mpnet-base-all-nli-triplet / README.md

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Add new SentenceTransformer model

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metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100000
  - 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
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: 0.8253341317176819
            name: Cosine Accuracy
          - type: cosine_accuracy
            value: 0.9163122773170471
            name: Cosine 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 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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tien314/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	0.8253

Triplet

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9163

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:

{
    "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
num_train_epochs: 1
warmup_ratio: 0.1
bf16: 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
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: 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}
tp_size: 0
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
-1	-1	-	-	0.6211
0.016	100	2.7517	0.8461	0.7871
0.032	200	1.1987	0.6156	0.8253
0.048	300	0.9926	-	-
-1	-1	-	-	0.8302
0.016	100	0.8938	0.5516	0.8393
0.032	200	0.542	0.5312	0.8458
0.048	300	0.2881	0.5963	0.8288
0.064	400	0.954	0.5271	0.8518
0.08	500	0.8661	0.5029	0.8612
0.096	600	0.8477	0.5000	0.8688
0.112	700	0.8385	0.4942	0.8782
0.128	800	0.8051	0.4967	0.8890
0.144	900	0.7436	0.4855	0.8829
0.16	1000	0.6705	0.5059	0.8736
0.176	1100	0.7461	0.4721	0.8875
0.192	1200	0.6506	0.4403	0.8897
0.208	1300	0.6449	0.4410	0.8964
0.224	1400	0.6272	0.4310	0.8949
0.24	1500	0.6698	0.4382	0.8967
0.256	1600	0.624	0.3988	0.9060
0.272	1700	0.5965	0.4297	0.8917
0.288	1800	0.5652	0.4255	0.8995
0.304	1900	0.5301	0.4271	0.9042
0.32	2000	0.5132	0.4547	0.8985
0.336	2100	0.4971	0.4141	0.9028
0.352	2200	0.4969	0.4229	0.8999
0.368	2300	0.4824	0.4106	0.9039
0.384	2400	0.4854	0.4117	0.8952
0.4	2500	0.4874	0.4071	0.9019
0.416	2600	0.4675	0.4428	0.8981
0.432	2700	0.517	0.4130	0.9019
0.448	2800	0.4514	0.4361	0.9034
0.464	2900	0.4981	0.3958	0.9077
0.48	3000	0.4461	0.4124	0.9066
0.496	3100	0.4662	0.4147	0.9066
0.512	3200	0.3938	0.4122	0.9020
0.528	3300	0.4122	0.4157	0.9001
0.544	3400	0.4387	0.4118	0.9029
0.56	3500	0.4181	0.3876	0.9042
0.576	3600	0.3603	0.3888	0.9048
0.592	3700	0.4182	0.3936	0.8988
0.608	3800	0.3918	0.3996	0.9013
0.624	3900	0.4158	0.3777	0.9074
0.64	4000	0.3861	0.3689	0.9081
0.656	4100	0.3142	0.3842	0.9086
0.672	4200	0.3327	0.3794	0.9090
0.688	4300	0.3784	0.3785	0.9052
0.704	4400	0.3208	0.3849	0.9017
0.72	4500	0.3591	0.3910	0.9070
0.736	4600	0.3331	0.3817	0.9092
0.752	4700	0.3567	0.3762	0.9125
0.768	4800	0.3445	0.3639	0.9108
0.784	4900	0.3472	0.3723	0.9116
0.8	5000	0.2895	0.3685	0.9115
0.816	5100	0.3067	0.3714	0.9121
0.832	5200	0.3139	0.3623	0.9134
0.848	5300	0.3106	0.3635	0.9127
0.864	5400	0.2965	0.3681	0.9136
0.88	5500	0.3154	0.3646	0.9145
0.896	5600	0.2963	0.3548	0.9154
0.912	5700	0.296	0.3550	0.9152
0.928	5800	0.2847	0.3535	0.9157
0.944	5900	0.2732	0.3522	0.9162
0.96	6000	0.3154	0.3502	0.9168
0.976	6100	0.2979	0.3510	0.9165
0.992	6200	0.1601	0.3502	0.9163

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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}
}