---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1200000
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: Mutton, roasted
sentences:
- Imagine Creamy Butternut Squash Soup
- Perrier Water, bottled
- Crackers, whole-wheat
- source_sentence: Beef Chuck Mock Tender Steak, lean and fat raw
sentences:
- Lamb, Australian leg roasted, bone-in
- Chicken wing, meat and skin, cooked fried flour
- Peaches, canned in heavy syrup
- source_sentence: Squash, zucchini baby raw
sentences:
- Dandelion greens, cooked with salt
- Beets, pickled canned
- Cod, Atlantic canned
- source_sentence: Veggie Meatballs
sentences:
- Salt, iodized
- Sweet and Sour Sauce, ready-to-serve
- Salt pork, raw
- source_sentence: Beef Top Round, lean raw
sentences:
- Ravioli, meat-filled with tomato or meat sauce canned
- Pasta Sauce, spaghetti/marinara ready-to-serve
- Luncheon Slices, meatless
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: validation
type: validation
metrics:
- type: pearson_cosine
value: 0.9913128359649296
name: Pearson Cosine
- type: spearman_cosine
value: 0.9868170667730207
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### 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
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 |