metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:100K<n<1M
  - loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-xsmall
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
  - cosine_accuracy
  - cosine_accuracy_threshold
  - cosine_f1
  - cosine_f1_threshold
  - cosine_precision
  - cosine_recall
  - cosine_ap
  - dot_accuracy
  - dot_accuracy_threshold
  - dot_f1
  - dot_f1_threshold
  - dot_precision
  - dot_recall
  - dot_ap
  - manhattan_accuracy
  - manhattan_accuracy_threshold
  - manhattan_f1
  - manhattan_f1_threshold
  - manhattan_precision
  - manhattan_recall
  - manhattan_ap
  - euclidean_accuracy
  - euclidean_accuracy_threshold
  - euclidean_f1
  - euclidean_f1_threshold
  - euclidean_precision
  - euclidean_recall
  - euclidean_ap
  - max_accuracy
  - max_accuracy_threshold
  - max_f1
  - max_f1_threshold
  - max_precision
  - max_recall
  - max_ap
widget:
  - source_sentence: No, monsieur.
    sentences:
      - Yes, sir.
      - Look, there's a legend here.
      - All models are subject to analysis.
  - source_sentence: She shrugged.
    sentences:
      - She acted like it didn't matter.
      - He felt bad for doubting her.
      - Jacques Teti movies are my favorite.
  - source_sentence: We can think.
    sentences:
      - We need to think.
      - A man is on his way to work.
      - Her favorite candy is chocolate.
  - source_sentence: He loved her.
    sentences:
      - She was loved by him.
      - The person is playing rugby.
      - All models are subject to analysis.
  - source_sentence: in each square
    sentences:
      - It is widespread.
      - A young girl flips an omelet.
      - He charged Jon with a knife.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on microsoft/deberta-v3-xsmall
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.7972304062599285
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8069984848350104
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8078500467589406
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8072286629818308
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8083747460970299
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.807329204776433
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7028547677818588
            name: Pearson Dot
          - type: spearman_dot
            value: 0.690944321229592
            name: Spearman Dot
          - type: pearson_max
            value: 0.8083747460970299
            name: Pearson Max
          - type: spearman_max
            value: 0.807329204776433
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.677155205095155
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7285403609275818
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7186860786908915
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.6111028790473938
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.6110485933503836
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8723528552650796
            name: Cosine Recall
          - type: cosine_ap
            value: 0.73917897685454
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.6382591553567367
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 228.40408325195312
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.706771220880316
            name: Dot F1
          - type: dot_f1_threshold
            value: 177.3942108154297
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5811370481927711
            name: Dot Precision
          - type: dot_recall
            value: 0.9017087775668176
            name: Dot Recall
          - type: dot_ap
            value: 0.6903597943138529
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.6635074683448328
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 174.62747192382812
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.7054413268204022
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 232.6788330078125
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5771911887721908
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.906966554695487
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.7282119371967055
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.6650997042990371
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 13.422540664672852
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.7067711563398544
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 17.634807586669922
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5755739210284665
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.9154374178472323
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.730311832588485
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.677155205095155
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 228.40408325195312
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.7186860786908915
            name: Max F1
          - type: max_f1_threshold
            value: 232.6788330078125
            name: Max F1 Threshold
          - type: max_precision
            value: 0.6110485933503836
            name: Max Precision
          - type: max_recall
            value: 0.9154374178472323
            name: Max Recall
          - type: max_ap
            value: 0.73917897685454
            name: Max Ap

SentenceTransformer based on microsoft/deberta-v3-xsmall

This is a sentence-transformers model finetuned from microsoft/deberta-v3-xsmall on the stanfordnlp/snli dataset. It maps sentences & paragraphs to a 384-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: microsoft/deberta-v3-xsmall
Maximum Sequence Length: 512 tokens
Output Dimensionality: 384 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- stanfordnlp/snli
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: DebertaV2Model 
  (1): Pooling({'word_embedding_dimension': 384, '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("bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03")
# Run inference
sentences = [
    'in each square',
    'It is widespread.',
    'A young girl flips an omelet.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7972
spearman_cosine	0.807
pearson_manhattan	0.8079
spearman_manhattan	0.8072
pearson_euclidean	0.8084
spearman_euclidean	0.8073
pearson_dot	0.7029
spearman_dot	0.6909
pearson_max	0.8084
spearman_max	0.8073

Binary Classification

Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6772
cosine_accuracy_threshold	0.7285
cosine_f1	0.7187
cosine_f1_threshold	0.6111
cosine_precision	0.611
cosine_recall	0.8724
cosine_ap	0.7392
dot_accuracy	0.6383
dot_accuracy_threshold	228.4041
dot_f1	0.7068
dot_f1_threshold	177.3942
dot_precision	0.5811
dot_recall	0.9017
dot_ap	0.6904
manhattan_accuracy	0.6635
manhattan_accuracy_threshold	174.6275
manhattan_f1	0.7054
manhattan_f1_threshold	232.6788
manhattan_precision	0.5772
manhattan_recall	0.907
manhattan_ap	0.7282
euclidean_accuracy	0.6651
euclidean_accuracy_threshold	13.4225
euclidean_f1	0.7068
euclidean_f1_threshold	17.6348
euclidean_precision	0.5756
euclidean_recall	0.9154
euclidean_ap	0.7303
max_accuracy	0.6772
max_accuracy_threshold	228.4041
max_f1	0.7187
max_f1_threshold	232.6788
max_precision	0.611
max_recall	0.9154
max_ap	0.7392

Training Details

Training Dataset

stanfordnlp/snli

Dataset: stanfordnlp/snli at cdb5c3d
Size: 314,315 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 5 tokens
mean: 16.62 tokens
max: 62 tokens

min: 4 tokens
mean: 9.46 tokens
max: 29 tokens

0: 100.00%

	sentence1	sentence2	label
type	string	string	int
details	min: 5 tokens mean: 16.62 tokens max: 62 tokens	min: 4 tokens mean: 9.46 tokens max: 29 tokens	0: 100.00%

Samples:

sentence1	sentence2	label
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`0`
`Children smiling and waving at camera`	`There are children present`	`0`
`A boy is jumping on skateboard in the middle of a red bridge.`	`The boy does a skateboarding trick.`	`0`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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: 14.77 tokens
max: 45 tokens

min: 6 tokens
mean: 14.74 tokens
max: 49 tokens

min: 0.0
mean: 0.47
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 5 tokens mean: 14.77 tokens max: 45 tokens	min: 6 tokens mean: 14.74 tokens max: 49 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: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 7.5e-05
num_train_epochs: 2
warmup_ratio: 0.25
save_safetensors: False
fp16: True
push_to_hub: True
hub_model_id: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n
hub_strategy: checkpoint

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 7.5e-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: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.25
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: 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: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: True
resume_from_checkpoint: None
hub_model_id: bobox/DeBERTaV3-xSmall-SentenceTransformer-0.03n
hub_strategy: checkpoint
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_eval_metrics: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	max_ap	sts-dev_spearman_cosine
None	0	-	3.7624	0.5721	0.4168
0.0501	246	3.3825	-	-	-
0.1002	492	1.8307	-	-	-
0.1500	737	-	1.0084	0.7024	-
0.1502	738	1.055	-	-	-
0.2003	984	0.7961	-	-	-
0.2504	1230	0.6859	-	-	-
0.3001	1474	-	0.7410	0.7191	-
0.3005	1476	0.5914	-	-	-
0.3506	1722	0.5324	-	-	-
0.4007	1968	0.5077	-	-	-
0.4501	2211	-	0.6152	0.7144	-
0.4507	2214	0.4647	-	-	-
0.5008	2460	0.4443	-	-	-
0.5509	2706	0.4169	-	-	-
0.6002	2948	-	0.5820	0.7207	-
0.6010	2952	0.3831	-	-	-
0.6511	3198	0.393	-	-	-
0.7011	3444	0.3654	-	-	-
0.7502	3685	-	0.5284	0.7264	-
0.7512	3690	0.344	-	-	-
0.8013	3936	0.3336	-	-	-
0.8514	4182	0.3382	-	-	-
0.9002	4422	-	0.4911	0.7294	-
0.9015	4428	0.3182	-	-	-
0.9515	4674	0.3213	-	-	-
1.0016	4920	0.3032	-	-	-
1.0503	5159	-	0.4777	0.7325	-
1.0517	5166	0.2526	-	-	-
1.1018	5412	0.2652	-	-	-
1.1519	5658	0.2538	-	-	-
1.2003	5896	-	0.4569	0.7331	-
1.2020	5904	0.2454	-	-	-
1.2520	6150	0.2528	-	-	-
1.3021	6396	0.2448	-	-	-
1.3504	6633	-	0.4334	0.7370	-
1.3522	6642	0.2282	-	-	-
1.4023	6888	0.2295	-	-	-
1.4524	7134	0.2313	-	-	-
1.5004	7370	-	0.4237	0.7342	-
1.5024	7380	0.2218	-	-	-
1.5525	7626	0.2246	-	-	-
1.6026	7872	0.218	-	-	-
1.6504	8107	-	0.4102	0.7388	-
1.6527	8118	0.2095	-	-	-
1.7028	8364	0.2114	-	-	-
1.7529	8610	0.2063	-	-	-
1.8005	8844	-	0.4075	0.7370	-
1.8029	8856	0.1968	-	-	-
1.8530	9102	0.2061	-	-	-
1.9031	9348	0.2089	-	-	-
1.9505	9581	-	0.3978	0.7395	-
1.9532	9594	0.2005	-	-	-
2.0	9824	-	0.3963	0.7392	-
None	0	-	1.5506	-	0.8070

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.2
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",
}

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}
}