metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:314315
  - loss:AdaptiveLayerLoss
  - loss:MultipleNegativesRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
  - stanfordnlp/snli
  - sentence-transformers/stsb
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: The pitcher is pitching the ball in a game of baseball.
    sentences:
      - the lady digs into the ground
      - A group of people are sitting at tables.
      - The pitcher throws the ball.
  - source_sentence: People are conversing at a dining table under a canopy.
    sentences:
      - A canine is using his legs.
      - The people are creative.
      - People at a party are seated for dinner on the lawn.
  - source_sentence: Two teenage girls conversing next to lockers.
    sentences:
      - Girls talking about their problems next to lockers.
      - A group of people play in the ocean.
      - The man is testing the bike.
  - source_sentence: >-
      A young boy in a hoodie climbs a red slide sitting on a red and green
      checkered background.
    sentences:
      - People are buying food from a street vendor.
      - A boy is playing.
      - A dog outside digging.
  - source_sentence: >-
      A professional swimmer spits water out after surfacing while grabbing the
      hand of someone helping him back to land.
    sentences:
      - A group of people wait in a line.
      - A tourist has his picture taken on Easter Island.
      - The swimmer almost drowned after being sucked under a fast current.
pipeline_tag: sentence-similarity
model-index:
  - name: SentenceTransformer based on microsoft/deberta-v3-small
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.7641416788909702
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.763668633314844
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7808845626705342
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.783960481366303
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7714319160162553
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7750607015673249
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.587659176024498
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6010467058509925
            name: Spearman Dot
          - type: pearson_max
            value: 0.7808845626705342
            name: Pearson Max
          - type: spearman_max
            value: 0.783960481366303
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.6773826673743271
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.5830236673355103
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7209834880077135
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.5085207223892212
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.6137273007079102
            name: Cosine Precision
          - type: cosine_recall
            value: 0.873667299547247
            name: Cosine Recall
          - type: cosine_ap
            value: 0.7219177301725319
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.6389415421942528
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 45.1016845703125
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.7090406632451638
            name: Dot F1
          - type: dot_f1_threshold
            value: 32.459449768066406
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5775450202131569
            name: Dot Precision
          - type: dot_recall
            value: 0.9180663064115671
            name: Dot Recall
          - type: dot_ap
            value: 0.6795197111227502
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.6625217984684206
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 158.29489135742188
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.7041269465332466
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 178.5047607421875
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5921131248755228
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8684095224185775
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.7054112942825768
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.6578967321252559
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 7.951424598693848
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.7015471831817645
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 9.045232772827148
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5888767720828789
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8675332262304659
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.7024193897121154
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.6773826673743271
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 158.29489135742188
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.7209834880077135
            name: Max F1
          - type: max_f1_threshold
            value: 178.5047607421875
            name: Max F1 Threshold
          - type: max_precision
            value: 0.6137273007079102
            name: Max Precision
          - type: max_recall
            value: 0.9180663064115671
            name: Max Recall
          - type: max_ap
            value: 0.7219177301725319
            name: Max Ap

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the stanfordnlp/snli 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: microsoft/deberta-v3-small
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 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("bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAll")
# Run inference
sentences = [
    'A professional swimmer spits water out after surfacing while grabbing the hand of someone helping him back to land.',
    'The swimmer almost drowned after being sucked under a fast current.',
    'A group of people wait in a line.',
]
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

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7641
spearman_cosine	0.7637
pearson_manhattan	0.7809
spearman_manhattan	0.784
pearson_euclidean	0.7714
spearman_euclidean	0.7751
pearson_dot	0.5877
spearman_dot	0.601
pearson_max	0.7809
spearman_max	0.784

Binary Classification

Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6774
cosine_accuracy_threshold	0.583
cosine_f1	0.721
cosine_f1_threshold	0.5085
cosine_precision	0.6137
cosine_recall	0.8737
cosine_ap	0.7219
dot_accuracy	0.6389
dot_accuracy_threshold	45.1017
dot_f1	0.709
dot_f1_threshold	32.4594
dot_precision	0.5775
dot_recall	0.9181
dot_ap	0.6795
manhattan_accuracy	0.6625
manhattan_accuracy_threshold	158.2949
manhattan_f1	0.7041
manhattan_f1_threshold	178.5048
manhattan_precision	0.5921
manhattan_recall	0.8684
manhattan_ap	0.7054
euclidean_accuracy	0.6579
euclidean_accuracy_threshold	7.9514
euclidean_f1	0.7015
euclidean_f1_threshold	9.0452
euclidean_precision	0.5889
euclidean_recall	0.8675
euclidean_ap	0.7024
max_accuracy	0.6774
max_accuracy_threshold	158.2949
max_f1	0.721
max_f1_threshold	178.5048
max_precision	0.6137
max_recall	0.9181
max_ap	0.7219

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: AdaptiveLayerLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "n_layers_per_step": -1,
    "last_layer_weight": 1,
    "prior_layers_weight": 1,
    "kl_div_weight": 1.2,
    "kl_temperature": 1.2
}

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: AdaptiveLayerLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "n_layers_per_step": -1,
    "last_layer_weight": 1,
    "prior_layers_weight": 1,
    "kl_div_weight": 1.2,
    "kl_temperature": 1.2
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
learning_rate: 5e-06
weight_decay: 1e-07
warmup_ratio: 0.33
save_safetensors: False
fp16: True
push_to_hub: True
hub_model_id: bobox/DeBERTaV3-small-SentenceTransformer-AdaptiveLayerAlln
hub_strategy: checkpoint
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
learning_rate: 5e-06
weight_decay: 1e-07
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.33
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-small-SentenceTransformer-AdaptiveLayerAlln
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	max_ap	spearman_cosine
None	0	-	5.4171	-	0.4276
0.1501	1474	4.9879	-	-	-
0.3000	2947	-	2.6463	0.6840	-
0.3001	2948	3.2669	-	-	-
0.4502	4422	2.6363	-	-	-
0.6000	5894	-	1.8436	0.7014	-
0.6002	5896	2.192	-	-	-
0.7503	7370	0.8208	-	-	-
0.9000	8841	-	1.5551	0.7065	-
0.9003	8844	0.6161	-	-	-
1.0504	10318	1.0301	-	-	-
1.2000	11788	-	1.1883	0.7131	-
1.2004	11792	1.8209	-	-	-
1.3505	13266	1.6887	-	-	-
1.5001	14735	-	1.1067	0.7119	-
1.5006	14740	1.6114	-	-	-
1.6506	16214	1.0691	-	-	-
1.8001	17682	-	1.0872	0.7183	-
1.8007	17688	0.3982	-	-	-
1.9507	19162	0.3659	-	-	-
2.1001	20629	-	0.9642	0.7221	-
2.1008	20636	1.1702	-	-	-
2.2508	22110	1.4984	-	-	-
2.4001	23576	-	0.9437	0.7200	-
2.4009	23584	1.4609	-	-	-
2.5510	25058	1.4477	-	-	-
2.7001	26523	-	0.9428	0.7216	-
2.7010	26532	0.5802	-	-	-
2.8511	28006	0.3297	-	-	-
3.0	29469	-	0.9532	0.7219	-
None	0	-	2.4079	0.7219	0.7637

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2
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",
}

AdaptiveLayerLoss

@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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}
}