Upload food embeddings model

README.md

@@ -4,35 +4,35 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:4278
+- dataset_size:4256
 - loss:ContrastiveLoss
 base_model: sentence-transformers/all-mpnet-base-v2
 widget:
-- source_sentence: Chicken thigh, meat and skin, cooked fried
+- source_sentence: So Delicious Key Lime Yogurt
   sentences:
-  - Pork loin chops, center loin bone-in cooked braised
-  - Nature’S Path Homestyle Waffles
-  - Turkey, liver raw
-- source_sentence: Lamb, New Zealand hind-shank lean and fat raw
+  - Squash, yellow raw
+  - Babyfood, mixed fruit yogurt
+  - Beef, rib eye steak/roast bone-in lip-on raw
+- source_sentence: Cocoa Bumpers Cereal, Quaker Mother's
   sentences:
-  - Cabbage, pak-choi cooked with salt
-  - Lamb, New Zealand, frozen, mixed cuts, raw
-  - gitwero, banana/potato + vegetable
-- source_sentence: Lamb, New Zealand chump boneless lean raw
+  - Lovebird Cereal Honey Box
+  - Ham, canned roasted
+  - Chicken, light meat with skin, cooked stewed
+- source_sentence: Broadbeans, raw immature seeds
   sentences:
-  - Mushrooms, white stir-fried
-  - Mooala Organic Chocolate Bananamilk
-  - Elk, raw game meat
-- source_sentence: Cereal, mixed with applesauce and bananas, junior fortified
+  - Peas, canned rinsed
+  - Promin Minestrone Soup
+  - Rice, brown long-grain cooked
+- source_sentence: Chicken, dark meat thigh meat and skin, added solution cooked braised
   sentences:
-  - Beef brisket, lean and fat braised
-  - Beef, grilled boneless top loin steak, no fat
-  - Beef top loin, cooked grilled
-- source_sentence: Beef, cooked lean cuts
+  - Moose, raw
+  - Chickpeas, cooked with salt
+  - Sausage, pork turkey and beef reduced sodium
+- source_sentence: Shortening, soy and cottonseed for pastries
   sentences:
-  - Chicken, separable fat, raw
-  - Burger King, double cheeseburger
-  - Beef, chuck under blade pot roast boneless cooked braised
+  - Soup, chicken noodle reduced sodium
+  - Sea lion kidney, Steller (Alaska Native)
+  - Salad, McDonald's side
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 metrics:
@@ -49,10 +49,10 @@ model-index:
       type: validation
     metrics:
     - type: pearson_cosine
-      value: 0.7954090460164015
+      value: 0.8269809784218102
       name: Pearson Cosine
     - type: spearman_cosine
-      value: 0.8307590405531435
+      value: 0.845955787172452
       name: Spearman Cosine
 ---
 
@@ -106,9 +106,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("jonny9f/food_embeddings4")
 # Run inference
 sentences = [
-    'Beef, cooked lean cuts',
-    'Beef, chuck under blade pot roast boneless cooked braised',
-    'Burger King, double cheeseburger',
+    'Shortening, soy and cottonseed for pastries',
+    'Sea lion kidney, Steller (Alaska Native)',
+    'Soup, chicken noodle reduced sodium',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -153,10 +153,10 @@ You can finetune this model on your own dataset.
 * 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      | 0.7954     |
-| **spearman_cosine** | **0.8308** |
+| Metric              | Value     |
+|:--------------------|:----------|
+| pearson_cosine      | 0.827     |
+| **spearman_cosine** | **0.846** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -177,19 +177,19 @@ You can finetune this model on your own dataset.
 #### Unnamed Dataset
 
 
-* Size: 4,278 training samples
+* Size: 4,256 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: 3 tokens</li><li>mean: 10.01 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.95 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.37</li><li>max: 0.85</li></ul> |
+  |         | sentence_0                                                                       | sentence_1                                                                       | label                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                           |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 9.91 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.96 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.39</li><li>max: 0.85</li></ul> |
 * Samples:
-  | sentence_0                                                           | sentence_1                                                       | label                           |
-  |:---------------------------------------------------------------------|:-----------------------------------------------------------------|:--------------------------------|
-  | <code>Cocoa Butter Oil</code>                                        | <code>Lamb, tenderloin fast fried</code>                         | <code>0.0</code>                |
-  | <code>Shrimp, cooked breaded fried</code>                            | <code>Shrimp, breaded and fried (family style restaurant)</code> | <code>0.0</code>                |
-  | <code>Pineapple Juice, canned unsweetened with added vitamins</code> | <code>cranberry-apple juice drink, bottled</code>                | <code>0.7392904043197631</code> |
+  | sentence_0                                   | sentence_1                                      | label                           |
+  |:---------------------------------------------|:------------------------------------------------|:--------------------------------|
+  | <code>Fava Beans, cooked without salt</code> | <code>Red Kidney Beans, cooked with salt</code> | <code>0.85</code>               |
+  | <code>Spaghetti squash, raw</code>           | <code>Mushrooms, white cooked</code>            | <code>0.5719985961914062</code> |
+  | <code>Chicken, back with skin roasted</code> | <code>Beef rib, roasted</code>                  | <code>0.0</code>                |
 * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
   ```json
   {
@@ -331,7 +331,7 @@ You can finetune this model on your own dataset.
 ### Training Logs
 | Epoch | Step | validation_spearman_cosine |
 |:-----:|:----:|:--------------------------:|
-| 1.0   | 134  | 0.8308                     |
+| 1.0   | 133  | 0.8460                     |
 
 
 ### Framework Versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:44512fb0fe9566fddc194c4ecd20617070852653233eccc58ae88f6fc48e2c73
 size 437967672