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_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]

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: 118.85 tokens max: 128 tokens	min: 63 tokens mean: 117.66 tokens max: 128 tokens	min: 0.0 mean: 0.19 max: 1.0

Samples:

sentence_0	sentence_1	label
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."]	`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."]`	`0.5`
`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."]`	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."]	`0.1`
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."]	`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."]`	`0.6`

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: 5
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: 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

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

@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",
}

DivyaMereddy007
/

RecipeBert_v5originalCopy_of_TrainSetenceTransforme-Finetuning_v5_DistilledBert