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 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.

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:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        256,
        128,
        64,
        32,
        16
    ],
    "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": [
        256,
        128,
        64,
        32,
        16
    ],
    "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-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

@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-reduced

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-reduced

Evaluation results