metadata

language: []
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dataset_size:1K<n<10K
  - loss:CosineSimilarityLoss
base_model: distilbert/distilroberta-base
widget:
  - source_sentence: >-
      Herb Butter ["2 Tbsp. dried herbs: equal parts of parsley, tarragon,
      chives and/or basil", "1/2 c. margarine"] ["Blend all together and chill
      overnight."]
    sentences:
      - >-
        Salad Dressing ["2 Tbsp. lemon juice or wine vinegar", "1 Tbsp. honey",
        "1 clove garlic, diced", "1 Tbsp. rosemary", "2 Tbsp. water", "1 small
        diced onion", "1 Tbsp. flax seed", "1 tsp. parsley"] ["Place in blender
        until smooth."]
      - >-
        Fried Sweet Potato Strips ["1 large sweet potato, peeled and grated into
        long strips", "1 c. vegetable oil"] ["Fry potato in hot oil in a small
        skillet until lightly browned (watch carefully, they brown quickly).",
        "Remove with a slotted spoon and drain on paper towels.", "(Strips will
        be crisp when cooled.)", "Yield:", "about 1 cup."]
      - >-
        Chocolate Chip Pecan Pie ["1/2 c. semi-sweet chocolate chips", "4 eggs",
        "1/3 c. granulated sugar", "1 1/4 c. Karo syrup (lite or dark)", "3
        Tbsp. melted butter", "1 1/2 tsp. vanilla", "3/4 c. chopped pecans"]
        ["Beat eggs; add sugar, corn syrup and vanilla.", "Mix well.", "Stir in
        nuts and chips.", "Pour into 9-inch unbaked pie shell. Bake at 325\u00b0
        to 350\u00b0", "for 25 minutes.", "Yields 1 pie."]
  - source_sentence: >-
      Snicker Bars ["1 c. milk chocolate chips", "1/4 c. butterscotch chips",
      "1/4 c. peanut butter"] ["Melt together; pour into 9 x 13-inch greased pan
      and cool."]
    sentences:
      - >-
        Reeses Cups(Candy)   ["1 c. peanut butter", "3/4 c. graham cracker
        crumbs", "1 c. melted butter", "1 lb. (3 1/2 c.) powdered sugar", "1
        large pkg. chocolate chips"] ["Combine first four ingredients and press
        in 13 x 9-inch ungreased pan.", "Melt chocolate chips and spread over
        mixture. Refrigerate for about 20 minutes and cut into pieces before
        chocolate gets hard.", "Keep in refrigerator."]
      - >-
        Heavenly Potatoes ["1 (24 oz.) pkg. frozen hash browns, thaw to use", "1
        3/4 c. grated Cheddar cheese", "1 can cream of chicken soup", "8 oz.
        carton sour cream", "1 stick melted butter or margarine", "1 tsp. salt",
        "1 medium onion, chopped"] ["Mix all together and place in a casserole
        dish.", "Bake 45 minutes to 1 hour at 350\u00b0.", "Serves 12."]
      - >-
        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."]
  - source_sentence: >-
      Foil Packs ["boneless pork chops (or other meat)", "potatoes, quartered",
      "carrots, quartered", "onions, quartered"] ["You will also need 1 large
      piece of aluminum foil."]
    sentences:
      - >-
        Pork Sausage ["12 lb. pork meat, cut in pieces, ready for grinding", "5
        Tbsp. salt", "3 Tbsp. black pepper", "2 Tbsp. pulverized sage leaves"]
        ["Sprinkle meat well with the remaining ingredients.", "Grind all
        together and it will need no further mixing."]
      - >-
        Shepherd'S Pie ["1 lb. hamburg", "1/4 c. chopped onion", "1/4 tsp.
        salt", "1/8 tsp. pepper", "1 c. mashed potatoes"] ["Fry hamburg and
        onion until brown.", "Drain off liquid.", "Add salt and pepper.", "Spoon
        into 1-quart casserole and place potatoes on top.", "Put butter and
        paprika over potatoes.", "Bake in a 425\u00b0 oven for 15 minutes."]
      - >-
        Homemade Vanilla Ice Cream ["4 eggs", "2 c. sugar", "4 c. milk", "1 can
        Eagle Brand milk", "2 Tbsp. vanilla", "1/2 tsp. salt"] ["Mix milk, Eagle
        Brand, vanilla and salt in small mixing bowl. In large mixing bowl, beat
        eggs until light; add sugar gradually beating constantly.", "Beat in
        mixture from small bowl.", "Pour into freezer and freeze according to
        freezer directions."]
  - source_sentence: >-
      Orange Julius ["couple of oranges", "2 Tbsp. honey"] ["Put in blender.",
      "Add crushed ice until desired thickness.", "Add enough milk to fill
      blender, approximately 1 cup."]
    sentences:
      - >-
        Ambrosia ["8 to 10 juicy oranges, peeled and diced", "1 c. moist
        coconut", "1/2 c. pecans, chopped", "1/2 c. cherries, halved", "1/4 c.
        sugar", "1 c. orange juice"] ["Combine all ingredients. Chill
        overnight.", "Yields 4 to 6 servings."]
      - >-
        Toffee Refrigerator Dessert ["1 1/2 c. graham cracker crumbs, finely
        crushed", "1/2 c. soda cracker crumbs", "1/2 c. oleo, melted", "2 pkg.
        vanilla instant pudding", "2 c. milk", "1 qt. vanilla ice cream,
        softened", "1 (4 1/2 oz.) tub Cool Whip", "2 Butterfinger candy bars,
        crushed"] ["Mix the first 3 ingredients and pat into a 9 x 13-inch
        dish."]
      - >-
        Mediterranean Orzo ["1 1/2 c. orzo pasta", "1 Tbsp. olive oil", "3 Tbsp.
        sun-dried tomato paste", "1 Tbsp. white balsamic vinegar"] ["Cook orzo
        according to directions. Drain. Add remaining ingredients."]
  - source_sentence: >-
      Sour Cream Coconut Cake ["2 c. sugar", "2 (8 oz.) carton sour cream", "2
      pkg. frozen coconut", "1 (3-layer) cake, baked"] ["Bake cake; split the 3
      layers into 6 layers."]
    sentences:
      - >-
        Milk Chocolate Bar Cake ["1 (18 oz.) pkg. Swiss chocolate cake mix", "1
        (8 oz.) pkg. cream cheese, softened", "1 c. powdered sugar", "1/2 c.
        granulated sugar", "10 (15 oz.) milk chocolate candy bars with almonds,
        divided", "1 (12 oz.) carton thawed Cool Whip"] ["Prepare cake batter
        according to directions on box.", "Pour into 2 greased and floured
        8-inch round cake pans.", "Bake at 325\u00b0 for 20 to 25 minutes.",
        "Cool and divide to make 4 layers."]
      - >-
        Chili Sauce ["12 ripe tomatoes", "4 onions", "2 green peppers", "1 red
        pepper", "4 Tbsp. sugar", "2 Tbsp. salt", "2 tsp. cinnamon", "2 tsp.
        cloves", "2 tsp. allspice", "1 tsp. ginger", "1 qt. vinegar"] ["Peel
        onions and tomatoes, seed peppers and chop all fine, add the spices and
        put over the fire. Boil steadily for two hours; cool, bottle and seal."]
      - >-
        Creamed Onions(Makes 8 Servings)   ["4 c. small white onions, peeled (1
        1/2 lb.)", "2 Tbsp. plus 2 tsp. reduced calorie margarine", "1 1/2 Tbsp.
        all-purpose flour", "1 c. skim milk", "1/2 tsp. thyme", "1/4 tsp. salt",
        "pinch of nutmeg", "pinch of ground white pepper"] ["In a medium
        saucepan of boiling water, cook onion for 15 to 20 minutes, until
        tender.", "Drain."]
pipeline_tag: sentence-similarity

SentenceTransformer based on distilbert/distilroberta-base

This is a sentence-transformers model finetuned from distilbert/distilroberta-base. 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: distilbert/distilroberta-base
Maximum Sequence Length: 512 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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("jeevansai93/Jeevan_cv_run2_roberta_5_epoc")
# Run inference
sentences = [
    'Sour Cream Coconut Cake ["2 c. sugar", "2 (8 oz.) carton sour cream", "2 pkg. frozen coconut", "1 (3-layer) cake, baked"] ["Bake cake; split the 3 layers into 6 layers."]',
    'Milk Chocolate Bar Cake ["1 (18 oz.) pkg. Swiss chocolate cake mix", "1 (8 oz.) pkg. cream cheese, softened", "1 c. powdered sugar", "1/2 c. granulated sugar", "10 (15 oz.) milk chocolate candy bars with almonds, divided", "1 (12 oz.) carton thawed Cool Whip"] ["Prepare cake batter according to directions on box.", "Pour into 2 greased and floured 8-inch round cake pans.", "Bake at 325\\u00b0 for 20 to 25 minutes.", "Cool and divide to make 4 layers."]',
    'Chili Sauce ["12 ripe tomatoes", "4 onions", "2 green peppers", "1 red pepper", "4 Tbsp. sugar", "2 Tbsp. salt", "2 tsp. cinnamon", "2 tsp. cloves", "2 tsp. allspice", "1 tsp. ginger", "1 qt. vinegar"] ["Peel onions and tomatoes, seed peppers and chop all fine, add the spices and put over the fire. Boil steadily for two hours; cool, bottle and seal."]',
]
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: 4,149 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: 42 tokens mean: 136.35 tokens max: 326 tokens	min: 34 tokens mean: 137.99 tokens max: 358 tokens	min: 0.0 mean: 0.24 max: 1.0

Samples:

sentence_0	sentence_1	label
`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."]`	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."]	`0.15000000000000002`
`Grandma Mary'S Butter Cookies ["1 c. sweet butter", "1 c. granulated sugar", "3 egg yolks", "2 1/2 c. sifted flour", "1 tsp. vanilla"] ["Cream butter.", "Beat into sugar.", "Add egg yolks and vanilla. Beat well after adding each yolk.", "Add flour and beat after each 1/2 cup is added.", "Chill about 1 hour."]`	Magic Cookie Bars ["1/2 c. butter", "1 1/2 c. graham cracker crumbs", "1 (14 oz.) can Eagle Brand milk", "6 oz. semi-sweet chocolate chips", "1 (3 1/2 oz.) can flaked coconut (1 1/2 c.)", "1 c. chopped nuts"] ["Preheat oven to 350\u00b0 (325\u00b0 for glass dish).", "In 13 x 9-inch pan, melt butter in oven.", "Sprinkle with crumbs.", "Top with Eagle Brand milk evenly.", "Top with remaining ingredients.", "Press down. Bake 25 to 30 minutes until lightly brown.", "Cool or chill.", "Cut into bars; store, loosely covered, at room temperature."]	`0.65`
`Angel Biscuits ["5 c. flour", "3 Tbsp. sugar", "4 tsp. baking powder", "1 1/2 pkg. dry yeast", "2 c. buttermilk", "1 tsp. soda", "1 1/2 sticks margarine", "1/2 c. warm water"] ["Mix flour, sugar, baking powder, soda and salt together.", "Cut in margarine, dissolve yeast in warm water.", "Stir into buttermilk and add to dry mixture.", "Cover and chill."]`	Mexican Cookie Rings ["1 1/2 c. sifted flour", "1/2 tsp. baking powder", "1/2 tsp. salt", "1/2 c. butter", "2/3 c. sugar", "3 egg yolks", "1 tsp. vanilla", "multi-colored candies"] ["Sift flour, baking powder and salt together.", "Cream together butter and sugar.", "Add egg yolks and vanilla.", "Beat until light and fluffy.", "Mix in sifted dry ingredients.", "Shape into 1-inch balls.", "Push wooden spoon handle through center (twist).", "Shape into rings.", "Dip each cookie into candies.", "Place on lightly greased baking sheets.", "Bake in 375\u00b0 oven for 10 to 12 minutes or until golden brown.", "Cool on racks.", "Serves 2 dozen."]	`0.1`

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: 1
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: 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: 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

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.0
Transformers: 4.41.1
PyTorch: 2.3.0+cu121
Accelerate: 0.30.0
Datasets: 2.19.1
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",
}