5.0443 |
- |
- |
- |
- |
- |
0.6367 |
11100 |
5.2001 |
- |
- |
- |
- |
- |
0.6425 |
11200 |
4.8813 |
- |
- |
- |
- |
- |
0.6482 |
11300 |
5.4734 |
- |
- |
- |
- |
- |
0.6539 |
11400 |
5.0344 |
- |
- |
- |
- |
- |
0.6597 |
11500 |
5.5043 |
- |
- |
- |
- |
- |
0.6654 |
11600 |
4.6201 |
- |
- |
- |
- |
- |
0.6711 |
11700 |
5.4626 |
- |
- |
- |
- |
- |
0.6769 |
11800 |
5.3813 |
- |
- |
- |
- |
- |
0.6826 |
11900 |
4.626 |
- |
- |
- |
- |
- |
0.6883 |
12000 |
4.87 |
- |
- |
- |
- |
- |
0.6941 |
12100 |
5.0015 |
- |
- |
- |
- |
- |
0.6998 |
12200 |
4.962 |
- |
- |
- |
- |
- |
0.7056 |
12300 |
5.1613 |
- |
- |
- |
- |
- |
0.7113 |
12400 |
5.2074 |
- |
- |
- |
- |
- |
0.7170 |
12500 |
4.958 |
- |
- |
- |
- |
- |
0.7228 |
12600 |
4.4516 |
- |
- |
- |
- |
- |
0.7285 |
12700 |
4.8421 |
- |
- |
- |
- |
- |
0.7342 |
12800 |
4.9242 |
- |
- |
- |
- |
- |
0.7400 |
12900 |
4.9256 |
- |
- |
- |
- |
- |
0.7457 |
13000 |
4.8254 |
- |
- |
- |
- |
- |
0.7514 |
13100 |
4.5114 |
- |
- |
- |
- |
- |
0.7572 |
13200 |
7.7118 |
- |
- |
- |
- |
- |
0.7629 |
13300 |
7.0822 |
- |
- |
- |
- |
- |
0.7687 |
13400 |
6.8022 |
- |
- |
- |
- |
- |
0.7744 |
13500 |
6.7295 |
- |
- |
- |
- |
- |
0.7801 |
13600 |
6.0547 |
- |
- |
- |
- |
- |
0.7859 |
13700 |
6.5285 |
- |
- |
- |
- |
- |
0.7916 |
13800 |
6.2666 |
- |
- |
- |
- |
- |
0.7973 |
13900 |
6.1031 |
- |
- |
- |
- |
- |
0.8031 |
14000 |
5.9138 |
- |
- |
- |
- |
- |
0.8088 |
14100 |
5.6636 |
- |
- |
- |
- |
- |
0.8145 |
14200 |
5.7073 |
- |
- |
- |
- |
- |
0.8203 |
14300 |
5.7963 |
- |
- |
- |
- |
- |
0.8260 |
14400 |
5.7336 |
- |
- |
- |
- |
- |
0.8318 |
14500 |
5.8113 |
- |
- |
- |
- |
- |
0.8375 |
14600 |
5.6708 |
- |
- |
- |
- |
- |
0.8432 |
14700 |
5.4565 |
- |
- |
- |
- |
- |
0.8490 |
14800 |
5.4293 |
- |
- |
- |
- |
- |
0.8547 |
14900 |
5.4166 |
- |
- |
- |
- |
- |
0.8604 |
15000 |
5.3616 |
- |
- |
- |
- |
- |
0.8662 |
15100 |
5.1579 |
- |
- |
- |
- |
- |
0.8719 |
15200 |
5.3887 |
- |
- |
- |
- |
- |
0.8776 |
15300 |
5.346 |
- |
- |
- |
- |
- |
0.8834 |
15400 |
5.2762 |
- |
- |
- |
- |
- |
0.8891 |
15500 |
5.3417 |
- |
- |
- |
- |
- |
0.8949 |
15600 |
5.1607 |
- |
- |
- |
- |
- |
0.9006 |
15700 |
5.4493 |
- |
- |
- |
- |
- |
0.9063 |
15800 |
5.0268 |
- |
- |
- |
- |
- |
0.9121 |
15900 |
5.0612 |
- |
- |
- |
- |
- |
0.9178 |
16000 |
5.1471 |
- |
- |
- |
- |
- |
0.9235 |
16100 |
4.8275 |
- |
- |
- |
- |
- |
0.9293 |
16200 |
5.1464 |
- |
- |
- |
- |
- |
0.9350 |
16300 |
4.958 |
- |
- |
- |
- |
- |
0.9407 |
16400 |
5.1968 |
- |
- |
- |
- |
- |
0.9465 |
16500 |
4.7783 |
- |
- |
- |
- |
- |
0.9522 |
16600 |
5.0834 |
- |
- |
- |
- |
- |
0.9580 |
16700 |
4.9839 |
- |
- |
- |
- |
- |
0.9637 |
16800 |
5.0078 |
- |
- |
- |
- |
- |
0.9694 |
16900 |
5.1624 |
- |
- |
- |
- |
- |
0.9752 |
17000 |
5.2132 |
- |
- |
- |
- |
- |
0.9809 |
17100 |
4.9741 |
- |
- |
- |
- |
- |
0.9866 |
17200 |
4.96 |
- |
- |
- |
- |
- |
0.9924 |
17300 |
5.1834 |
- |
- |
- |
- |
- |
0.9981 |
17400 |
4.8955 |
- |
- |
- |
- |
- |
1.0 |
17433 |
- |
0.6638 |
0.6702 |
0.6770 |
0.6556 |
0.6802 |
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}
}