Update fine-tuned Sentence Transformer model

7e1d01f verified 10 months ago

37.1 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:121408
	- loss:MultipleNegativesRankingLoss
	base_model: sentence-transformers/all-mpnet-base-v2
	widget:
	- source_sentence: "NAME: Baked Beans\n\nCATEGORY: Beans\n\nKEYWORDS: < 60 Mins, Easy,\
	\ Inexpensive\n\nTOOLS: pan\n\nINGREDIENTS: ground beef, bell pepper, onion, brown\
	\ sugar, lemon juice, ketchup, barbecue sauce\n\nINSTRUCTIONS: \nCook you bell\
	\ pepper and onion in with your ground beef. Drain grease.\nIn a casserole mix\
	\ all other ingredients.\nPut in over for 20 minutes."
	sentences:
	- 'NAME: Hearty White Bean Bake


	CATEGORY: Beans


	KEYWORDS: < 60 Mins, Easy, Inexpensive, One-Pot


	TOOLS: pan


	INGREDIENTS: ground turkey, poblano pepper, shallot, maple syrup, apple cider
	vinegar, chili sauce, smoked paprika, cannellini beans


	INSTRUCTIONS:

	Cook the poblano pepper and shallot with the ground turkey until the turkey is
	browned. Drain any excess grease.

	In the same pan, combine the cooked turkey mixture with maple syrup, apple cider
	vinegar, chili sauce, smoked paprika, and cannellini beans.

	Mix well and bake in the oven for 25 minutes, or until heated through and bubbly.'
	- 'NAME: Spicy Southwest Seasoning Mix

	CATEGORY: < 15 Mins

	KEYWORDS: No Cook, Easy, Spice Mix

	TOOLS: Small jar

	INGREDIENTS: onion powder, chili powder, smoked paprika, seasoning salt, cayenne
	pepper, black pepper

	INSTRUCTIONS: Add all ingredients into a small jar. Secure the lid tightly. Shake
	well until thoroughly combined.'
	- 'NAME: Rosé Sangria


	CATEGORY: Beverages


	KEYWORDS: Citrus, Berries, < 4 Hours, Easy, Refreshing


	TOOLS: large punch bowl, wooden spoon


	INGREDIENTS: limes, raspberries, sugar, rosé wine, brandy, strawberry slices


	INSTRUCTIONS:

	Place lime slices and raspberries in a large punch bowl.

	Pour sugar over slices and berries and with a wooden spoon lightly mash together
	until sugar dissolves and the fruit begins to break down.

	Stir in rosé wine and brandy.

	Add strawberry slices.

	Refrigerate at least 2 hours or up to 10.

	Add ice cubes and sparkling water just before serving.'
	- source_sentence: "NAME: Pink Banana Bread\n\nCATEGORY: Quick Breads\n\nKEYWORDS:\
	\ Breads, Pineapple, Tropical Fruits, Fruit, Healthy, < 4 Hours, Easy\n\nTOOLS:\
	\ grease bread pan, oven\n\nINGREDIENTS: yogurt, sugar, crushed pineapple, eggs,\
	\ flour, baking soda, baking powder, salt, bananas, pecans\n\nINSTRUCTIONS: \n\
	Mix yogurt, sugar, crushed pineapple, eggs, flour, baking soda, baking powder,\
	\ salt, grenadine, bananas, and pecans together.\nGrease bread pan.\nBake at 375\
	\ degrees Fahrenheit for 60 minutes or until done."
	sentences:
	- 'NAME: Lemon-Herb Baked Cod


	CATEGORY: Tilapia


	KEYWORDS: Healthy, High Protein, Quick, Baked, Lemon, < 45 Mins


	TOOLS: spoon, baking dish, oven, zester


	INGREDIENTS: olive oil, lemon juice, lemon zest, garlic cloves, dried oregano,
	dried thyme, cod fillets, salt, black pepper


	INSTRUCTIONS: Preheat oven to 400°F (200°C).

	In a small bowl, combine olive oil, lemon juice, lemon zest, minced garlic, oregano,
	thyme, salt, and pepper.

	Place cod fillets in a baking dish.

	Spoon the lemon-herb mixture over the cod, ensuring each fillet is well coated.

	Bake for 12-15 minutes, or until the cod is opaque and flakes easily with a fork.

	Check for doneness by flaking with a fork.

	Serve immediately.'
	- 'NAME: Fluffy Maple Buttercream Frosting

	CATEGORY: Dessert

	KEYWORDS: Low Protein, Kid Friendly, Sweet, Mixer, < 15 Mins, Beginner Cook, Small
	Appliance, Easy, Maple

	TOOLS: mixer, bowl

	INGREDIENTS: vegetable shortening, brown butter, maple extract, confectioners''
	sugar, milk

	INSTRUCTIONS: In a bowl, use a mixer to beat the vegetable shortening with the
	browned butter and maple extract until light and creamy. Gradually add in the
	confectioners'' sugar, beating on low speed until combined, then increase speed
	and beat until fluffy. If needed, add milk, one tablespoon at a time, to reach
	the desired consistency. Add a pinch of cinnamon if using.'
	- 'NAME: Mango Coconut Bread


	CATEGORY: Quick Breads


	KEYWORDS: Breads, Mango, Tropical Fruits, Fruit, Healthy, < 4 Hours, Easy, Gluten-Free


	TOOLS: grease bread pan, oven, mixing bowl


	INGREDIENTS: Greek yogurt, coconut sugar, shredded coconut, eggs, almond flour,
	baking soda, baking powder, salt, mangos, macadamia nuts, lime zest


	INSTRUCTIONS:

	Preheat oven to 375 degrees Fahrenheit.

	In a large mixing bowl, combine Greek yogurt, coconut sugar, shredded coconut,
	eggs, almond flour, baking soda, baking powder, salt, and lime zest. Mix well.

	Fold in diced mangos and chopped macadamia nuts.

	Grease bread pan.

	Pour batter into the prepared bread pan.

	Bake for 55-65 minutes, or until a toothpick inserted into the center comes out
	clean.

	Let cool in the pan for 10 minutes before transferring to a wire rack to cool
	completely.'
	- source_sentence: "NAME: Layered Zucchini & Yellow Squash Casserole\n\nCATEGORY:\
	\ Vegetable\n\nKEYWORDS: Low Protein, Low Cholesterol, Summer, < 60 Mins, Oven\n\
	\nTOOLS: oven, baking pan\n\nINGREDIENTS: zucchini, onion, green bell pepper,\
	\ fresh mushrooms, tomatoes, butter, parmesan cheese\n\nINSTRUCTIONS: \nLightly\
	\ grease an 8 inch square baking pan (or spray with Pam).\nLayer the vegetables\
	\ in the order listed, sprinkling each layer with salt and pepper as desired.\n\
	Dot the top with butter, and sprinkle with Parmesan cheese.\nBake at 350F for\
	\ 35 minutes or until crisp-tender."
	sentences:
	- 'NAME: Rustic Spelt Bread


	CATEGORY: Yeast Breads


	KEYWORDS: Breads, Grains, Swiss, European, Low Cholesterol, Healthy, Small Appliance,
	< 4 Hours, Easy, Spelt


	TOOLS: bowl, sharp knife, oven, electric mixer, baking sheet


	INGREDIENTS: spelt flour, whole wheat flour, salt, dry yeast, warm water, milk,
	olive oil


	INSTRUCTIONS:

	Combine the spelt flour, whole wheat flour, and salt in a large bowl. Add the
	dry yeast.

	Pour in the warm water, milk, and olive oil. Mix using an electric mixer with
	a dough hook until the dough forms a fairly firm ball and cleans the sides of
	the bowl.

	Let the dough rise in a warm place until it has doubled in size.

	Preheat the oven to 400°F.

	Shape the dough into a round loaf on a prepared baking sheet. Cover with a damp
	cloth, and allow to rest for about 20 minutes for a second rise.

	Use a sharp knife to score the top of the loaf with a simple cross pattern. Bake
	for 35 to 40 minutes, or until the crust is golden brown and the loaf sounds hollow
	when tapped.'
	- 'NAME: Layered Eggplant & Bell Pepper Casserole


	CATEGORY: Vegetable


	KEYWORDS: Low Protein, Low Cholesterol, Summer, < 60 Mins, Oven, Vegetarian


	TOOLS: oven, baking pan


	INGREDIENTS: eggplant, red onion, yellow bell pepper, fresh cremini mushrooms,
	diced tomatoes, olive oil, mozzarella cheese


	INSTRUCTIONS: Lightly grease an 8 inch square baking pan (or spray with cooking
	spray). Layer the vegetables in the order listed, sprinkling each layer with salt,
	pepper, and a pinch of dried oregano as desired. Drizzle the top with olive oil,
	and sprinkle with mozzarella cheese. Bake at 350F for 40 minutes or until the
	vegetables are tender and the cheese is melted and lightly browned.'
	- 'NAME: Turkey Spinach Orzo Skillet


	CATEGORY: One Dish Meal


	KEYWORDS: Turkey, Poultry, Meat, Low Cholesterol, Healthy, < 45 Mins, Stove Top,
	Quick


	TOOLS: large skillet


	INGREDIENTS: olive oil, butter, water, ground turkey, frozen spinach, sun-dried
	tomatoes, dried oregano, feta cheese, orzo pasta


	INSTRUCTIONS: In large skillet, saute orzo pasta in olive oil until lightly toasted
	over medium heat. Stir in water and oregano; bring to a boil over high heat. Cover;
	reduce heat to low. Simmer 8 minutes. Stir in spinach, ground turkey, and sun-dried
	tomatoes. Cover, simmer 5 to 7 minutes or until most of liquid is absorbed and
	turkey is cooked through. Crumble feta cheese over the top.'
	- source_sentence: "NAME: Easy Basalmic Vinaigrette\n\nCATEGORY: Salad Dressings\n\
	\nKEYWORDS: < 15 Mins, Easy\n\nTOOLS: \n\nINGREDIENTS: extra virgin olive oil,\
	\ Dijon mustard, dried basil, salt, fresh ground pepper\n\nINSTRUCTIONS: \nPlace\
	\ all ingredients in a 20 ounce reusable water bottle.\nShake vigorously until\
	\ combined."
	sentences:
	- 'NAME: Savory Turkey Loaf


	CATEGORY: One Dish Meal


	KEYWORDS: Meat, Weeknight, < 4 Hours, Inexpensive, Easy


	TOOLS: bread pan, oven, large bowl, turkey bowl


	INGREDIENTS: ground turkey, onions, bell pepper, garlic powder, salt, egg, bread
	crumbs, Worcestershire sauce


	INSTRUCTIONS:

	Preheat oven to 375 degrees F.

	Finely dice the onions and bell pepper. In a large bowl, combine ground turkey,
	diced onions, diced bell pepper, salt, garlic powder, and a dash of Worcestershire
	sauce. Mix thoroughly with your hands until well combined. Add bread crumbs to
	the mixture and combine again using your hands. Incorporate the egg, mixing until
	evenly distributed.

	Press the mixture firmly into a bread pan.

	Bake for 50-60 minutes, or until the internal temperature reaches 165 degrees
	F. Let stand for 10 minutes before slicing and serving.'
	- 'NAME: Smoked Salmon Spread


	CATEGORY: Spreads


	KEYWORDS: Salmon, < 4 Hours, Easy, Smoked, Appetizer


	TOOLS:


	INGREDIENTS: cream cheese, mayonnaise, dill


	INSTRUCTIONS:

	Combine the cream cheese and mayonnaise.

	Mix well, and chill for 2 hours.

	Garnish with fresh dill before serving with crackers, bagel chips, or vegetables.'
	- 'NAME: Simple Lemon Herb Vinaigrette

	CATEGORY: Salad Dressings

	KEYWORDS: < 15 Mins, Easy, Fresh

	TOOLS: 20 ounce reusable water bottle

	INGREDIENTS: extra virgin olive oil, honey Dijon mustard, dried oregano, salt,
	fresh ground pepper, lemon juice

	INSTRUCTIONS: Place all ingredients in a 20 ounce reusable water bottle. Shake
	vigorously until combined. Let stand for 5 minutes before serving to allow flavors
	to meld.'
	- source_sentence: "NAME: Spinach with Raisins and Pine Nuts\n\nCATEGORY: Fruit\n\n\
	KEYWORDS: Vegetable, Nuts, Low Cholesterol, Healthy, < 30 Mins, Stove Top\n\n\
	TOOLS: grill, pot\n\nINGREDIENTS: fresh spinach, pine nuts, salt, raisins, olive\
	\ oil, lemon juice\n\nINSTRUCTIONS: \nClean the spinach thoroughly.\nGrill the\
	\ pine nuts until golden brown, watching carefully so as not to burn.\nBring a\
	\ pot of salted water to the boil and toss in raisins and spinach.\nDrain as soon\
	\ as spinach goes limp.\ntoss in olive oil and lemon juice, and scatter with the\
	\ grilled pine nuts."
	sentences:
	- 'NAME: Dried Apricots with Pistachios and Almonds


	CATEGORY: Fruit


	KEYWORDS: Dried Fruit, Nuts, Low Cholesterol, Healthy, < 30 Mins, Stove Top, Vegan


	TOOLS: grill, pot


	INGREDIENTS: dried apricots, pistachios, salt, slivered almonds, olive oil, orange
	juice


	INSTRUCTIONS:

	Soak the dried apricots in warm water for 10 minutes to soften them.

	Grill the pistachios until lightly toasted, being careful not to burn them.

	Bring a pot of salted water to the boil and add the softened apricots.

	Drain immediately after the apricots plump up slightly.

	Toss with olive oil and orange juice, then sprinkle with the grilled pistachios
	and slivered almonds.'
	- 'NAME: Smoky Chipotle Turkey Meatloaf


	CATEGORY: Meat


	KEYWORDS: < 60 Mins, Spicy, Oven, Comfort Food


	TOOLS: frying pan, meat thermometer, oven, loaf pan


	INGREDIENTS: bacon, yellow onion, green bell pepper, chipotle powder, garlic powder,
	dried oregano, salt, ground mustard, smoked paprika, chili powder, tomato paste,
	chicken broth, eggs, ground turkey


	INSTRUCTIONS:

	Preheat oven to 425 degrees.

	Cook bacon in frying pan, remove, drain, and chop.

	Leave drippings in pan and saute (but do not brown) onion and green pepper.

	Add chipotle powder, garlic powder, oregano, salt, mustard, smoked paprika, and
	chili powder.

	Cook for 8 minutes.

	Remove pan from heat and add tomato paste and chicken broth.

	Mix bread crumbs with eggs and add to ground turkey.

	Add spice mixture and bacon to turkey mixture and mix gently.

	Place mixture in two or three 8 x 4 inch individual loaf pans.

	Cook until done, about 35 to 45 minutes, or until internal temperature reaches
	165 degrees on a meat thermometer.

	Let rest for 10 minutes before slicing.'
	- 'NAME: Buttermilk Corn Fritters


	CATEGORY: Breads


	KEYWORDS: Healthy, Spicy, < 60 Mins, Deep Fried, Corn


	TOOLS: pan, mixing bowl, slotted spoon


	INGREDIENTS: yellow cornmeal, gluten-free flour blend, baking powder, brown sugar,
	salt, eggs, buttermilk, scallions, cheddar cheese


	INSTRUCTIONS: In a mixing bowl, combine cornmeal, flour, baking powder, brown
	sugar, and salt; mix well. Add eggs, buttermilk, chopped scallions, and shredded
	cheddar cheese; stir until just combined. Heat 1-inch of oil to 365°F in a pan.
	Carefully drop spoonfuls of batter into the hot oil, cooking in batches to avoid
	overcrowding. Fry fritters for 2-3 minutes, flipping halfway through, until golden
	brown and cooked through. Remove fritters with a slotted spoon and place on paper
	towel-lined plates to drain excess oil. Serve immediately.'
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# 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 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
	- 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("GPTasty/TastyRecipesEmbedder")
	# Run inference
	sentences = [
	'NAME: Spinach with Raisins and Pine Nuts\n\nCATEGORY: Fruit\n\nKEYWORDS: Vegetable, Nuts, Low Cholesterol, Healthy, < 30 Mins, Stove Top\n\nTOOLS: grill, pot\n\nINGREDIENTS: fresh spinach, pine nuts, salt, raisins, olive oil, lemon juice\n\nINSTRUCTIONS: \nClean the spinach thoroughly.\nGrill the pine nuts until golden brown, watching carefully so as not to burn.\nBring a pot of salted water to the boil and toss in raisins and spinach.\nDrain as soon as spinach goes limp.\ntoss in olive oil and lemon juice, and scatter with the grilled pine nuts.',
	'NAME: Dried Apricots with Pistachios and Almonds\n\nCATEGORY: Fruit\n\nKEYWORDS: Dried Fruit, Nuts, Low Cholesterol, Healthy, < 30 Mins, Stove Top, Vegan\n\nTOOLS: grill, pot\n\nINGREDIENTS: dried apricots, pistachios, salt, slivered almonds, olive oil, orange juice\n\nINSTRUCTIONS:\nSoak the dried apricots in warm water for 10 minutes to soften them.\nGrill the pistachios until lightly toasted, being careful not to burn them.\nBring a pot of salted water to the boil and add the softened apricots.\nDrain immediately after the apricots plump up slightly.\nToss with olive oil and orange juice, then sprinkle with the grilled pistachios and slivered almonds.',
	'NAME: Smoky Chipotle Turkey Meatloaf\n\nCATEGORY: Meat\n\nKEYWORDS: < 60 Mins, Spicy, Oven, Comfort Food\n\nTOOLS: frying pan, meat thermometer, oven, loaf pan\n\nINGREDIENTS: bacon, yellow onion, green bell pepper, chipotle powder, garlic powder, dried oregano, salt, ground mustard, smoked paprika, chili powder, tomato paste, chicken broth, eggs, ground turkey\n\nINSTRUCTIONS:\nPreheat oven to 425 degrees.\nCook bacon in frying pan, remove, drain, and chop.\nLeave drippings in pan and saute (but do not brown) onion and green pepper.\nAdd chipotle powder, garlic powder, oregano, salt, mustard, smoked paprika, and chili powder.\nCook for 8 minutes.\nRemove pan from heat and add tomato paste and chicken broth.\nMix bread crumbs with eggs and add to ground turkey.\nAdd spice mixture and bacon to turkey mixture and mix gently.\nPlace mixture in two or three 8 x 4 inch individual loaf pans.\nCook until done, about 35 to 45 minutes, or until internal temperature reaches 165 degrees on a meat thermometer.\nLet rest for 10 minutes before slicing.',
	]
	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.
	-->

	<!--
	## 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: 121,408 training samples
	* Columns: <code>sentence_0</code> and <code>sentence_1</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \|
	\|:--------\|:------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 45 tokens</li><li>mean: 185.8 tokens</li><li>max: 384 tokens</li></ul> \| <ul><li>min: 59 tokens</li><li>mean: 222.58 tokens</li><li>max: 384 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>NAME: Homemade Honey Mustard<br><br>CATEGORY: Sauces<br><br>KEYWORDS: Low Protein, < 15 Mins, Easy<br><br>TOOLS: <br><br>INGREDIENTS: Dijon mustard, sour cream, honey, Worcestershire sauce<br><br>INSTRUCTIONS: <br>Mix well, enjoy.</code> \| <code>NAME: Creamy Maple Mustard Sauce<br>CATEGORY: Sauces<br>KEYWORDS: Low Protein, < 15 Mins, Easy, Gluten-Free<br>TOOLS:<br>INGREDIENTS: Whole grain mustard, Greek yogurt, maple syrup, apple cider vinegar<br>INSTRUCTIONS: Combine all ingredients in a bowl and mix until well combined. Refrigerate for at least 10 minutes before serving to allow flavors to meld. Enjoy with pretzels or veggies.</code> \|
	\| <code>NAME: Baby Greens With Hazelnut Parmesan Crisps<br><br>CATEGORY: Greens<br><br>KEYWORDS: Vegetable, High In..., < 30 Mins<br><br>TOOLS: parchment paper, mixer, whisk, oven, baking sheet<br><br>INGREDIENTS: parmesan cheese, hazelnuts, lemon juice, olive oil, maple syrup, lettuce, prosciutto<br><br>INSTRUCTIONS: <br>Preheat oven to 350°F Line a baking sheet with parchment paper.<br>Combine Parmesan and hazelnuts. Drop 12 spoonfuls of Parmesan mixture onto baking sheet 3 inches apart.<br>Bake crisps for 8 to 10 minutes, or until golden. Cool on baking sheet.<br>Whisk together lemon juice, oil and maple syrup. Season with salt and pepper.<br>Toss lettuce with vinaigrette and pile on individual plates.<br>Coil each slice of prosciutto into a rose shape and set a rose in center of each mound of greens. Garnish each serving with two Parmesan crisps.</code> \| <code>NAME: Spinach Salad with Almond Manchego Crisps<br><br>CATEGORY: Greens<br><br>KEYWORDS: Vegetable, High In..., < 30 Mins, Gluten-Free<br><br>TOOLS: parchment paper, mixer, whisk, oven, baking sheet<br><br>INGREDIENTS: manchego cheese, almonds, lime juice, avocado oil, honey, spinach, serrano ham<br><br>INSTRUCTIONS:<br>Preheat oven to 375°F. Line a baking sheet with parchment paper.<br>Combine Manchego cheese and chopped almonds. Drop 12 spoonfuls of the Manchego mixture onto the baking sheet, spacing them 3 inches apart.<br>Bake crisps for 6 to 8 minutes, or until golden brown. Let cool on the baking sheet.<br>Whisk together lime juice, avocado oil, and honey. Season with salt and a pinch of red pepper flakes.<br>Toss spinach with the vinaigrette and arrange on individual plates.<br>Roll each slice of serrano ham into a flower shape and place one in the center of each spinach mound. Garnish each serving with two Manchego crisps.</code> \|
	\| <code>NAME: Classic Delicious New York Cheesecake<br><br>CATEGORY: Cheesecake<br><br>KEYWORDS: Dessert, Weeknight, For Large Groups, < 4 Hours<br><br>TOOLS: pan, mixing bowl, warm oven, mixer, refrigerator<br><br>INGREDIENTS: graham cracker crumbs, cream cheese, eggs, sour cream, butter, sugar, vanilla<br><br>INSTRUCTIONS: <br>Preheat oven to 450 degrees.<br>To make the crust, mix graham crackers crumbs, butter, and 2 tablespoons of sugar in bowl.<br>Press mixture in bottom and sides of 9 inch springform pan.<br>In mixing bowl, beat cream cheese and remaining sugar for 2 minutes.<br>Add eggs and vanilla to mixture and mix until well blended.<br>Then stir or fold in sour cream.<br>Pour mixture in crust filled pan and bake for 10 minutes.<br>Then reduce to 200 degrees to bake for 45 minutes.<br>From here the cheese cake just needs to be chilled, but I recommend doing the following step if you have a few extra hours- Leave in warm oven, once you turn it off but leave door slightly open.<br>Let sit and cool for 2 hours and remove from oven.<br>Remove sides ...</code> \| <code>NAME: Lemon Ricotta Cheesecake Delight<br><br>CATEGORY: Cheesecake<br><br>KEYWORDS: Dessert, Weeknight, For Large Groups, < 4 Hours, Citrus<br><br>TOOLS: pan, mixing bowl, warm oven, mixer, refrigerator, zester<br><br>INGREDIENTS: gluten-free graham cracker crumbs, ricotta cheese, eggs, Greek yogurt, butter, sugar, vanilla extract, lemon zest, lemon juice<br><br>INSTRUCTIONS:<br>Preheat oven to 450 degrees Fahrenheit.<br>To make the crust, mix gluten-free graham cracker crumbs, melted butter, and 2 tablespoons of sugar in bowl.<br>Press mixture firmly in bottom and partially up the sides of a 9 inch springform pan.<br>In a large mixing bowl, beat ricotta cheese and remaining sugar for 3 minutes until light and fluffy.<br>Add eggs, vanilla extract, lemon zest, and lemon juice to mixture; mix until just combined. Avoid overmixing.<br>Gently fold in Greek yogurt.<br>Pour mixture into the prepared crust-lined pan and bake for 12 minutes.<br>Reduce oven temperature to 225 degrees Fahrenheit and continue baking for 40 minutes, or until the edge...</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `fp16`: True
	- `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`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 3
	- `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`: True
	- `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
	- `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
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 0.2636 \| 500 \| 0.0583 \|
	\| 0.5271 \| 1000 \| 0.0017 \|
	\| 0.7907 \| 1500 \| 0.001 \|
	\| 1.0543 \| 2000 \| 0.0008 \|
	\| 1.3179 \| 2500 \| 0.0005 \|
	\| 1.5814 \| 3000 \| 0.0006 \|
	\| 1.8450 \| 3500 \| 0.0004 \|
	\| 2.1086 \| 4000 \| 0.0005 \|
	\| 2.3722 \| 4500 \| 0.0003 \|
	\| 2.6357 \| 5000 \| 0.0003 \|
	\| 2.8993 \| 5500 \| 0.0003 \|


	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 4.0.1
	- Transformers: 4.50.2
	- PyTorch: 2.4.0
	- Accelerate: 1.5.2
	- Datasets: 3.5.0
	- Tokenizers: 0.21.1

	## 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",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@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}
	}
	```

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