Upload food embeddings model

1_Pooling/config.json

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+{
+  "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
+}

README.md

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+---
+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) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `validation`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.9913     |
+| **spearman_cosine** | **0.9868** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 1,200,000 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                       | sentence_1                                                                       | label                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                           |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 10.2 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.65 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.26</li><li>max: 0.92</li></ul> |
+* Samples:
+  | sentence_0                                                     | sentence_1                                                   | label                            |
+  |:---------------------------------------------------------------|:-------------------------------------------------------------|:---------------------------------|
+  | <code>Beef top round roast, boneless lean select cooked</code> | <code>Blueberries, canned wild in heavy syrup drained</code> | <code>0.21440656185150148</code> |
+  | <code>Nance, frozen unsweetened</code>                         | <code>Soymilk, unsweetened</code>                            | <code>0.3654276132583618</code>  |
+  | <code>Drops - Lemonade</code>                                  | <code>Pickle relish, sweet</code>                            | <code>0.30108280181884767</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](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
+<details><summary>Click to expand</summary>
+
+- `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
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| 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                     |
+
+</details>
+
+### 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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "fine_tuned_mpnet_food_embeddings",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.0",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eebe0c3415141b3025aaf909f6040bb92835dcef8671d07f87507dd151f16e30
+size 437967672

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,73 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}