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MPNet base trained on AllNLI triplets

Name: mpnet-base-all-nli-triplet
Availability: InStock

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, 'architecture': '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("along26/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)
# tensor([[1.0000, 0.5836, 0.6494],
#         [0.5836, 1.0000, 0.6130],
#         [0.6494, 0.6130, 1.0000]])

Evaluation

Metrics

Triplet

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

Metric	all-nli-dev	all-nli-test
cosine_accuracy	0.9072	0.9269

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 "gather_across_devices": false 5}

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 "gather_across_devices": false 5}

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
fp16: True
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

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	2.644	0.8565	0.7890	-
0.032	200	1.1933	0.6266	0.8187	-
0.048	300	0.9572	0.6026	0.8316	-
0.064	400	0.9417	0.5672	0.8434	-
0.08	500	0.9389	0.5626	0.8417	-
0.096	600	0.9275	0.5402	0.8604	-
0.112	700	0.8031	0.5011	0.8703	-
0.128	800	0.8476	0.5033	0.8884	-
0.144	900	0.7866	0.5073	0.8917	-
0.16	1000	0.7336	0.4882	0.8982	-
0.176	1100	0.7047	0.5411	0.8858	-
0.192	1200	0.7392	0.4348	0.8891	-
0.208	1300	0.6411	0.4642	0.8969	-
0.224	1400	0.604	0.4447	0.8970	-
0.24	1500	0.6054	0.4657	0.8938	-
0.256	1600	0.6027	0.4712	0.8899	-
0.272	1700	0.61	0.4663	0.8896	-
0.288	1800	0.6469	0.4405	0.9028	-
0.304	1900	0.6137	0.4432	0.8960	-
0.32	2000	0.5991	0.4620	0.8884	-
0.336	2100	0.5365	0.4334	0.8928	-
0.352	2200	0.4874	0.4160	0.8972	-
0.368	2300	0.5237	0.4389	0.8926	-
0.384	2400	0.5557	0.4228	0.9023	-
0.4	2500	0.4954	0.4156	0.9011	-
0.416	2600	0.4969	0.4200	0.8949	-
0.432	2700	0.5247	0.4473	0.8946	-
0.448	2800	0.5338	0.4226	0.8969	-
0.464	2900	0.4651	0.4066	0.9031	-
0.48	3000	0.4424	0.4024	0.9016	-
0.496	3100	0.4596	0.4179	0.9028	-
0.512	3200	0.4499	0.4025	0.9029	-
0.528	3300	0.452	0.4187	0.8960	-
0.544	3400	0.4253	0.4222	0.8947	-
0.56	3500	0.4242	0.4075	0.9049	-
0.576	3600	0.4048	0.4052	0.9057	-
0.592	3700	0.3868	0.4036	0.8975	-
0.608	3800	0.3972	0.3897	0.9028	-
0.624	3900	0.3807	0.3879	0.9069	-
0.64	4000	0.3518	0.3887	0.9039	-
0.656	4100	0.3835	0.3867	0.9060	-
0.672	4200	0.4213	0.3963	0.9063	-
0.688	4300	0.3922	0.3831	0.9054	-
0.704	4400	0.339	0.3951	0.9086	-
0.72	4500	0.3712	0.4047	0.9034	-
0.736	4600	0.3625	0.3804	0.9042	-
0.752	4700	0.3416	0.4116	0.9061	-
0.768	4800	0.3527	0.4107	0.9093	-
0.784	4900	0.3281	0.3763	0.9077	-
0.8	5000	0.3301	0.3794	0.9026	-
0.816	5100	0.313	0.3768	0.9054	-
0.832	5200	0.3466	0.3762	0.9052	-
0.848	5300	0.2839	0.3683	0.9077	-
0.864	5400	0.314	0.3699	0.9051	-
0.88	5500	0.315	0.3682	0.9084	-
0.896	5600	0.2769	0.3717	0.9070	-
0.912	5700	0.2898	0.3764	0.9099	-
0.928	5800	0.2654	0.3788	0.9118	-
0.944	5900	0.3299	0.3719	0.9099	-
0.96	6000	0.3251	0.3720	0.9086	-
0.976	6100	0.2885	0.3680	0.9067	-
0.992	6200	0.1397	0.3677	0.9072	-
-1	-1	-	-	-	0.9269

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.1.2
Transformers: 4.57.1
PyTorch: 2.8.0+cu126
Accelerate: 1.11.0
Datasets: 4.0.0
Tokenizers: 0.22.1

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