metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated
base_model: microsoft/mpnet-base
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: 'Really? No kidding! '
    sentences:
      - yeah really no kidding
      - At the end of the fourth century was when baked goods flourished.
      - The campaigns seem to reach a new pool of contributors.
  - source_sentence: A sleeping man.
    sentences:
      - Two men are sleeping.
      - Someone is selling oranges
      - the family is young
  - source_sentence: a guy on a bike
    sentences:
      - A tall person on a bike
      - A man is on a frozen lake.
      - The women throw food at the kids
  - source_sentence: yeah really no kidding
    sentences:
      - oh uh-huh well no they wouldn't would they no
      - yeah i mean just when uh the they military paid for her education
      - The campaigns seem to reach a new pool of contributors.
  - source_sentence: He ran like an athlete.
    sentences:
      - ' Then he ran.'
      - yeah i mean just when uh the they military paid for her education
      - >-
        Similarly, OIM revised the electronic Grant Renewal Application to
        accommodate new information sought by LSC and to ensure greater ease for
        users.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 17.515467907816664
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.13
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: SentenceTransformer based on microsoft/mpnet-base
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.7331234146933103
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.7435439430716654
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.7389474504545281
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.7473580293303098
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.7356264396007131
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.7436137284782617
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7093073700072118
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7150453113301433
            name: Spearman Dot
          - type: pearson_max
            value: 0.7389474504545281
            name: Pearson Max
          - type: spearman_max
            value: 0.7473580293303098
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.6750510843835755
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6615639695746663
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.6718085205234632
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6589482932175834
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6693170762111229
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6578210069410166
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6490291380804283
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6335192601696299
            name: Spearman Dot
          - type: pearson_max
            value: 0.6750510843835755
            name: Pearson Max
          - type: spearman_max
            value: 0.6615639695746663
            name: Spearman Max

SentenceTransformer based on microsoft/mpnet-base

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the multi_nli, snli and stsb datasets. 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/mpnet-base
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 tokens
Training Datasets:
- multi_nli
- snli
- stsb
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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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/st-v3-test-mpnet-base-allnli-stsb")
# Run inference
sentences = [
    "He ran like an athlete.",
    " Then he ran.",
    "yeah i mean just when uh the they military paid for her education",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7331
spearman_cosine	0.7435
pearson_manhattan	0.7389
spearman_manhattan	0.7474
pearson_euclidean	0.7356
spearman_euclidean	0.7436
pearson_dot	0.7093
spearman_dot	0.715
pearson_max	0.7389
spearman_max	0.7474

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.6751
spearman_cosine	0.6616
pearson_manhattan	0.6718
spearman_manhattan	0.6589
pearson_euclidean	0.6693
spearman_euclidean	0.6578
pearson_dot	0.649
spearman_dot	0.6335
pearson_max	0.6751
spearman_max	0.6616

Training Details

Training Datasets

multi_nli

Dataset: multi_nli at da70db2
Size: 10,000 training samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 4 tokens
mean: 26.95 tokens
max: 189 tokens

min: 5 tokens
mean: 14.11 tokens
max: 49 tokens

0: ~34.30%
1: ~28.20%
2: ~37.50%

	premise	hypothesis	label
type	string	string	int
details	min: 4 tokens mean: 26.95 tokens max: 189 tokens	min: 5 tokens mean: 14.11 tokens max: 49 tokens	0: ~34.30% 1: ~28.20% 2: ~37.50%

Samples:

premise	hypothesis	label
`Conceptually cream skimming has two basic dimensions - product and geography.`	`Product and geography are what make cream skimming work.`	`1`
`you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him`	`You lose the things to the following level if the people recall.`	`0`
`One of our number will carry out your instructions minutely.`	`A member of my team will execute your orders with immense precision.`	`0`

Loss: sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss

snli

Dataset: snli at cdb5c3d
Size: 10,000 training samples
Columns: snli_premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
snli_premise hypothesis label
type string string int
details
min: 6 tokens
mean: 17.38 tokens
max: 52 tokens

min: 4 tokens
mean: 10.7 tokens
max: 31 tokens

0: ~33.40%
1: ~33.30%
2: ~33.30%

	snli_premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	0: ~33.40% 1: ~33.30% 2: ~33.30%

Samples:

snli_premise	hypothesis	label
`A person on a horse jumps over a broken down airplane.`	`A person is training his horse for a competition.`	`1`
`A person on a horse jumps over a broken down airplane.`	`A person is at a diner, ordering an omelette.`	`2`
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`0`

Loss: sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss

stsb

Dataset: stsb at 8913289
Size: 5,749 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 6 tokens
mean: 10.0 tokens
max: 28 tokens

min: 5 tokens
mean: 9.95 tokens
max: 25 tokens

min: 0.0
mean: 0.54
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 6 tokens mean: 10.0 tokens max: 28 tokens	min: 5 tokens mean: 9.95 tokens max: 25 tokens	min: 0.0 mean: 0.54 max: 1.0

Samples:

sentence1	sentence2	label
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss with these parameters:
```
{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}
```

Evaluation Datasets

multi_nli

Dataset: multi_nli at da70db2
Size: 100 evaluation samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 5 tokens
mean: 27.67 tokens
max: 138 tokens

min: 6 tokens
mean: 13.48 tokens
max: 27 tokens

0: ~35.00%
1: ~31.00%
2: ~34.00%

	premise	hypothesis	label
type	string	string	int
details	min: 5 tokens mean: 27.67 tokens max: 138 tokens	min: 6 tokens mean: 13.48 tokens max: 27 tokens	0: ~35.00% 1: ~31.00% 2: ~34.00%

Samples:

premise	hypothesis	label
`The new rights are nice enough`	`Everyone really likes the newest benefits`	`1`
`This site includes a list of all award winners and a searchable database of Government Executive articles.`	`The Government Executive articles housed on the website are not able to be searched.`	`2`
`uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him`	`I like him for the most part, but would still enjoy seeing someone beat him.`	`0`

Loss: sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss

snli

Dataset: snli at cdb5c3d
Size: 9,842 evaluation samples
Columns: snli_premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
snli_premise hypothesis label
type string string int
details
min: 6 tokens
mean: 18.44 tokens
max: 57 tokens

min: 5 tokens
mean: 10.57 tokens
max: 25 tokens

0: ~33.10%
1: ~33.30%
2: ~33.60%

	snli_premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 18.44 tokens max: 57 tokens	min: 5 tokens mean: 10.57 tokens max: 25 tokens	0: ~33.10% 1: ~33.30% 2: ~33.60%

Samples:

snli_premise	hypothesis	label
`Two women are embracing while holding to go packages.`	`The sisters are hugging goodbye while holding to go packages after just eating lunch.`	`1`
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`0`
`Two women are embracing while holding to go packages.`	`The men are fighting outside a deli.`	`2`

Loss: sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss

stsb

Dataset: stsb at 8913289
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 5 tokens
mean: 15.1 tokens
max: 45 tokens

min: 6 tokens
mean: 15.11 tokens
max: 53 tokens

min: 0.0
mean: 0.47
max: 1.0

	sentence1	sentence2	label
type	string	string	float
details	min: 5 tokens mean: 15.1 tokens max: 45 tokens	min: 6 tokens mean: 15.11 tokens max: 53 tokens	min: 0.0 mean: 0.47 max: 1.0

Samples:

sentence1	sentence2	label
`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: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss with these parameters:
```
{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}
```

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
seed: 33
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
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: 2e-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: 33
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
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}
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
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
round_robin_sampler: False

Training Logs

Epoch	Step	Training Loss	multi nli loss	snli loss	stsb loss	sts-dev spearman cosine
0.0493	10	0.9199	1.1019	1.1017	0.3016	0.6324
0.0985	20	1.0063	1.1000	1.0966	0.2635	0.6093
0.1478	30	1.002	1.0995	1.0908	0.1766	0.5328
0.1970	40	0.7946	1.0980	1.0913	0.0923	0.5991
0.2463	50	0.9891	1.0967	1.0781	0.0912	0.6457
0.2956	60	0.784	1.0938	1.0699	0.0934	0.6629
0.3448	70	0.6735	1.0940	1.0728	0.0640	0.7538
0.3941	80	0.7713	1.0893	1.0676	0.0612	0.7653
0.4433	90	0.9772	1.0870	1.0573	0.0636	0.7621
0.4926	100	0.8613	1.0862	1.0515	0.0632	0.7583
0.5419	110	0.7528	1.0814	1.0397	0.0617	0.7536
0.5911	120	0.6541	1.0854	1.0329	0.0657	0.7512
0.6404	130	1.051	1.0658	1.0211	0.0607	0.7340
0.6897	140	0.8516	1.0631	1.0171	0.0587	0.7467
0.7389	150	0.7484	1.0563	1.0122	0.0556	0.7537
0.7882	160	0.7368	1.0534	1.0100	0.0588	0.7526
0.8374	170	0.8373	1.0498	1.0030	0.0565	0.7491
0.8867	180	0.9311	1.0387	0.9981	0.0588	0.7302
0.9360	190	0.5445	1.0357	0.9967	0.0565	0.7382
0.9852	200	0.9154	1.0359	0.9964	0.0556	0.7435

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.018 kg of CO2
Hours Used: 0.13 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: 2.7.0.dev0
Transformers: 4.39.3
PyTorch: 2.1.0+cu121
Accelerate: 0.26.1
Datasets: 2.18.0
Tokenizers: 0.15.2

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}