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SentenceTransformer based on microsoft/mpnet-base

Name: mpnet-base-allnli
Availability: InStock

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
Similarity Function: Dot Product
Training Datasets:
- multi_nli
- snli
- stsb
Language: en
License: apache-2.0

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})
  (2): Normalize()
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/mpnet-base-allnli")
# Run inference
sentences = [
    "Rouen is the ancient center of Normandy's thriving textile industry, and the place of Joan of Arc's martyrdom ' a national symbol of resistance to tyranny.",
    'Joan of Arc sacrificed her life at Rouen, which became an enduring symbol of opposition to tyranny.',
    'The islands are part of France now instead of just colonies.',
]
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

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8344
spearman_cosine	0.8295
pearson_manhattan	0.8317
spearman_manhattan	0.8332
pearson_euclidean	0.8273
spearman_euclidean	0.8295
pearson_dot	0.8344
spearman_dot	0.8295
pearson_max	0.8344
spearman_max	0.8332

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7776
spearman_cosine	0.7643
pearson_manhattan	0.7788
spearman_manhattan	0.7659
pearson_euclidean	0.7763
spearman_euclidean	0.7643
pearson_dot	0.7776
spearman_dot	0.7643
pearson_max	0.7788
spearman_max	0.7659

Training Details

Training Datasets

multi_nli

Dataset: multi_nli at da70db2
Size: 392,702 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%

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: SoftmaxLoss

snli

Dataset: snli at cdb5c3d
Size: 549,367 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%

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: 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

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: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

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%

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: 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%

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: 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

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: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
seed: 33
bf16: True
load_best_model_at_end: True
push_to_hub: True
hub_model_id: tomaarsen/mpnet-base-allnli
hub_private_repo: True
multi_dataset_batch_sampler: round_robin

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: 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
restore_callback_states_from_checkpoint: 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: True
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: tomaarsen/mpnet-base-allnli
hub_strategy: every_save
hub_private_repo: True
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: round_robin

Training Logs

Epoch	Step	Training Loss	multi nli loss	snli loss	stsb loss	sts-dev_spearman_dot	sts-test_spearman_cosine
0.0370	10	0.8347	-	-	-	-	-
0.0741	20	0.8269	-	-	-	-	-
0.1111	30	0.7036	1.0978	1.0984	0.0830	0.6636	-
0.1481	40	0.7889	-	-	-	-	-
0.1852	50	0.7948	-	-	-	-	-
0.2222	60	0.688	1.0976	1.0961	0.0679	0.7124	-
0.2593	70	0.7911	-	-	-	-	-
0.2963	80	0.7847	-	-	-	-	-
0.3333	90	0.6801	1.0950	1.0942	0.0522	0.7810	-
0.3704	100	0.7837	-	-	-	-	-
0.4074	110	0.7803	-	-	-	-	-
0.4444	120	0.6756	1.0978	1.0929	0.0441	0.8157	-
0.4815	130	0.7829	-	-	-	-	-
0.5185	140	0.7789	-	-	-	-	-
0.5556	150	0.6756	1.0954	1.0911	0.0433	0.8215	-
0.5926	160	0.7802	-	-	-	-	-
0.6296	170	0.7751	-	-	-	-	-
0.6667	180	0.6679	1.0934	1.0885	0.0401	0.8235	-
0.7037	190	0.7755	-	-	-	-	-
0.7407	200	0.775	-	-	-	-	-
0.7778	210	0.6694	1.0919	1.0859	0.0377	0.8295	-
0.8148	220	0.7733	-	-	-	-	-
0.8519	230	0.772	-	-	-	-	-
0.8889	240	0.6656	1.0891	1.0838	0.0365	0.8292	-
0.9259	250	0.7726	-	-	-	-	-
0.9630	260	0.7731	-	-	-	-	-
1.0	270	0.6674	1.0888	1.0833	0.0372	0.8295	0.7643

The bold row denotes the saved checkpoint.

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.018 kWh
Carbon Emitted: 0.007 kg of CO2
Hours Used: 0.068 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: 3.1.0.dev0
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

bibtex

@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",
}

	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%