metadata

language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - 100K<n<1M
  - loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
widget:
  - source_sentence: >-
      The strangely dressed guys, one wearing an orange wig, sunglasses with
      peace signs, and a karate costume with an orannge belt, another wearing a
      curly blue wig, heart shaped sunglasses, and a karate outfit painted with
      leaves, and the third wearing pink underwear, a black afro, and giant
      sunglasses.
    sentences:
      - >-
        A blonde female is reaching into a golf hole while holding two golf
        balls.
      - There are people wearing outfits.
      - The people are naked.
  - source_sentence: A group of children playing and having a good time.
    sentences:
      - The kids are together.
      - The children are reading books.
      - People are pointing at a Middle-aged woman.
  - source_sentence: >-
      Three children dressed in winter clothes are walking through the woods
      while pushing cargo along.
    sentences:
      - A woman is sitting.
      - Three childre are dressed in summer clothes.
      - Three children are dressed in winter clothes.
  - source_sentence: A young child is enjoying the water and rock scenery with their dog.
    sentences:
      - The child and dog are enjoying some fresh air.
      - The teenage boy is taking his cat for a walk beside the water.
      - A lady in blue has birds around her.
  - source_sentence: >-
      Boca da Corrida Encumeada (moderate; 5 hours): views of Curral das Freiras
      and the valley of Ribeiro do Poco.
    sentences:
      - >-
        Boca da Corrida Encumeada is a moderate text that takes 5 hours to
        complete. 
      - This chapter is in the advance category.
      - I think it is something that we need.
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 118.81134392463773
  energy_consumed: 0.30566177669432554
  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: 1.661
  hardware_used: 1 x NVIDIA GeForce RTX 3090
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: 0.9003645200486027
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.09705346294046173
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.8968712029161604
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.8974787363304981
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9003645200486027
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli test
          type: all-nli-test
        metrics:
          - type: cosine_accuracy
            value: 0.9149644424269935
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.08564079285822364
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.911484339536995
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9134513542139506
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9149644424269935
            name: Max Accuracy

MPNet base trained on AllNLI triplets

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the sentence-transformers/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:
- sentence-transformers/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("tomaarsen/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
    'Then he ran.',
    'The people are running.',
    'The man is on his bike.',
]
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	0.9004
dot_accuracy	0.0971
manhattan_accuracy	0.8969
euclidean_accuracy	0.8975
max_accuracy	0.9004

Triplet

Dataset: all-nli-test
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.915
dot_accuracy	0.0856
manhattan_accuracy	0.9115
euclidean_accuracy	0.9135
max_accuracy	0.915

Training Details

Training Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/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:

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

Evaluation Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/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
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
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: 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}
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
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	2.6355	1.0725	0.7924	-
0.032	200	0.9206	0.8342	0.8080	-
0.048	300	1.2567	0.7855	0.8133	-
0.064	400	0.7949	0.8857	0.7974	-
0.08	500	0.7583	0.9487	0.7872	-
0.096	600	1.0022	1.1312	0.7848	-
0.112	700	0.8178	1.2282	0.7895	-
0.128	800	0.9997	1.5132	0.7488	-
0.144	900	1.1173	1.4605	0.7473	-
0.16	1000	1.0089	1.3794	0.7543	-
0.176	1100	1.0235	1.4188	0.7640	-
0.192	1200	1.0031	1.2465	0.7570	-
0.208	1300	0.8286	1.4176	0.7426	-
0.224	1400	0.8411	1.1914	0.7600	-
0.24	1500	0.8389	1.1719	0.7820	-
0.256	1600	0.7144	1.1167	0.7691	-
0.272	1700	0.881	1.0747	0.7902	-
0.288	1800	0.8657	1.1576	0.7966	-
0.304	1900	0.7323	1.0122	0.8322	-
0.32	2000	0.6578	1.1248	0.8273	-
0.336	2100	0.6037	1.1194	0.8269	-
0.352	2200	0.641	1.1410	0.8341	-
0.368	2300	0.7843	1.0600	0.8328	-
0.384	2400	0.8222	0.9988	0.8161	-
0.4	2500	0.7287	1.2026	0.8395	-
0.416	2600	0.6035	0.8802	0.8273	-
0.432	2700	0.8275	1.1631	0.8458	-
0.448	2800	0.8483	0.9218	0.8316	-
0.464	2900	0.8813	1.1187	0.8147	-
0.48	3000	0.7408	0.9582	0.8246	-
0.496	3100	0.7886	0.9364	0.8261	-
0.512	3200	0.6064	0.8338	0.8302	-
0.528	3300	0.6415	0.7895	0.8650	-
0.544	3400	0.5766	0.7525	0.8571	-
0.56	3500	0.6212	0.8605	0.8572	-
0.576	3600	0.5773	0.7460	0.8419	-
0.592	3700	0.6104	0.7480	0.8580	-
0.608	3800	0.5754	0.7215	0.8657	-
0.624	3900	0.5525	0.7900	0.8630	-
0.64	4000	0.7802	0.7443	0.8612	-
0.656	4100	0.9796	0.7756	0.8748	-
0.672	4200	0.9355	0.6917	0.8796	-
0.688	4300	0.7081	0.6442	0.8832	-
0.704	4400	0.6868	0.6395	0.8891	-
0.72	4500	0.5964	0.5983	0.8820	-
0.736	4600	0.6618	0.5754	0.8861	-
0.752	4700	0.6957	0.6177	0.8803	-
0.768	4800	0.6375	0.5577	0.8881	-
0.784	4900	0.5481	0.5496	0.8835	-
0.8	5000	0.6626	0.5728	0.8949	-
0.816	5100	0.5192	0.5329	0.8935	-
0.832	5200	0.5856	0.5188	0.8935	-
0.848	5300	0.5142	0.5252	0.8920	-
0.864	5400	0.6404	0.5641	0.8885	-
0.88	5500	0.5466	0.5209	0.8929	-
0.896	5600	0.575	0.5170	0.8961	-
0.912	5700	0.626	0.5095	0.9001	-
0.928	5800	0.5631	0.4817	0.8984	-
0.944	5900	0.7301	0.4996	0.8984	-
0.96	6000	0.7712	0.5160	0.9014	-
0.976	6100	0.6203	0.5000	0.9007	-
0.992	6200	0.0005	0.4996	0.9004	-
1.0	6250	-	-	-	0.9150

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.306 kWh
Carbon Emitted: 0.119 kg of CO2
Hours Used: 1.661 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: 3.0.0.dev0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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}
}