This is a sentence-transformers model finetuned from distilbert/distilroberta-base on the sentence-transformers/all-nli dataset. It maps sentences & paragraphs to a 256-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: distilbert/distilroberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 256 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/all-nli
Language: en

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: RobertaModel 
  (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})
  (reduced_dim): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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("tomaarsen/distilroberta-base-nli-matryoshka-reduced")
# Run inference
sentences = [
    'A boy is vacuuming.',
    'A little boy is vacuuming the floor.',
    'A woman is applying eye shadow.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 256]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.833
spearman_cosine	0.845
pearson_manhattan	0.8284
spearman_manhattan	0.8314
pearson_euclidean	0.8291
spearman_euclidean	0.8319
pearson_dot	0.7274
spearman_dot	0.7358
pearson_max	0.833
spearman_max	0.845

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8266
spearman_cosine	0.8416
pearson_manhattan	0.825
spearman_manhattan	0.8277
pearson_euclidean	0.8256
spearman_euclidean	0.8285
pearson_dot	0.712
spearman_dot	0.7163
pearson_max	0.8266
spearman_max	0.8416

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8171
spearman_cosine	0.8356
pearson_manhattan	0.8176
spearman_manhattan	0.8213
pearson_euclidean	0.8175
spearman_euclidean	0.8216
pearson_dot	0.6852
spearman_dot	0.6861
pearson_max	0.8176
spearman_max	0.8356

Semantic Similarity

Dataset: sts-dev-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7964
spearman_cosine	0.8244
pearson_manhattan	0.7983
spearman_manhattan	0.8049
pearson_euclidean	0.8003
spearman_euclidean	0.807
pearson_dot	0.6312
spearman_dot	0.6277
pearson_max	0.8003
spearman_max	0.8244

Semantic Similarity

Dataset: sts-dev-16
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7401
spearman_cosine	0.7872
pearson_manhattan	0.761
spearman_manhattan	0.7761
pearson_euclidean	0.7645
spearman_euclidean	0.7794
pearson_dot	0.5202
spearman_dot	0.5115
pearson_max	0.7645
spearman_max	0.7872

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8124
spearman_cosine	0.8211
pearson_manhattan	0.7835
spearman_manhattan	0.7822
pearson_euclidean	0.7852
spearman_euclidean	0.784
pearson_dot	0.5917
spearman_dot	0.5785
pearson_max	0.8124
spearman_max	0.8211

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8079
spearman_cosine	0.819
pearson_manhattan	0.7795
spearman_manhattan	0.7786
pearson_euclidean	0.7813
spearman_euclidean	0.7813
pearson_dot	0.5714
spearman_dot	0.5602
pearson_max	0.8079
spearman_max	0.819

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7988
spearman_cosine	0.8129
pearson_manhattan	0.7728
spearman_manhattan	0.7728
pearson_euclidean	0.7735
spearman_euclidean	0.7751
pearson_dot	0.5397
spearman_dot	0.5279
pearson_max	0.7988
spearman_max	0.8129

Semantic Similarity

Dataset: sts-test-32
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.772
spearman_cosine	0.7936
pearson_manhattan	0.7561
spearman_manhattan	0.7597
pearson_euclidean	0.7581
spearman_euclidean	0.7628
pearson_dot	0.489
spearman_dot	0.4779
pearson_max	0.772
spearman_max	0.7936

Semantic Similarity

Dataset: sts-test-16
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7138
spearman_cosine	0.7486
pearson_manhattan	0.7254
spearman_manhattan	0.7339
pearson_euclidean	0.7274
spearman_euclidean	0.7382
pearson_dot	0.3856
spearman_dot	0.3749
pearson_max	0.7274
spearman_max	0.7486

Training Details

Training Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/all-nli at 65dd388
Size: 557,850 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: 10.38 tokens
max: 45 tokens
min: 6 tokens
mean: 12.8 tokens
max: 39 tokens
min: 6 tokens
mean: 13.4 tokens
max: 50 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.`
`Children smiling and waving at camera`	`There are children present`	`The kids are frowning`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`The boy skates down the sidewalk.`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 256, 5 128, 6 64, 7 32, 8 16 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

Evaluation Dataset

sentence-transformers/stsb

Dataset: sentence-transformers/stsb at ab7a5ac
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 15.0 tokens
max: 44 tokens
min: 6 tokens
mean: 14.99 tokens
max: 61 tokens
min: 0.0
mean: 0.47
max: 1.0

Samples:

sentence1	sentence2	score
`A man with a hard hat is dancing.`	`A man wearing a hard hat is dancing.`	`1.0`
`A young child is riding a horse.`	`A child is riding a horse.`	`0.95`
`A man is feeding a mouse to a snake.`	`The man is feeding a mouse to the snake.`	`1.0`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 256, 5 128, 6 64, 7 32, 8 16 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

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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
eval_strategy: steps
prediction_loss_only: False
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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, 'non_blocking': False, '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: None
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

Epoch	Step	Training Loss	loss	sts-dev-128_spearman_cosine	sts-dev-16_spearman_cosine	sts-dev-256_spearman_cosine	sts-dev-32_spearman_cosine	sts-dev-64_spearman_cosine	sts-test-128_spearman_cosine	sts-test-16_spearman_cosine	sts-test-256_spearman_cosine	sts-test-32_spearman_cosine	sts-test-64_spearman_cosine
0.0229	100	21.0363	14.2448	0.7856	0.7417	0.7873	0.7751	0.7846	-	-	-	-	-
0.0459	200	11.1093	13.4736	0.7877	0.7298	0.7861	0.7687	0.7798	-	-	-	-	-
0.0688	300	10.1847	13.7191	0.7877	0.7284	0.7898	0.7617	0.7755	-	-	-	-	-
0.0918	400	9.356	13.2955	0.7906	0.7385	0.7914	0.7715	0.7799	-	-	-	-	-
0.1147	500	8.9318	12.8099	0.7889	0.7346	0.7910	0.7690	0.7801	-	-	-	-	-
0.1376	600	8.5293	13.7384	0.7814	0.7362	0.7866	0.7656	0.7736	-	-	-	-	-
0.1606	700	8.7589	13.4466	0.7899	0.7467	0.7945	0.7770	0.7847	-	-	-	-	-
0.1835	800	7.7941	13.6734	0.7960	0.7526	0.7986	0.7800	0.7894	-	-	-	-	-
0.2065	900	7.9183	12.9082	0.7885	0.7470	0.7966	0.7705	0.7803	-	-	-	-	-
0.2294	1000	7.3669	13.2827	0.7751	0.7181	0.7822	0.7557	0.7675	-	-	-	-	-
0.2524	1100	7.6205	13.0227	0.7875	0.7373	0.7914	0.7730	0.7828	-	-	-	-	-
0.2753	1200	7.4308	13.4980	0.7844	0.7373	0.7890	0.7709	0.7755	-	-	-	-	-
0.2982	1300	7.3625	12.8380	0.7984	0.7520	0.8032	0.7824	0.7915	-	-	-	-	-
0.3212	1400	6.9421	12.7016	0.7912	0.7358	0.7960	0.7749	0.7850	-	-	-	-	-
0.3441	1500	7.0635	13.2198	0.8018	0.7578	0.8070	0.7861	0.7961	-	-	-	-	-
0.3671	1600	6.6682	13.3225	0.7906	0.7522	0.7944	0.7763	0.7849	-	-	-	-	-
0.3900	1700	6.42	12.7381	0.7984	0.7449	0.8021	0.7806	0.7911	-	-	-	-	-
0.4129	1800	6.659	13.0247	0.7947	0.7461	0.8002	0.7808	0.7876	-	-	-	-	-
0.4359	1900	6.1664	12.6814	0.7893	0.7312	0.7959	0.7700	0.7807	-	-	-	-	-
0.4588	2000	6.392	13.0238	0.7935	0.7354	0.7987	0.7758	0.7860	-	-	-	-	-
0.4818	2100	6.177	12.8833	0.7891	0.7428	0.7924	0.7723	0.7801	-	-	-	-	-
0.5047	2200	6.0411	12.5269	0.7836	0.7400	0.7875	0.7664	0.7765	-	-	-	-	-
0.5276	2300	6.1506	13.4349	0.7741	0.7350	0.7803	0.7556	0.7634	-	-	-	-	-
0.5506	2400	6.109	12.6996	0.7808	0.7326	0.7860	0.7663	0.7735	-	-	-	-	-
0.5735	2500	6.2849	13.2831	0.7874	0.7365	0.7932	0.7727	0.7794	-	-	-	-	-
0.5965	2600	6.0658	12.9425	0.7988	0.7481	0.8042	0.7818	0.7889	-	-	-	-	-
0.6194	2700	6.0646	13.0144	0.7965	0.7509	0.8010	0.7800	0.7875	-	-	-	-	-
0.6423	2800	6.0795	12.7602	0.7912	0.7472	0.7937	0.7778	0.7818	-	-	-	-	-
0.6653	2900	6.2407	13.2381	0.7829	0.7381	0.7873	0.7664	0.7765	-	-	-	-	-
0.6882	3000	6.1872	12.9064	0.7942	0.7516	0.7965	0.7793	0.7857	-	-	-	-	-
0.7112	3100	5.8987	12.9323	0.8065	0.7585	0.8087	0.7909	0.7989	-	-	-	-	-
0.7341	3200	5.996	13.1017	0.7971	0.7566	0.8005	0.7811	0.7889	-	-	-	-	-
0.7571	3300	5.3748	12.7601	0.8398	0.7881	0.8441	0.8232	0.8337	-	-	-	-	-
0.7800	3400	4.0798	12.7221	0.8400	0.7908	0.8440	0.8255	0.8342	-	-	-	-	-
0.8029	3500	3.6024	12.5445	0.8408	0.7892	0.8447	0.8247	0.8347	-	-	-	-	-
0.8259	3600	3.4619	12.6025	0.8405	0.7883	0.8442	0.8255	0.8347	-	-	-	-	-
0.8488	3700	3.2288	12.6636	0.8388	0.7872	0.8433	0.8226	0.8330	-	-	-	-	-
0.8718	3800	3.0543	12.6475	0.8386	0.7834	0.8427	0.8229	0.8330	-	-	-	-	-
0.8947	3900	3.0368	12.5390	0.8407	0.7845	0.8444	0.8227	0.8346	-	-	-	-	-
0.9176	4000	2.9591	12.5709	0.8419	0.7864	0.8456	0.8245	0.8359	-	-	-	-	-
0.9406	4100	2.944	12.6029	0.8415	0.7868	0.8452	0.8245	0.8359	-	-	-	-	-
0.9635	4200	2.9032	12.5514	0.8423	0.7888	0.8455	0.8254	0.8363	-	-	-	-	-
0.9865	4300	2.838	12.6054	0.8416	0.7872	0.8450	0.8244	0.8356	-	-	-	-	-
1.0	4359	-	-	-	-	-	-	-	0.8190	0.7486	0.8211	0.7936	0.8129

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.244 kWh
Carbon Emitted: 0.095 kg of CO2
Hours Used: 0.923 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 3.0.0.dev0
Transformers: 4.41.0.dev0
PyTorch: 2.3.0+cu121
Accelerate: 0.26.1
Datasets: 2.18.0
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: 10.38 tokens max: 45 tokens	min: 6 tokens mean: 12.8 tokens max: 39 tokens	min: 6 tokens mean: 13.4 tokens max: 50 tokens