This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 384 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 384, '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})
  (2): Normalize()
)

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("jonny9f/food_embeddings")
# Run inference
sentences = [
    'Beef Top Round, lean raw',
    'Luncheon Slices, meatless',
    'Pasta Sauce, spaghetti/marinara ready-to-serve',
]
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

Semantic Similarity

Dataset: validation
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9913
spearman_cosine	0.9868

Training Details

Training Dataset

Unnamed Dataset

Size: 1,200,000 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 4 tokens
mean: 10.2 tokens
max: 28 tokens
min: 4 tokens
mean: 9.65 tokens
max: 23 tokens
min: 0.0
mean: 0.26
max: 0.92

Samples:

sentence_0	sentence_1	label
`Beef top round roast, boneless lean select cooked`	`Blueberries, canned wild in heavy syrup drained`	`0.21440656185150148`
`Nance, frozen unsweetened`	`Soymilk, unsweetened`	`0.3654276132583618`
`Drops - Lemonade`	`Pickle relish, sweet`	`0.30108280181884767`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	validation_spearman_cosine
0.0133	500	0.0031	-
0.0267	1000	0.0028	-
0.04	1500	0.0025	-
0.0533	2000	0.0024	-
0.0667	2500	0.0023	-
0.08	3000	0.0022	-
0.0933	3500	0.0021	-
0.1067	4000	0.002	-
0.12	4500	0.002	-
0.1333	5000	0.0019	-
0.1467	5500	0.0018	-
0.16	6000	0.0018	-
0.1733	6500	0.0017	-
0.1867	7000	0.0017	-
0.2	7500	0.0016	-
0.2133	8000	0.0016	-
0.2267	8500	0.0016	-
0.24	9000	0.0015	-
0.2533	9500	0.0015	-
0.2667	10000	0.0015	-
0.28	10500	0.0015	-
0.2933	11000	0.0015	-
0.3067	11500	0.0014	-
0.32	12000	0.0014	-
0.3333	12500	0.0013	-
0.3467	13000	0.0013	-
0.36	13500	0.0013	-
0.3733	14000	0.0013	-
0.3867	14500	0.0012	-
0.4	15000	0.0012	-
0.4133	15500	0.0012	-
0.4267	16000	0.0012	-
0.44	16500	0.0012	-
0.4533	17000	0.0012	-
0.4667	17500	0.0011	-
0.48	18000	0.0011	-
0.4933	18500	0.0011	-
0.5067	19000	0.0011	-
0.52	19500	0.0011	-
0.5333	20000	0.0011	-
0.5467	20500	0.0011	-
0.56	21000	0.001	-
0.5733	21500	0.001	-
0.5867	22000	0.001	-
0.6	22500	0.001	-
0.6133	23000	0.001	-
0.6267	23500	0.001	-
0.64	24000	0.0009	-
0.6533	24500	0.0009	-
0.6667	25000	0.0009	-
0.68	25500	0.0009	-
0.6933	26000	0.0009	-
0.7067	26500	0.0009	-
0.72	27000	0.0009	-
0.7333	27500	0.0009	-
0.7467	28000	0.0009	-
0.76	28500	0.0008	-
0.7733	29000	0.0008	-
0.7867	29500	0.0008	-
0.8	30000	0.0008	-
0.8133	30500	0.0008	-
0.8267	31000	0.0008	-
0.84	31500	0.0008	-
0.8533	32000	0.0008	-
0.8667	32500	0.0008	-
0.88	33000	0.0007	-
0.8933	33500	0.0007	-
0.9067	34000	0.0008	-
0.92	34500	0.0007	-
0.9333	35000	0.0007	-
0.9467	35500	0.0007	-
0.96	36000	0.0007	-
0.9733	36500	0.0007	-
0.9867	37000	0.0007	-
1.0	37500	0.0007	0.9799
0.0133	500	0.0009	-
0.0267	1000	0.0011	-
0.04	1500	0.0011	-
0.0533	2000	0.001	-
0.0667	2500	0.001	-
0.08	3000	0.001	-
0.0933	3500	0.001	-
0.1067	4000	0.001	-
0.12	4500	0.001	-
0.1333	5000	0.001	-
0.1467	5500	0.001	-
0.16	6000	0.0009	-
0.1733	6500	0.0009	-
0.1867	7000	0.0009	-
0.2	7500	0.0009	-
0.2133	8000	0.001	-
0.2267	8500	0.0009	-
0.24	9000	0.0009	-
0.2533	9500	0.0009	-
0.2667	10000	0.0008	-
0.28	10500	0.0009	-
0.2933	11000	0.0008	-
0.3067	11500	0.0008	-
0.32	12000	0.0008	-
0.3333	12500	0.0008	-
0.3467	13000	0.0008	-
0.36	13500	0.0008	-
0.3733	14000	0.0008	-
0.3867	14500	0.0008	-
0.4	15000	0.0008	-
0.4133	15500	0.0007	-
0.4267	16000	0.0007	-
0.44	16500	0.0008	-
0.4533	17000	0.0007	-
0.4667	17500	0.0007	-
0.48	18000	0.0007	-
0.4933	18500	0.0007	-
0.5067	19000	0.0007	-
0.52	19500	0.0007	-
0.5333	20000	0.0007	-
0.5467	20500	0.0007	-
0.56	21000	0.0007	-
0.5733	21500	0.0006	-
0.5867	22000	0.0007	-
0.6	22500	0.0006	-
0.6133	23000	0.0006	-
0.6267	23500	0.0006	-
0.64	24000	0.0006	-
0.6533	24500	0.0006	-
0.6667	25000	0.0006	-
0.68	25500	0.0006	-
0.6933	26000	0.0006	-
0.7067	26500	0.0006	-
0.72	27000	0.0006	-
0.7333	27500	0.0006	-
0.7467	28000	0.0006	-
0.76	28500	0.0005	-
0.7733	29000	0.0005	-
0.7867	29500	0.0006	-
0.8	30000	0.0005	-
0.8133	30500	0.0005	-
0.8267	31000	0.0005	-
0.84	31500	0.0005	-
0.8533	32000	0.0005	-
0.8667	32500	0.0005	-
0.88	33000	0.0005	-
0.8933	33500	0.0005	-
0.9067	34000	0.0005	-
0.92	34500	0.0005	-
0.9333	35000	0.0005	-
0.9467	35500	0.0005	-
0.96	36000	0.0005	-
0.9733	36500	0.0005	-
0.9867	37000	0.0005	-
1.0	37500	0.0005	0.9850
0.0133	500	0.0004	-
0.0267	1000	0.0005	-
0.04	1500	0.0005	-
0.0533	2000	0.0005	-
0.0667	2500	0.0005	-
0.08	3000	0.0005	-
0.0933	3500	0.0005	-
0.1067	4000	0.0004	-
0.12	4500	0.0004	-
0.1333	5000	0.0004	-
0.1467	5500	0.0004	-
0.16	6000	0.0004	-
0.1733	6500	0.0004	-
0.1867	7000	0.0004	-
0.2	7500	0.0004	-
0.2133	8000	0.0004	-
0.2267	8500	0.0004	-
0.24	9000	0.0004	-
0.2533	9500	0.0004	-
0.2667	10000	0.0004	-
0.28	10500	0.0004	-
0.2933	11000	0.0004	-
0.3067	11500	0.0004	-
0.32	12000	0.0004	-
0.3333	12500	0.0004	-
0.3467	13000	0.0004	-
0.36	13500	0.0004	-
0.3733	14000	0.0004	-
0.3867	14500	0.0004	-
0.4	15000	0.0004	-
0.4133	15500	0.0004	-
0.4267	16000	0.0004	-
0.44	16500	0.0004	-
0.4533	17000	0.0004	-
0.4667	17500	0.0004	-
0.48	18000	0.0004	-
0.4933	18500	0.0004	-
0.5067	19000	0.0004	-
0.52	19500	0.0004	-
0.5333	20000	0.0004	-
0.5467	20500	0.0004	-
0.56	21000	0.0004	-
0.5733	21500	0.0004	-
0.5867	22000	0.0004	-
0.6	22500	0.0004	-
0.6133	23000	0.0004	-
0.6267	23500	0.0004	-
0.64	24000	0.0004	-
0.6533	24500	0.0004	-
0.6667	25000	0.0004	-
0.68	25500	0.0004	-
0.6933	26000	0.0004	-
0.7067	26500	0.0004	-
0.72	27000	0.0004	-
0.7333	27500	0.0004	-
0.7467	28000	0.0004	-
0.76	28500	0.0004	-
0.7733	29000	0.0004	-
0.7867	29500	0.0004	-
0.8	30000	0.0004	-
0.8133	30500	0.0004	-
0.8267	31000	0.0004	-
0.84	31500	0.0004	-
0.8533	32000	0.0004	-
0.8667	32500	0.0004	-
0.88	33000	0.0004	-
0.8933	33500	0.0004	-
0.9067	34000	0.0004	-
0.92	34500	0.0004	-
0.9333	35000	0.0004	-
0.9467	35500	0.0004	-
0.96	36000	0.0004	-
0.9733	36500	0.0004	-
0.9867	37000	0.0004	-
1.0	37500	0.0004	0.9868

Framework Versions

Python: 3.11.3
Sentence Transformers: 3.3.1
Transformers: 4.48.0
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 4 tokens mean: 10.2 tokens max: 28 tokens	min: 4 tokens mean: 9.65 tokens max: 23 tokens	min: 0.0 mean: 0.26 max: 0.92