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sartifyllc/
swahili-gte-base-en-v1.5-nli-matryoshka

Name: swahili-gte-base-en-v1.5-nli-matryoshka
Availability: InStock

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SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5. 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: Alibaba-NLP/gte-base-en-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

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: NewModel 
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sartifyllc/swahili-gte-base-en-v1.5-nli-matryoshka")
# Run inference
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)
# [3, 768]

# 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	0.7043
spearman_cosine	0.6964
pearson_manhattan	0.6909
spearman_manhattan	0.6919
pearson_euclidean	0.6929
spearman_euclidean	0.6938
pearson_dot	0.7012
spearman_dot	0.6918
pearson_max	0.7043
spearman_max	0.6964

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7024
spearman_cosine	0.6961
pearson_manhattan	0.6937
spearman_manhattan	0.6943
pearson_euclidean	0.6959
spearman_euclidean	0.6965
pearson_dot	0.674
spearman_dot	0.6636
pearson_max	0.7024
spearman_max	0.6965

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6976
spearman_cosine	0.6922
pearson_manhattan	0.7013
spearman_manhattan	0.7002
pearson_euclidean	0.7033
spearman_euclidean	0.7028
pearson_dot	0.6354
spearman_dot	0.6242
pearson_max	0.7033
spearman_max	0.7028

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.691
spearman_cosine	0.688
pearson_manhattan	0.7045
spearman_manhattan	0.7001
pearson_euclidean	0.7064
spearman_euclidean	0.7029
pearson_dot	0.5847
spearman_dot	0.5729
pearson_max	0.7064
spearman_max	0.7029

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.681
spearman_cosine	0.6803
pearson_manhattan	0.7053
spearman_manhattan	0.6987
pearson_euclidean	0.7061
spearman_euclidean	0.7004
pearson_dot	0.5177
spearman_dot	0.502
pearson_max	0.7061
spearman_max	0.7004

Training Details

Training Hyperparameters

Non-Default Hyperparameters

num_train_epochs: 1
warmup_ratio: 0.1
fp16: 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: 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
learning_rate: 5e-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: False
fp16: True
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	13.2716	-	-	-	-	-
0.0057	200	9.83	-	-	-	-	-
0.0086	300	9.9047	-	-	-	-	-
0.0115	400	7.5137	-	-	-	-	-
0.0143	500	7.6419	-	-	-	-	-
0.0172	600	6.9603	-	-	-	-	-
0.0201	700	7.3009	-	-	-	-	-
0.0229	800	7.1397	-	-	-	-	-
0.0258	900	8.1352	-	-	-	-	-
0.0287	1000	7.5945	-	-	-	-	-
0.0315	1100	7.0476	-	-	-	-	-
0.0344	1200	5.3356	-	-	-	-	-
0.0373	1300	5.1529	-	-	-	-	-
0.0402	1400	4.9726	-	-	-	-	-
0.0430	1500	5.1683	-	-	-	-	-
0.0459	1600	4.7945	-	-	-	-	-
0.0488	1700	4.9624	-	-	-	-	-
0.0516	1800	4.4254	-	-	-	-	-
0.0545	1900	4.4379	-	-	-	-	-
0.0574	2000	4.0327	-	-	-	-	-
0.0602	2100	3.5138	-	-	-	-	-
0.0631	2200	4.5055	-	-	-	-	-
0.0660	2300	3.8966	-	-	-	-	-
0.0688	2400	4.4884	-	-	-	-	-
0.0717	2500	3.5825	-	-	-	-	-
0.0746	2600	4.0155	-	-	-	-	-
0.0774	2700	4.9842	-	-	-	-	-
0.0803	2800	4.7732	-	-	-	-	-
0.0832	2900	4.5095	-	-	-	-	-
0.0860	3000	4.2526	-	-	-	-	-
0.0889	3100	4.033	-	-	-	-	-
0.0918	3200	4.0052	-	-	-	-	-
0.0946	3300	3.197	-	-	-	-	-
0.0975	3400	3.3423	-	-	-	-	-
0.1004	3500	2.9528	-	-	-	-	-
0.1033	3600	3.9315	-	-	-	-	-
0.1061	3700	3.7733	-	-	-	-	-
0.1090	3800	3.5153	-	-	-	-	-
0.1119	3900	4.1326	-	-	-	-	-
0.1147	4000	5.2179	-	-	-	-	-
0.1176	4100	6.4314	-	-	-	-	-
0.1205	4200	6.3485	-	-	-	-	-
0.1233	4300	4.7771	-	-	-	-	-
0.1262	4400	4.9055	-	-	-	-	-
0.1291	4500	3.9025	-	-	-	-	-
0.1319	4600	4.4638	-	-	-	-	-
0.1348	4700	5.0049	-	-	-	-	-
0.1377	4800	4.3124	-	-	-	-	-
0.1405	4900	4.0027	-	-	-	-	-
0.1434	5000	4.3173	-	-	-	-	-
0.1463	5100	3.6629	-	-	-	-	-
0.1491	5200	4.2759	-	-	-	-	-
0.1520	5300	3.4621	-	-	-	-	-
0.1549	5400	3.9251	-	-	-	-	-
0.1577	5500	4.2294	-	-	-	-	-
0.1606	5600	3.6244	-	-	-	-	-
0.1635	5700	4.283	-	-	-	-	-
0.1664	5800	4.4665	-	-	-	-	-
0.1692	5900	4.956	-	-	-	-	-
0.1721	6000	4.795	-	-	-	-	-
0.1750	6100	4.998	-	-	-	-	-
0.1778	6200	5.3316	-	-	-	-	-
0.1807	6300	5.2247	-	-	-	-	-
0.1836	6400	4.6554	-	-	-	-	-
0.1864	6500	5.2474	-	-	-	-	-
0.1893	6600	5.1168	-	-	-	-	-
0.1922	6700	5.1372	-	-	-	-	-
0.1950	6800	4.1564	-	-	-	-	-
0.1979	6900	4.6997	-	-	-	-	-
0.2008	7000	4.1854	-	-	-	-	-
0.2036	7100	4.4574	-	-	-	-	-
0.2065	7200	4.1859	-	-	-	-	-
0.2094	7300	4.8306	-	-	-	-	-
0.2122	7400	4.4487	-	-	-	-	-
0.2151	7500	4.4606	-	-	-	-	-
0.2180	7600	4.4222	-	-	-	-	-
0.2208	7700	4.7836	-	-	-	-	-
0.2237	7800	4.1475	-	-	-	-	-
0.2266	7900	5.1679	-	-	-	-	-
0.2294	8000	5.0106	-	-	-	-	-
0.2323	8100	4.1899	-	-	-	-	-
0.2352	8200	4.9873	-	-	-	-	-
0.2381	8300	4.3656	-	-	-	-	-
0.2409	8400	4.6117	-	-	-	-	-
0.2438	8500	4.1785	-	-	-	-	-
0.2467	8600	3.7809	-	-	-	-	-
0.2495	8700	4.9116	-	-	-	-	-
0.2524	8800	4.553	-	-	-	-	-
0.2553	8900	4.3178	-	-	-	-	-
0.2581	9000	5.6111	-	-	-	-	-
0.2610	9100	5.4219	-	-	-	-	-
0.2639	9200	5.5628	-	-	-	-	-
0.2667	9300	4.4221	-	-	-	-	-
0.2696	9400	4.7988	-	-	-	-	-
0.2725	9500	4.9361	-	-	-	-	-
0.2753	9600	4.7225	-	-	-	-	-
0.2782	9700	4.7258	-	-	-	-	-
0.2811	9800	4.7071	-	-	-	-	-
0.2839	9900	4.5519	-	-	-	-	-
0.2868	10000	4.5354	-	-	-	-	-
0.2897	10100	4.3893	-	-	-	-	-
0.2925	10200	4.7848	-	-	-	-	-
0.2954	10300	4.7195	-	-	-	-	-
0.2983	10400	4.0155	-	-	-	-	-
0.3012	10500	5.1602	-	-	-	-	-
0.3040	10600	4.6345	-	-	-	-	-
0.3069	10700	5.39	-	-	-	-	-
0.3098	10800	4.7974	-	-	-	-	-
0.3126	10900	4.9736	-	-	-	-	-
0.3155	11000	5.0949	-	-	-	-	-
0.3184	11100	4.6704	-	-	-	-	-
0.3212	11200	4.7001	-	-	-	-	-
0.3241	11300	4.2913	-	-	-	-	-
0.3270	11400	4.7536	-	-	-	-	-
0.3298	11500	4.8349	-	-	-	-	-
0.3327	11600	4.2567	-	-	-	-	-
0.3356	11700	4.6754	-	-	-	-	-
0.3384	11800	4.8534	-	-	-	-	-
0.3413	11900	4.7486	-	-	-	-	-
0.3442	12000	4.9194	-	-	-	-	-
0.3470	12100	4.4572	-	-	-	-	-
0.3499	12200	4.6173	-	-	-	-	-
0.3528	12300	5.1292	-	-	-	-	-
0.3556	12400	4.6138	-	-	-	-	-
0.3585	12500	4.6884	-	-	-	-	-
0.3614	12600	4.4245	-	-	-	-	-
0.3643	12700	4.7534	-	-	-	-	-
0.3671	12800	4.7027	-	-	-	-	-
0.3700	12900	4.5186	-	-	-	-	-
0.3729	13000	3.8917	-	-	-	-	-
0.3757	13100	4.507	-	-	-	-	-
0.3786	13200	5.4866	-	-	-	-	-
0.3815	13300	4.0424	-	-	-	-	-
0.3843	13400	4.4017	-	-	-	-	-
0.3872	13500	4.0016	-	-	-	-	-
0.3901	13600	4.0695	-	-	-	-	-
0.3929	13700	4.4957	-	-	-	-	-
0.3958	13800	4.4655	-	-	-	-	-
0.3987	13900	4.5717	-	-	-	-	-
0.4015	14000	4.134	-	-	-	-	-
0.4044	14100	4.2704	-	-	-	-	-
0.4073	14200	4.7712	-	-	-	-	-
0.4101	14300	4.3946	-	-	-	-	-
0.4130	14400	4.5848	-	-	-	-	-
0.4159	14500	4.4655	-	-	-	-	-
0.4187	14600	4.278	-	-	-	-	-
0.4216	14700	4.2877	-	-	-	-	-
0.4245	14800	3.9299	-	-	-	-	-
0.4274	14900	4.7078	-	-	-	-	-
0.4302	15000	4.8527	-	-	-	-	-
0.4331	15100	4.3476	-	-	-	-	-
0.4360	15200	4.2012	-	-	-	-	-
0.4388	15300	4.1766	-	-	-	-	-
0.4417	15400	3.9842	-	-	-	-	-
0.4446	15500	4.1244	-	-	-	-	-
0.4474	15600	4.7983	-	-	-	-	-
0.4503	15700	4.2341	-	-	-	-	-
0.4532	15800	4.9829	-	-	-	-	-
0.4560	15900	4.0221	-	-	-	-	-
0.4589	16000	4.1082	-	-	-	-	-
0.4618	16100	3.8922	-	-	-	-	-
0.4646	16200	4.5382	-	-	-	-	-
0.4675	16300	4.4428	-	-	-	-	-
0.4704	16400	3.9087	-	-	-	-	-
0.4732	16500	3.7465	-	-	-	-	-
0.4761	16600	4.149	-	-	-	-	-
0.4790	16700	4.5691	-	-	-	-	-
0.4818	16800	3.8776	-	-	-	-	-
0.4847	16900	3.7354	-	-	-	-	-
0.4876	17000	4.25	-	-	-	-	-
0.4904	17100	4.4119	-	-	-	-	-
0.4933	17200	4.2319	-	-	-	-	-
0.4962	17300	4.3736	-	-	-	-	-
0.4991	17400	4.5345	-	-	-	-	-
0.5019	17500	4.1824	-	-	-	-	-
0.5048	17600	4.0033	-	-	-	-	-
0.5077	17700	4.277	-	-	-	-	-
0.5105	17800	4.3553	-	-	-	-	-
0.5134	17900	3.9528	-	-	-	-	-
0.5163	18000	4.068	-	-	-	-	-
0.5191	18100	4.0464	-	-	-	-	-
0.5220	18200	4.1665	-	-	-	-	-
0.5249	18300	3.7445	-	-	-	-	-
0.5277	18400	4.2248	-	-	-	-	-
0.5306	18500	3.9295	-	-	-	-	-
0.5335	18600	3.546	-	-	-	-	-
0.5363	18700	3.7463	-	-	-	-	-
0.5392	18800	3.9798	-	-	-	-	-
0.5421	18900	4.4773	-	-	-	-	-
0.5449	19000	4.3534	-	-	-	-	-
0.5478	19100	4.2347	-	-	-	-	-
0.5507	19200	3.8113	-	-	-	-	-
0.5535	19300	4.4689	-	-	-	-	-
0.5564	19400	4.2188	-	-	-	-	-
0.5593	19500	4.1266	-	-	-	-	-
0.5622	19600	3.9222	-	-	-	-	-
0.5650	19700	4.38	-	-	-	-	-
0.5679	19800	4.4557	-	-	-	-	-
0.5708	19900	4.7566	-	-	-	-	-
0.5736	20000	3.8922	-	-	-	-	-
0.5765	20100	4.0263	-	-	-	-	-
0.5794	20200	3.9258	-	-	-	-	-
0.5822	20300	4.3767	-	-	-	-	-
0.5851	20400	4.1211	-	-	-	-	-
0.5880	20500	4.3083	-	-	-	-	-
0.5908	20600	4.4544	-	-	-	-	-
0.5937	20700	4.0118	-	-	-	-	-
0.5966	20800	3.9136	-	-	-	-	-
0.5994	20900	3.8614	-	-	-	-	-
0.6023	21000	3.8057	-	-	-	-	-
0.6052	21100	4.4934	-	-	-	-	-
0.6080	21200	3.9206	-	-	-	-	-
0.6109	21300	4.43	-	-	-	-	-
0.6138	21400	4.0576	-	-	-	-	-
0.6166	21500	3.9019	-	-	-	-	-
0.6195	21600	4.4216	-	-	-	-	-
0.6224	21700	4.0959	-	-	-	-	-
0.6253	21800	3.8756	-	-	-	-	-
0.6281	21900	4.7791	-	-	-	-	-
0.6310	22000	3.6284	-	-	-	-	-
0.6339	22100	4.5534	-	-	-	-	-
0.6367	22200	4.18	-	-	-	-	-
0.6396	22300	4.3002	-	-	-	-	-
0.6425	22400	3.7162	-	-	-	-	-
0.6453	22500	4.8495	-	-	-	-	-
0.6482	22600	4.2966	-	-	-	-	-
0.6511	22700	3.7718	-	-	-	-	-
0.6539	22800	4.2257	-	-	-	-	-
0.6568	22900	3.9821	-	-	-	-	-
0.6597	23000	4.0853	-	-	-	-	-
0.6625	23100	3.6124	-	-	-	-	-
0.6654	23200	3.732	-	-	-	-	-
0.6683	23300	4.3821	-	-	-	-	-
0.6711	23400	4.229	-	-	-	-	-
0.6740	23500	4.2589	-	-	-	-	-
0.6769	23600	4.4975	-	-	-	-	-
0.6797	23700	3.8062	-	-	-	-	-
0.6826	23800	3.6924	-	-	-	-	-
0.6855	23900	3.7736	-	-	-	-	-
0.6883	24000	3.7815	-	-	-	-	-
0.6912	24100	4.1192	-	-	-	-	-
0.6941	24200	4.2336	-	-	-	-	-
0.6970	24300	4.1145	-	-	-	-	-
0.6998	24400	4.0681	-	-	-	-	-
0.7027	24500	4.0492	-	-	-	-	-
0.7056	24600	3.7831	-	-	-	-	-
0.7084	24700	4.2445	-	-	-	-	-
0.7113	24800	3.9308	-	-	-	-	-
0.7142	24900	3.8705	-	-	-	-	-
0.7170	25000	3.6998	-	-	-	-	-
0.7199	25100	3.4736	-	-	-	-	-
0.7228	25200	3.9971	-	-	-	-	-
0.7256	25300	3.8292	-	-	-	-	-
0.7285	25400	3.8499	-	-	-	-	-
0.7314	25500	3.8732	-	-	-	-	-
0.7342	25600	3.9409	-	-	-	-	-
0.7371	25700	4.4416	-	-	-	-	-
0.7400	25800	3.663	-	-	-	-	-
0.7428	25900	3.9786	-	-	-	-	-
0.7457	26000	4.1781	-	-	-	-	-
0.7486	26100	3.692	-	-	-	-	-
0.7514	26200	3.2601	-	-	-	-	-
0.7543	26300	7.1759	-	-	-	-	-
0.7572	26400	7.0459	-	-	-	-	-
0.7601	26500	6.1797	-	-	-	-	-
0.7629	26600	6.2055	-	-	-	-	-
0.7658	26700	6.1403	-	-	-	-	-
0.7687	26800	5.703	-	-	-	-	-
0.7715	26900	6.1283	-	-	-	-	-
0.7744	27000	5.71	-	-	-	-	-
0.7773	27100	5.3105	-	-	-	-	-
0.7801	27200	5.4202	-	-	-	-	-
0.7830	27300	5.2964	-	-	-	-	-
0.7859	27400	5.4852	-	-	-	-	-
0.7887	27500	5.241	-	-	-	-	-
0.7916	27600	5.4322	-	-	-	-	-
0.7945	27700	5.6285	-	-	-	-	-
0.7973	27800	5.0215	-	-	-	-	-
0.8002	27900	5.2433	-	-	-	-	-
0.8031	28000	4.9617	-	-	-	-	-
0.8059	28100	4.9479	-	-	-	-	-
0.8088	28200	4.9077	-	-	-	-	-
0.8117	28300	4.853	-	-	-	-	-
0.8145	28400	4.6727	-	-	-	-	-
0.8174	28500	4.9987	-	-	-	-	-
0.8203	28600	4.8405	-	-	-	-	-
0.8232	28700	4.9627	-	-	-	-	-
0.8260	28800	4.5608	-	-	-	-	-
0.8289	28900	5.0802	-	-	-	-	-
0.8318	29000	4.9069	-	-	-	-	-
0.8346	29100	4.8605	-	-	-	-	-
0.8375	29200	4.6424	-	-	-	-	-
0.8404	29300	4.7813	-	-	-	-	-
0.8432	29400	4.5925	-	-	-	-	-
0.8461	29500	4.7081	-	-	-	-	-
0.8490	29600	4.4319	-	-	-	-	-
0.8518	29700	4.7291	-	-	-	-	-
0.8547	29800	4.749	-	-	-	-	-
0.8576	29900	4.6148	-	-	-	-	-
0.8604	30000	4.2549	-	-	-	-	-
0.8633	30100	4.3415	-	-	-	-	-
0.8662	30200	4.1999	-	-	-	-	-
0.8690	30300	4.4298	-	-	-	-	-
0.8719	30400	4.3612	-	-	-	-	-
0.8748	30500	4.4834	-	-	-	-	-
0.8776	30600	4.4774	-	-	-	-	-
0.8805	30700	4.2524	-	-	-	-	-
0.8834	30800	4.5562	-	-	-	-	-
0.8863	30900	4.5261	-	-	-	-	-
0.8891	31000	4.0262	-	-	-	-	-
0.8920	31100	4.1109	-	-	-	-	-
0.8949	31200	4.1955	-	-	-	-	-
0.8977	31300	4.3169	-	-	-	-	-
0.9006	31400	4.5862	-	-	-	-	-
0.9035	31500	4.5503	-	-	-	-	-
0.9063	31600	4.2587	-	-	-	-	-
0.9092	31700	4.0028	-	-	-	-	-
0.9121	31800	4.3575	-	-	-	-	-
0.9149	31900	4.1033	-	-	-	-	-
0.9178	32000	4.2877	-	-	-	-	-
0.9207	32100	3.9537	-	-	-	-	-
0.9235	32200	4.107	-	-	-	-	-
0.9264	32300	4.3288	-	-	-	-	-
0.9293	32400	4.102	-	-	-	-	-
0.9321	32500	4.1751	-	-	-	-	-
0.9350	32600	3.7919	-	-	-	-	-
0.9379	32700	4.0939	-	-	-	-	-
0.9407	32800	4.1822	-	-	-	-	-
0.9436	32900	3.959	-	-	-	-	-
0.9465	33000	3.9173	-	-	-	-	-
0.9493	33100	4.3087	-	-	-	-	-
0.9522	33200	4.1239	-	-	-	-	-
0.9551	33300	4.1012	-	-	-	-	-
0.9580	33400	3.9988	-	-	-	-	-
0.9608	33500	4.1478	-	-	-	-	-
0.9637	33600	4.1669	-	-	-	-	-
0.9666	33700	4.0398	-	-	-	-	-
0.9694	33800	3.9814	-	-	-	-	-
0.9723	33900	4.3764	-	-	-	-	-
0.9752	34000	4.2847	-	-	-	-	-
0.9780	34100	3.9461	-	-	-	-	-
0.9809	34200	4.3377	-	-	-	-	-
0.9838	34300	3.8114	-	-	-	-	-
0.9866	34400	4.0827	-	-	-	-	-
0.9895	34500	4.0014	-	-	-	-	-
0.9924	34600	4.3964	-	-	-	-	-
0.9952	34700	3.9103	-	-	-	-	-
0.9981	34800	4.0363	-	-	-	-	-
1.0	34866	-	0.6880	0.6922	0.6961	0.6803	0.6964

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}
}