This is a sentence-transformers model finetuned from microsoft/mpnet-base on the all-nli 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/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- all-nli
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': 512, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("harcor/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
    'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
    'A worker is looking out of a manhole.',
    'The workers are both inside the manhole.',
]
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

Triplet

Datasets: all-nli-dev and all-nli-test
Evaluated with TripletEvaluator

Metric	all-nli-dev	all-nli-test
cosine_accuracy	0.9116	0.9248

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 100,000 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: 10.46 tokens
max: 46 tokens
min: 6 tokens
mean: 12.81 tokens
max: 40 tokens
min: 5 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: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

all-nli

Dataset: all-nli at d482672
Size: 6,584 evaluation samples
Columns: anchor, positive, and negative
Approximate statistics based on the first 1000 samples:
anchor positive negative
type string string string
details
min: 6 tokens
mean: 17.95 tokens
max: 63 tokens
min: 4 tokens
mean: 9.78 tokens
max: 29 tokens
min: 5 tokens
mean: 10.35 tokens
max: 29 tokens

Samples:

anchor	positive	negative
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`The men are fighting outside a deli.`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`Two kids in numbered jerseys wash their hands.`	`Two kids in jackets walk to school.`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`A man selling donuts to a customer.`	`A woman drinks her coffee in a small cafe.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
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: 16
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
torch_empty_cache_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
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: 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, '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: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	all-nli-dev_cosine_accuracy	all-nli-test_cosine_accuracy
-1	-1	-	-	0.6211	-
0.016	100	2.6312	0.8138	0.7939	-
0.032	200	1.1584	0.6249	0.8232	-
0.048	300	0.9679	0.5541	0.8343	-
0.064	400	0.9226	0.5611	0.8430	-
0.08	500	0.9101	0.5274	0.8553	-
0.096	600	0.8655	0.4890	0.8685	-
0.112	700	0.8312	0.5214	0.8768	-
0.128	800	0.8305	0.5567	0.8677	-
0.144	900	0.7703	0.4940	0.8826	-
0.16	1000	0.679	0.5206	0.8572	-
0.176	1100	0.7441	0.5054	0.8788	-
0.192	1200	0.6862	0.4745	0.8832	-
0.208	1300	0.6419	0.4997	0.8931	-
0.224	1400	0.7631	0.4611	0.8849	-
0.24	1500	0.698	0.4679	0.8931	-
0.256	1600	0.6035	0.4362	0.8960	-
0.272	1700	0.6201	0.4490	0.8878	-
0.288	1800	0.5754	0.4431	0.8981	-
0.304	1900	0.512	0.4739	0.8984	-
0.32	2000	0.5218	0.5082	0.8961	-
0.336	2100	0.5293	0.4141	0.8999	-
0.352	2200	0.5168	0.4223	0.9025	-
0.368	2300	0.5037	0.4505	0.8991	-
0.384	2400	0.4958	0.4338	0.8944	-
0.4	2500	0.4969	0.4279	0.9016	-
0.416	2600	0.4748	0.4342	0.8987	-
0.432	2700	0.5305	0.4149	0.8960	-
0.448	2800	0.4726	0.4178	0.9037	-
0.464	2900	0.4979	0.4139	0.9033	-
0.48	3000	0.4691	0.4136	0.9051	-
0.496	3100	0.4696	0.4086	0.9089	-
0.512	3200	0.4016	0.4221	0.9087	-
0.528	3300	0.4203	0.4322	0.9090	-
0.544	3400	0.4501	0.4060	0.9075	-
0.56	3500	0.4296	0.3973	0.9040	-
0.576	3600	0.3731	0.4298	0.9037	-
0.592	3700	0.4131	0.4036	0.9031	-
0.608	3800	0.4051	0.4140	0.9034	-
0.624	3900	0.4162	0.4043	0.9066	-
0.64	4000	0.4106	0.3957	0.9107	-
0.656	4100	0.3189	0.4152	0.9052	-
0.672	4200	0.3506	0.3954	0.9055	-
0.688	4300	0.3851	0.4004	0.9022	-
0.704	4400	0.3302	0.3964	0.9025	-
0.72	4500	0.3626	0.4100	0.9060	-
0.736	4600	0.3179	0.4012	0.9058	-
0.752	4700	0.3687	0.3882	0.9052	-
0.768	4800	0.3644	0.3819	0.9074	-
0.784	4900	0.3684	0.3826	0.9095	-
0.8	5000	0.3002	0.3768	0.9072	-
0.816	5100	0.3226	0.3759	0.9093	-
0.832	5200	0.3186	0.3736	0.9090	-
0.848	5300	0.3137	0.3781	0.9078	-
0.864	5400	0.3141	0.3798	0.9101	-
0.88	5500	0.3241	0.3703	0.9095	-
0.896	5600	0.2999	0.3691	0.9104	-
0.912	5700	0.3046	0.3704	0.9110	-
0.928	5800	0.293	0.3710	0.9104	-
0.944	5900	0.2824	0.3687	0.9093	-
0.96	6000	0.305	0.3678	0.9111	-
0.976	6100	0.2883	0.3693	0.9115	-
0.992	6200	0.1689	0.3692	0.9116	-
-1	-1	-	-	-	0.9248

Framework Versions

Python: 3.12.3
Sentence Transformers: 4.1.0
Transformers: 4.52.4
PyTorch: 2.7.1+cu118
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

Citation

BibTeX

Sentence Transformers

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",
}

MultipleNegativesRankingLoss

bibtex

@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}
}

	anchor	positive	negative
type	string	string	string
details	min: 7 tokens mean: 10.46 tokens max: 46 tokens	min: 6 tokens mean: 12.81 tokens max: 40 tokens	min: 5 tokens mean: 13.4 tokens max: 50 tokens