README.md

metadata

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 model finetuned from 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
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("DivyaMereddy007/RecipeBert_v5original_epoc50Copy_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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,746 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	min: 63 tokens mean: 118.82 tokens max: 128 tokens	min: 63 tokens mean: 118.59 tokens max: 128 tokens	min: 0.0 mean: 0.19 max: 1.0

• Samples:

  sentence_0	sentence_1	label
  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."]	Easy Fudge ["1 (14 oz.) can sweetened condensed milk", "1 (12 oz.) pkg. semi-sweet chocolate chips", "1 (1 oz.) sq. unsweetened chocolate (if desired)", "1 1/2 c. chopped nuts (if desired)", "1 tsp. vanilla"] ["Butter a square pan, 8 x 8 x 2-inches.", "Heat milk, chocolate chips and unsweetened chocolate over low heat, stirring constantly, until chocolate is melted and mixture is smooth. Remove from heat.", "Stir in nuts and vanilla.", "Spread in pan."]	0.05
  Scalloped Corn ["1 can cream-style corn", "1 can whole kernel corn", "1/2 pkg. (approximately 20) saltine crackers, crushed", "1 egg, beaten", "6 tsp. butter, divided", "pepper to taste"] ["Mix together both cans of corn, crackers, egg, 2 teaspoons of melted butter and pepper and place in a buttered baking dish.", "Dot with remaining 4 teaspoons of butter.", "Bake at 350\u00b0 for 1 hour."]	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."]	0.1
  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."]	Rhubarb Coffee Cake ["1 1/2 c. sugar", "1/2 c. butter", "1 egg", "1 c. buttermilk", "2 c. flour", "1/2 tsp. salt", "1 tsp. soda", "1 c. buttermilk", "2 c. rhubarb, finely cut", "1 tsp. vanilla"] ["Cream sugar and butter.", "Add egg and beat well.", "To creamed butter, sugar and egg, add alternately buttermilk with mixture of flour, salt and soda.", "Mix well.", "Add rhubarb and vanilla.", "Pour into greased 9 x 13-inch pan and add Topping."]	0.4

• Loss: CosineSimilarityLoss with these parameters:

  {
      "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: 50
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 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: 50
• 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

Training Logs

Epoch	Step	Training Loss
4.5455	500	0.0092
9.0909	1000	0.0091
13.6364	1500	0.0081
18.1818	2000	0.0074
22.7273	2500	0.0071
27.2727	3000	0.0069
31.8182	3500	0.0066
36.3636	4000	0.0065
40.9091	4500	0.0061
45.4545	5000	0.006
50.0	5500	0.0056

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

@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",
}