metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:557850
  - loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
  - source_sentence: A man is jumping unto his filthy bed.
    sentences:
      - A young male is looking at a newspaper while 2 females walks past him.
      - The bed is dirty.
      - The man is on the moon.
  - source_sentence: >-
      A carefully balanced male stands on one foot near a clean ocean beach
      area.
    sentences:
      - A man is ouside near the beach.
      - Three policemen patrol the streets on bikes
      - A man is sitting on his couch.
  - source_sentence: The man is wearing a blue shirt.
    sentences:
      - Near the trashcan the man stood and smoked
      - >-
        A man in a blue shirt leans on a wall beside a road with a blue van and
        red car with water in the background.
      - A man in a black shirt is playing a guitar.
  - source_sentence: The girls are outdoors.
    sentences:
      - Two girls riding on an amusement part ride.
      - a guy laughs while doing laundry
      - >-
        Three girls are standing together in a room, one is listening, one is
        writing on a wall and the third is talking to them.
  - source_sentence: >-
      A construction worker peeking out of a manhole while his coworker sits on
      the sidewalk smiling.
    sentences:
      - A worker is looking out of a manhole.
      - A man is giving a presentation.
      - The workers are both inside the manhole.
datasets:
  - sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
model-index:
  - name: MPNet base trained on AllNLI triplets
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9155528554070473
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.08475091130012151
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.912363304981774
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9113001215066828
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9155528554070473
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.9261612952035103
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.07262823422605538
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9196550158874263
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9201089423513391
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9261612952035103
            name: Max Accuracy

MPNet base trained on AllNLI triplets

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 tokens
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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bingcheng9/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

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9156
dot_accuracy	0.0848
manhattan_accuracy	0.9124
euclidean_accuracy	0.9113
max_accuracy	0.9156

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9262
dot_accuracy	0.0726
manhattan_accuracy	0.9197
euclidean_accuracy	0.9201
max_accuracy	0.9262

Training Details

Training Dataset

all-nli

Dataset: all-nli at d482672
Size: 557,850 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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
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: 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: 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: 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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	all-nli-dev_max_accuracy	all-nli-test_max_accuracy
0	0	-	-	0.6832	-
0.016	100	3.0282	1.5784	0.7751	-
0.032	200	1.2537	0.9115	0.7983	-
0.048	300	1.435	0.7883	0.8095	-
0.064	400	0.8952	0.7637	0.8112	-
0.08	500	0.8482	0.8154	0.8086	-
0.096	600	1.056	0.8993	0.8033	-
0.112	700	0.967	0.8740	0.8007	-
0.128	800	1.1139	1.0261	0.7930	-
0.144	900	1.1765	0.9142	0.8127	-
0.16	1000	1.1022	0.8580	0.7980	-
0.176	1100	1.1095	1.0273	0.7889	-
0.192	1200	1.0725	0.9443	0.7998	-
0.208	1300	0.9075	0.8191	0.8070	-
0.224	1400	0.7504	0.8069	0.8104	-
0.24	1500	0.815	0.7824	0.8193	-
0.256	1600	0.6089	0.8256	0.8168	-
0.272	1700	0.8689	0.8470	0.8079	-
0.288	1800	0.8359	0.8588	0.8103	-
0.304	1900	0.8157	0.7955	0.8129	-
0.32	2000	0.7511	0.7027	0.8467	-
0.336	2100	0.603	0.7624	0.8467	-
0.352	2200	0.6005	0.7071	0.8686	-
0.368	2300	0.8079	0.7497	0.8492	-
0.384	2400	0.7237	0.6801	0.8586	-
0.4	2500	0.669	0.6595	0.8694	-
0.416	2600	0.6013	0.6700	0.8587	-
0.432	2700	0.8929	0.7217	0.8645	-
0.448	2800	0.8627	0.6720	0.8521	-
0.464	2900	0.8279	0.6561	0.8698	-
0.48	3000	0.6893	0.6243	0.8692	-
0.496	3100	0.7609	0.6052	0.8711	-
0.512	3200	0.5704	0.6042	0.8677	-
0.528	3300	0.6117	0.5398	0.8827	-
0.544	3400	0.5231	0.5743	0.8797	-
0.56	3500	0.5231	0.5817	0.8923	-
0.576	3600	0.4825	0.5309	0.8911	-
0.592	3700	0.5464	0.5261	0.8961	-
0.608	3800	0.4846	0.5017	0.8979	-
0.624	3900	0.4896	0.5280	0.8947	-
0.64	4000	0.7499	0.5435	0.9061	-
0.656	4100	0.916	0.5268	0.9060	-
0.672	4200	0.8733	0.4855	0.9074	-
0.688	4300	0.6963	0.4717	0.9105	-
0.704	4400	0.5907	0.4567	0.9142	-
0.72	4500	0.5768	0.4702	0.9111	-
0.736	4600	0.6173	0.4491	0.9151	-
0.752	4700	0.6802	0.4680	0.9124	-
0.768	4800	0.6099	0.4372	0.9130	-
0.784	4900	0.5689	0.4480	0.9066	-
0.8	5000	0.6554	0.4603	0.9118	-
0.816	5100	0.511	0.4356	0.9116	-
0.832	5200	0.5725	0.4246	0.9092	-
0.848	5300	0.5196	0.4359	0.9107	-
0.864	5400	0.6112	0.4403	0.9104	-
0.88	5500	0.5233	0.4236	0.9115	-
0.896	5600	0.5467	0.4217	0.9127	-
0.912	5700	0.6109	0.4199	0.9156	-
0.928	5800	0.54	0.4077	0.9148	-
0.944	5900	0.6739	0.4111	0.9145	-
0.96	6000	0.723	0.4170	0.9154	-
0.976	6100	0.6753	0.4162	0.9154	-
0.992	6200	0.0591	0.4157	0.9156	-
1.0	6250	-	-	-	0.9262

Framework Versions

Python: 3.12.4
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.2.2
Accelerate: 0.26.0
Datasets: 3.0.2
Tokenizers: 0.20.1

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",
}

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}
}