SentenceTransformer based on distilbert/distilroberta-base

This is a sentence-transformers model finetuned from distilbert/distilroberta-base on the sentence-transformers/all-nli dataset. 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: distilbert/distilroberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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})
)

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

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/distilroberta-base-nli-matryoshka-v3")
# Run inference
sentences = [
    'A man shoots a man.',
    'A man is shooting off guns.',
    'A man is erasing a chalk board.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8481
spearman_cosine	0.8519
pearson_manhattan	0.8393
spearman_manhattan	0.8385
pearson_euclidean	0.841
spearman_euclidean	0.8402
pearson_dot	0.7784
spearman_dot	0.778
pearson_max	0.8481
spearman_max	0.8519

Semantic Similarity

Dataset: sts-dev-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8481
spearman_cosine	0.8524
pearson_manhattan	0.8386
spearman_manhattan	0.8377
pearson_euclidean	0.8402
spearman_euclidean	0.8395
pearson_dot	0.7712
spearman_dot	0.7713
pearson_max	0.8481
spearman_max	0.8524

Semantic Similarity

Dataset: sts-dev-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8421
spearman_cosine	0.8488
pearson_manhattan	0.8313
spearman_manhattan	0.8316
pearson_euclidean	0.8333
spearman_euclidean	0.8335
pearson_dot	0.7446
spearman_dot	0.745
pearson_max	0.8421
spearman_max	0.8488

Semantic Similarity

Dataset: sts-dev-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8347
spearman_cosine	0.8445
pearson_manhattan	0.8241
spearman_manhattan	0.8248
pearson_euclidean	0.8254
spearman_euclidean	0.8262
pearson_dot	0.7084
spearman_dot	0.7093
pearson_max	0.8347
spearman_max	0.8445

Semantic Similarity

Dataset: sts-dev-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8201
spearman_cosine	0.8352
pearson_manhattan	0.8032
spearman_manhattan	0.8047
pearson_euclidean	0.806
spearman_euclidean	0.8072
pearson_dot	0.636
spearman_dot	0.6389
pearson_max	0.8201
spearman_max	0.8352

Semantic Similarity

Dataset: sts-test-768
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8262
spearman_cosine	0.8298
pearson_manhattan	0.8104
spearman_manhattan	0.8033
pearson_euclidean	0.8114
spearman_euclidean	0.8048
pearson_dot	0.7351
spearman_dot	0.7223
pearson_max	0.8262
spearman_max	0.8298

Semantic Similarity

Dataset: sts-test-512
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8265
spearman_cosine	0.8303
pearson_manhattan	0.8092
spearman_manhattan	0.8022
pearson_euclidean	0.81
spearman_euclidean	0.8034
pearson_dot	0.7239
spearman_dot	0.7141
pearson_max	0.8265
spearman_max	0.8303

Semantic Similarity

Dataset: sts-test-256
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8248
spearman_cosine	0.8305
pearson_manhattan	0.8012
spearman_manhattan	0.7951
pearson_euclidean	0.8028
spearman_euclidean	0.7974
pearson_dot	0.7011
spearman_dot	0.6946
pearson_max	0.8248
spearman_max	0.8305

Semantic Similarity

Dataset: sts-test-128
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8206
spearman_cosine	0.8284
pearson_manhattan	0.7932
spearman_manhattan	0.7878
pearson_euclidean	0.7947
spearman_euclidean	0.7891
pearson_dot	0.6618
spearman_dot	0.6586
pearson_max	0.8206
spearman_max	0.8284

Semantic Similarity

Dataset: sts-test-64
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8119
spearman_cosine	0.8241
pearson_manhattan	0.7761
spearman_manhattan	0.7738
pearson_euclidean	0.7777
spearman_euclidean	0.7746
pearson_dot	0.5934
spearman_dot	0.5884
pearson_max	0.8119
spearman_max	0.8241

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:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

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

	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:

{
    "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

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-256_spearman_cosine	sts-dev-512_spearman_cosine	sts-dev-64_spearman_cosine	sts-dev-768_spearman_cosine	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
0.0229	100	19.9245	11.3900	0.7772	0.7998	0.8049	0.7902	0.7919	-	-	-	-	-
0.0459	200	10.6055	11.1510	0.7809	0.7996	0.8055	0.7954	0.7954	-	-	-	-	-
0.0688	300	9.6389	11.1229	0.7836	0.8029	0.8114	0.7923	0.8083	-	-	-	-	-
0.0918	400	8.6917	11.0299	0.7976	0.8117	0.8142	0.8002	0.8087	-	-	-	-	-
0.1147	500	8.3064	11.3586	0.7895	0.8058	0.8120	0.7978	0.8065	-	-	-	-	-
0.1376	600	7.8026	11.5047	0.7876	0.8015	0.8065	0.7934	0.8016	-	-	-	-	-
0.1606	700	7.9978	11.5823	0.7944	0.8067	0.8072	0.7994	0.8045	-	-	-	-	-
0.1835	800	6.9249	11.5862	0.7945	0.8054	0.8085	0.8012	0.8033	-	-	-	-	-
0.2065	900	7.1059	11.2365	0.7895	0.8035	0.8072	0.7956	0.8031	-	-	-	-	-
0.2294	1000	6.5483	11.3770	0.7853	0.7994	0.8039	0.7894	0.8024	-	-	-	-	-
0.2524	1100	6.6684	11.5038	0.7968	0.8087	0.8115	0.8002	0.8065	-	-	-	-	-
0.2753	1200	6.4661	11.4057	0.7980	0.8082	0.8103	0.8057	0.8070	-	-	-	-	-
0.2982	1300	6.501	11.2521	0.7974	0.8100	0.8111	0.8025	0.8079	-	-	-	-	-
0.3212	1400	6.0769	11.1458	0.7971	0.8103	0.8124	0.7982	0.8082	-	-	-	-	-
0.3441	1500	6.1919	11.3180	0.8039	0.8129	0.8144	0.8094	0.8098	-	-	-	-	-
0.3671	1600	5.8213	11.6196	0.7924	0.8072	0.8090	0.8003	0.8012	-	-	-	-	-
0.3900	1700	5.534	11.0700	0.7979	0.8104	0.8132	0.8028	0.8101	-	-	-	-	-
0.4129	1800	5.7536	11.0916	0.7934	0.8087	0.8149	0.8008	0.8085	-	-	-	-	-
0.4359	1900	5.3778	11.2658	0.7942	0.8084	0.8104	0.7980	0.8049	-	-	-	-	-
0.4588	2000	5.4925	11.4851	0.7932	0.8062	0.8086	0.7932	0.8057	-	-	-	-	-
0.4818	2100	5.3125	11.4833	0.7987	0.8119	0.8154	0.8012	0.8124	-	-	-	-	-
0.5047	2200	5.1914	11.2848	0.7784	0.7971	0.8037	0.7911	0.8004	-	-	-	-	-
0.5276	2300	5.2921	11.5364	0.7698	0.7910	0.7974	0.7839	0.7900	-	-	-	-	-
0.5506	2400	5.288	11.3944	0.7873	0.8011	0.8051	0.7877	0.8003	-	-	-	-	-
0.5735	2500	5.3697	11.4532	0.7949	0.8077	0.8111	0.7955	0.8069	-	-	-	-	-
0.5965	2600	5.1521	11.2788	0.7973	0.8095	0.8130	0.7940	0.8088	-	-	-	-	-
0.6194	2700	5.2316	11.2472	0.7948	0.8077	0.8102	0.7939	0.8053	-	-	-	-	-
0.6423	2800	5.2599	11.4171	0.7882	0.8029	0.8065	0.7888	0.8019	-	-	-	-	-
0.6653	2900	5.4052	11.4026	0.7871	0.8005	0.8021	0.7833	0.7985	-	-	-	-	-
0.6882	3000	5.3474	11.2084	0.7895	0.8047	0.8079	0.7928	0.8050	-	-	-	-	-
0.7112	3100	5.0336	11.3999	0.8023	0.8150	0.8182	0.8024	0.8168	-	-	-	-	-
0.7341	3200	5.2496	11.2307	0.8015	0.8137	0.8167	0.8000	0.8140	-	-	-	-	-
0.7571	3300	3.8712	10.9468	0.8396	0.8440	0.8471	0.8284	0.8479	-	-	-	-	-
0.7800	3400	2.7068	10.9292	0.8414	0.8453	0.8489	0.8305	0.8497	-	-	-	-	-
0.8029	3500	2.3418	10.8626	0.8427	0.8467	0.8504	0.8322	0.8504	-	-	-	-	-
0.8259	3600	2.2419	10.9065	0.8421	0.8467	0.8504	0.8320	0.8502	-	-	-	-	-
0.8488	3700	2.125	10.9517	0.8424	0.8472	0.8509	0.8324	0.8510	-	-	-	-	-
0.8718	3800	1.9942	11.0142	0.8438	0.8482	0.8519	0.8337	0.8517	-	-	-	-	-
0.8947	3900	2.031	10.9662	0.8433	0.8480	0.8519	0.8340	0.8515	-	-	-	-	-
0.9176	4000	1.9734	11.0054	0.8452	0.8495	0.8531	0.8354	0.8528	-	-	-	-	-
0.9406	4100	1.9468	11.0183	0.8447	0.8490	0.8526	0.8348	0.8522	-	-	-	-	-
0.9635	4200	1.9008	11.0154	0.8445	0.8485	0.8521	0.8352	0.8517	-	-	-	-	-
0.9865	4300	1.8511	10.9966	0.8445	0.8488	0.8524	0.8352	0.8519	-	-	-	-	-
1.0	4359	-	-	-	-	-	-	-	0.8284	0.8305	0.8303	0.8241	0.8298

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.346 kWh
Carbon Emitted: 0.134 kg of CO2
Hours Used: 1.296 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

@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}
}

tomaarsen
/

distilroberta-base-nli-matryoshka-v3

SentenceTransformer based on distilbert/distilroberta-base

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Semantic Similarity

Training Details

Training Dataset

sentence-transformers/all-nli

Evaluation Dataset

sentence-transformers/stsb

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Environmental Impact

Training Hardware

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for tomaarsen/distilroberta-base-nli-matryoshka-v3

Evaluation results