Add new SentenceTransformer model.

c0282d8 verified 7 months ago

30.7 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1746
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/distilbert-base-nli-mean-tokens
	datasets: []
	widget:
	- source_sentence: Cheeseburger Potato Soup ["6 baking potatoes", "1 lb. of extra
	lean ground beef", "2/3 c. butter or margarine", "6 c. milk", "3/4 tsp. salt",
	"1/2 tsp. pepper", "1 1/2 c (6 oz.) shredded Cheddar cheese, divided", "12 sliced
	bacon, cooked, crumbled and divided", "4 green onion, chopped and divided", "1
	(8 oz.) carton sour cream (optional)"] ["Wash potatoes; prick several times with
	a fork.", "Microwave them with a wet paper towel covering the potatoes on high
	for 6-8 minutes.", "The potatoes should be soft, ready to eat.", "Let them cool
	enough to handle.", "Cut in half lengthwise; scoop out pulp and reserve.", "Discard
	shells.", "Brown ground beef until done.", "Drain any grease from the meat.",
	"Set aside when done.", "Meat will be added later.", "Melt butter in a large kettle
	over low heat; add flour, stirring until smooth.", "Cook 1 minute, stirring constantly.
	Gradually add milk; cook over medium heat, stirring constantly, until thickened
	and bubbly.", "Stir in potato, ground beef, salt, pepper, 1 cup of cheese, 2 tablespoons
	of green onion and 1/2 cup of bacon.", "Cook until heated (do not boil).", "Stir
	in sour cream if desired; cook until heated (do not boil).", "Sprinkle with remaining
	cheese, bacon and green onions."]
	sentences:
	- Nolan'S Pepper Steak ["1 1/2 lb. round steak (1-inch thick), cut into strips",
	"1 can drained tomatoes, cut up (save liquid)", "1 3/4 c. water", "1/2 c. onions",
	"1 1/2 Tbsp. Worcestershire sauce", "2 green peppers, diced", "1/4 c. oil"] ["Roll
	steak strips in flour.", "Brown in skillet.", "Salt and pepper.", "Combine tomato
	liquid, water, onions and browned steak. Cover and simmer for one and a quarter
	hours.", "Uncover and stir in Worcestershire sauce.", "Add tomatoes, green peppers
	and simmer for 5 minutes.", "Serve over hot cooked rice."]
	- Fresh Strawberry Pie ["1 baked pie shell", "1 qt. cleaned strawberries", "1 1/2
	c. water", "4 Tbsp. cornstarch", "1 c. sugar", "1/8 tsp. salt", "4 Tbsp. strawberry
	jello"] ["Mix water, cornstarch, sugar and salt in saucepan.", "Stir constantly
	and boil until thick and clear.", "Remove from heat and stir in jello.", "Set
	aside to cool.", "But don't allow it to set. Layer strawberries in baked crust.",
	"Pour cooled glaze over. Continue layering berries and glaze.", "Refrigerate.",
	"Serve with whipped cream."]
	- Vegetable-Burger Soup ["1/2 lb. ground beef", "2 c. water", "1 tsp. sugar", "1
	pkg. Cup-a-Soup onion soup mix (dry)", "1 lb. can stewed tomatoes", "1 (8 oz.)
	can tomato sauce", "1 (10 oz.) pkg. frozen mixed vegetables"] ["Lightly brown
	beef in soup pot.", "Drain off excess fat.", "Stir in tomatoes, tomato sauce,
	water, frozen vegetables, soup mix and sugar.", "Bring to a boil.", "Reduce heat
	and simmer for 20 minutes. Serve."]
	- source_sentence: Summer Spaghetti ["1 lb. very thin spaghetti", "1/2 bottle McCormick
	Salad Supreme (seasoning)", "1 bottle Zesty Italian dressing"] ["Prepare spaghetti
	per package.", "Drain.", "Melt a little butter through it.", "Marinate overnight
	in Salad Supreme and Zesty Italian dressing.", "Just before serving, add cucumbers,
	tomatoes, green peppers, mushrooms, olives or whatever your taste may want."]
	sentences:
	- Prize-Winning Meat Loaf ["1 1/2 lb. ground beef", "1 c. tomato juice", "3/4 c.
	oats (uncooked)", "1 egg, beaten", "1/4 c. chopped onion", "1/4 tsp. pepper",
	"1 1/2 tsp. salt"] ["Mix well.", "Press firmly into an 8 1/2 x 4 1/2 x 2 1/2-inch
	loaf pan.", "Bake in preheated moderate oven.", "Bake at 350\u00b0 for 1 hour.",
	"Let stand 5 minutes before slicing.", "Makes 8 servings."]
	- Cuddy Farms Marinated Turkey ["2 c. 7-Up or Sprite", "1 c. vegetable oil", "1
	c. Kikkoman soy sauce", "garlic salt"] ["Buy whole turkey breast; remove all skin
	and bones. Cut into pieces about the size of your hand. Pour marinade over turkey
	and refrigerate for at least 8 hours (up to 48 hours). The longer it marinates,
	the less cooking time it takes."]
	- Pear-Lime Salad ["1 (16 oz.) can pear halves, undrained", "1 (3 oz.) pkg. lime
	gelatin", "1 (8 oz.) pkg. cream cheese, softened", "1 (8 oz.) carton lemon yogurt"]
	["Drain pears, reserving juice.", "Bring juice to a boil, stirring constantly.",
	"Remove from heat.", "Add gelatin, stirring until dissolved.", "Let cool slightly.",
	"Coarsely chop pear halves. Combine cream cheese and yogurt; beat at medium speed
	of electric mixer until smooth.", "Add gelatin and beat well.", "Stir in pears.",
	"Pour into an oiled 4-cup mold or Pyrex dish.", "Chill."]
	- source_sentence: Millionaire Pie ["1 large container Cool Whip", "1 large can crushed
	pineapple", "1 can condensed milk", "3 lemons", "1 c. pecans", "2 graham cracker
	crusts"] ["Empty Cool Whip into a bowl.", "Drain juice from pineapple.", "Mix
	Cool Whip and pineapple.", "Add condensed milk.", "Squeeze lemons, remove seeds
	and add to Cool Whip and pineapple.", "Chop nuts into small pieces and add to
	mixture.", "Stir all ingredients together and mix well.", "Pour into a graham
	cracker crust.", "Use top from crust to cover top of pie.", "Chill overnight.",
	"Makes 2 pies."]
	sentences:
	- Jewell Ball'S Chicken ["1 small jar chipped beef, cut up", "4 boned chicken breasts",
	"1 can cream of mushroom soup", "1 carton sour cream"] ["Place chipped beef on
	bottom of baking dish.", "Place chicken on top of beef.", "Mix soup and cream
	together; pour over chicken. Bake, uncovered, at 275\u00b0 for 3 hours."]
	- Quick Peppermint Puffs ["8 marshmallows", "2 Tbsp. margarine, melted", "1/4 c.
	crushed peppermint candy", "1 can crescent rolls"] ["Dip marshmallows in melted
	margarine; roll in candy. Wrap a crescent triangle around each marshmallow, completely
	covering the marshmallow and square edges of dough tightly to seal.", "Dip in
	margarine and place in a greased muffin tin.", "Bake at 375\u00b0 for 10 to 15
	minutes; remove from pan."]
	- Double Cherry Delight ["1 (17 oz.) can dark sweet pitted cherries", "1/2 c. ginger
	ale", "1 (6 oz.) pkg. Jell-O cherry flavor gelatin", "2 c. boiling water", "1/8
	tsp. almond extract", "1 c. miniature marshmallows"] ["Drain cherries, measuring
	syrup.", "Cut cherries in half.", "Add ginger ale and enough water to syrup to
	make 1 1/2 cups.", "Dissolve gelatin in boiling water.", "Add measured liquid
	and almond extract. Chill until very thick.", "Fold in marshmallows and the cherries.
	Spoon into 6-cup mold.", "Chill until firm, at least 4 hours or overnight.", "Unmold.",
	"Makes about 5 1/3 cups."]
	- source_sentence: Prize-Winning Meat Loaf ["1 1/2 lb. ground beef", "1 c. tomato
	juice", "3/4 c. oats (uncooked)", "1 egg, beaten", "1/4 c. chopped onion", "1/4
	tsp. pepper", "1 1/2 tsp. salt"] ["Mix well.", "Press firmly into an 8 1/2 x 4
	1/2 x 2 1/2-inch loaf pan.", "Bake in preheated moderate oven.", "Bake at 350\u00b0
	for 1 hour.", "Let stand 5 minutes before slicing.", "Makes 8 servings."]
	sentences:
	- Beer Bread ["3 c. self rising flour", "1 - 12 oz. can beer", "1 Tbsp. sugar"]
	["Stir the ingredients together and put in a greased and floured loaf pan.", "Bake
	at 425 degrees for 50 minutes.", "Drizzle melted butter on top."]
	- Artichoke Dip ["2 cans or jars artichoke hearts", "1 c. mayonnaise", "1 c. Parmesan
	cheese"] ["Drain artichokes and chop.", "Mix with mayonnaise and Parmesan cheese.",
	"After well mixed, bake, uncovered, for 20 to 30 minutes at 350\u00b0.", "Serve
	with crackers."]
	- 'One Hour Rolls ["1 c. milk", "2 Tbsp. sugar", "1 pkg. dry yeast", "1 Tbsp. salt",
	"3 Tbsp. Crisco oil", "2 c. plain flour"] ["Put flour into a large mixing bowl.",
	"Combine sugar, milk, salt and oil in a saucepan and heat to boiling; remove from
	heat and let cool to lukewarm.", "Add yeast and mix well.", "Pour into flour and
	stir.", "Batter will be sticky.", "Roll out batter on a floured board and cut
	with biscuit cutter.", "Lightly brush tops with melted oleo and fold over.", "Place
	rolls on a cookie sheet, put in a warm place and let rise for 1 hour.", "Bake
	at 350\u00b0 for about 20 minutes. Yield: 2 1/2 dozen."]'
	- source_sentence: Watermelon Rind Pickles ["7 lb. watermelon rind", "7 c. sugar",
	"2 c. apple vinegar", "1/2 tsp. oil of cloves", "1/2 tsp. oil of cinnamon"] ["Trim
	off green and pink parts of watermelon rind; cut to 1-inch cubes.", "Parboil until
	tender, but not soft.", "Drain. Combine sugar, vinegar, oil of cloves and oil
	of cinnamon; bring to boiling and pour over rind.", "Let stand overnight.", "In
	the morning, drain off syrup.", "Heat and put over rind.", "The third morning,
	heat rind and syrup; seal in hot, sterilized jars.", "Makes 8 pints.", "(Oil of
	cinnamon and clove keeps rind clear and transparent.)"]
	sentences:
	- Summer Chicken ["1 pkg. chicken cutlets", "1/2 c. oil", "1/3 c. red vinegar",
	"2 Tbsp. oregano", "2 Tbsp. garlic salt"] ["Double recipe for more chicken."]
	- Summer Spaghetti ["1 lb. very thin spaghetti", "1/2 bottle McCormick Salad Supreme
	(seasoning)", "1 bottle Zesty Italian dressing"] ["Prepare spaghetti per package.",
	"Drain.", "Melt a little butter through it.", "Marinate overnight in Salad Supreme
	and Zesty Italian dressing.", "Just before serving, add cucumbers, tomatoes, green
	peppers, mushrooms, olives or whatever your taste may want."]
	- Chicken Funny ["1 large whole chicken", "2 (10 1/2 oz.) cans chicken gravy", "1
	(10 1/2 oz.) can cream of mushroom soup", "1 (6 oz.) box Stove Top stuffing",
	"4 oz. shredded cheese"] ["Boil and debone chicken.", "Put bite size pieces in
	average size square casserole dish.", "Pour gravy and cream of mushroom soup over
	chicken; level.", "Make stuffing according to instructions on box (do not make
	too moist).", "Put stuffing on top of chicken and gravy; level.", "Sprinkle shredded
	cheese on top and bake at 350\u00b0 for approximately 20 minutes or until golden
	and bubbly."]
	pipeline_tag: sentence-similarity
	---

	# SentenceTransformer based on sentence-transformers/distilbert-base-nli-mean-tokens

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/distilbert-base-nli-mean-tokens](https://huggingface.co/sentence-transformers/distilbert-base-nli-mean-tokens). 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/distilbert-base-nli-mean-tokens](https://huggingface.co/sentence-transformers/distilbert-base-nli-mean-tokens) <!-- at revision 2781c006adbf3726b509caa8649fc8077ff0724d -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 tokens
	- 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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
	(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})
	)
	```

	## 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("DivyaMereddy007/RecipeBert_v5originalCopy_of_TrainSetenceTransforme-Finetuning_v5_DistilledBert")
	# Run inference
	sentences = [
	'Watermelon Rind Pickles ["7 lb. watermelon rind", "7 c. sugar", "2 c. apple vinegar", "1/2 tsp. oil of cloves", "1/2 tsp. oil of cinnamon"] ["Trim off green and pink parts of watermelon rind; cut to 1-inch cubes.", "Parboil until tender, but not soft.", "Drain. Combine sugar, vinegar, oil of cloves and oil of cinnamon; bring to boiling and pour over rind.", "Let stand overnight.", "In the morning, drain off syrup.", "Heat and put over rind.", "The third morning, heat rind and syrup; seal in hot, sterilized jars.", "Makes 8 pints.", "(Oil of cinnamon and clove keeps rind clear and transparent.)"]',
	'Summer Chicken ["1 pkg. chicken cutlets", "1/2 c. oil", "1/3 c. red vinegar", "2 Tbsp. oregano", "2 Tbsp. garlic salt"] ["Double recipe for more chicken."]',
	'Summer Spaghetti ["1 lb. very thin spaghetti", "1/2 bottle McCormick Salad Supreme (seasoning)", "1 bottle Zesty Italian dressing"] ["Prepare spaghetti per package.", "Drain.", "Melt a little butter through it.", "Marinate overnight in Salad Supreme and Zesty Italian dressing.", "Just before serving, add cucumbers, tomatoes, green peppers, mushrooms, olives or whatever your taste may want."]',
	]
	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]
	```

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	<details><summary>Click to expand</summary>

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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 1,746 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: 63 tokens</li><li>mean: 118.85 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 63 tokens</li><li>mean: 117.66 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.19</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Cheeseburger Potato Soup ["6 baking potatoes", "1 lb. of extra lean ground beef", "2/3 c. butter or margarine", "6 c. milk", "3/4 tsp. salt", "1/2 tsp. pepper", "1 1/2 c (6 oz.) shredded Cheddar cheese, divided", "12 sliced bacon, cooked, crumbled and divided", "4 green onion, chopped and divided", "1 (8 oz.) carton sour cream (optional)"] ["Wash potatoes; prick several times with a fork.", "Microwave them with a wet paper towel covering the potatoes on high for 6-8 minutes.", "The potatoes should be soft, ready to eat.", "Let them cool enough to handle.", "Cut in half lengthwise; scoop out pulp and reserve.", "Discard shells.", "Brown ground beef until done.", "Drain any grease from the meat.", "Set aside when done.", "Meat will be added later.", "Melt butter in a large kettle over low heat; add flour, stirring until smooth.", "Cook 1 minute, stirring constantly. Gradually add milk; cook over medium heat, stirring constantly, until thickened and bubbly.", "Stir in potato, ground beef, salt, pepper, 1 cup of cheese, 2 tablespoons of green onion and 1/2 cup of bacon.", "Cook until heated (do not boil).", "Stir in sour cream if desired; cook until heated (do not boil).", "Sprinkle with remaining cheese, bacon and green onions."]</code> \| <code>Quick Barbecue Wings ["chicken wings (as many as you need for dinner)", "flour", "barbecue sauce (your choice)"] ["Clean wings.", "Flour and fry until done.", "Place fried chicken wings in microwave bowl.", "Stir in barbecue sauce.", "Microwave on High (stir once) for 4 minutes."]</code> \| <code>0.5</code> \|
	\| <code>Broccoli Dip For Crackers ["16 oz. sour cream", "1 pkg. dry vegetable soup mix", "10 oz. pkg. frozen chopped broccoli, thawed and drained", "4 to 6 oz. Cheddar cheese, grated"] ["Mix together sour cream, soup mix, broccoli and half of cheese.", "Sprinkle remaining cheese on top.", "Bake at 350\u00b0 for 30 minutes, uncovered.", "Serve hot with vegetable crackers."]</code> \| <code>Spaghetti Sauce To Can ["1/2 bushel tomatoes", "1 c. oil", "1/4 c. minced garlic", "6 cans tomato paste", "3 peppers (2 sweet and 1 hot)", "1 1/2 c. sugar", "1/2 c. salt", "1 Tbsp. sweet basil", "2 Tbsp. oregano", "1 tsp. Italian seasoning"] ["Cook ground or chopped peppers and onions in oil for 1/2 hour. Cook tomatoes and garlic as for juice.", "Put through the mill.", "(I use a food processor and do my tomatoes uncooked.", "I then add the garlic right to the juice.)", "Add peppers and onions to juice and remainder of ingredients.", "Cook approximately 1 hour.", "Put in jars and seal.", "Yields 7 quarts."]</code> \| <code>0.1</code> \|
	\| <code>Cheeseburger Potato Soup ["6 baking potatoes", "1 lb. of extra lean ground beef", "2/3 c. butter or margarine", "6 c. milk", "3/4 tsp. salt", "1/2 tsp. pepper", "1 1/2 c (6 oz.) shredded Cheddar cheese, divided", "12 sliced bacon, cooked, crumbled and divided", "4 green onion, chopped and divided", "1 (8 oz.) carton sour cream (optional)"] ["Wash potatoes; prick several times with a fork.", "Microwave them with a wet paper towel covering the potatoes on high for 6-8 minutes.", "The potatoes should be soft, ready to eat.", "Let them cool enough to handle.", "Cut in half lengthwise; scoop out pulp and reserve.", "Discard shells.", "Brown ground beef until done.", "Drain any grease from the meat.", "Set aside when done.", "Meat will be added later.", "Melt butter in a large kettle over low heat; add flour, stirring until smooth.", "Cook 1 minute, stirring constantly. Gradually add milk; cook over medium heat, stirring constantly, until thickened and bubbly.", "Stir in potato, ground beef, salt, pepper, 1 cup of cheese, 2 tablespoons of green onion and 1/2 cup of bacon.", "Cook until heated (do not boil).", "Stir in sour cream if desired; cook until heated (do not boil).", "Sprinkle with remaining cheese, bacon and green onions."]</code> \| <code>Tuna Macaroni Casserole ["1 box macaroni and cheese", "1 can tuna, drained", "1 small jar pimentos", "1 medium onion, chopped"] ["Prepare macaroni and cheese as directed.", "Add drained tuna, pimento and onion.", "Mix.", "Serve hot or cold."]</code> \| <code>0.6</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`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 5
	- `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`: 16
	- `per_device_eval_batch_size`: 16
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_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`: 5
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:------:\|:----:\|:-------------:\|
	\| 4.5455 \| 500 \| 0.0279 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.1
	- Transformers: 4.41.2
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.19.2
	- Tokenizers: 0.19.1

	## 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",
	}
	```

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