File size: 26,676 Bytes
a5dd439 df33303 a5dd439 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 |
---
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_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]
```
<!--
### 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: 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: 118.82 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 63 tokens</li><li>mean: 118.59 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>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>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."]</code> | <code>0.05</code> |
| <code>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."]</code> | <code>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."]</code> | <code>0.1</code> |
| <code>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."]</code> | <code>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."]</code> | <code>0.4</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`: 50
- `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`: 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
</details>
### 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
```bibtex
@INPROCEEDINGS{10973364,
author={Mereddy, Divya and Beedareddy, Jeevan Sai Reddy},
booktitle={2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)},
title={Enabling Next-Generation Smart Homes Through Bert Personalized Food Recommendations - RecipeBERT},
year={2024},
volume={},
number={},
pages={796-803},
keywords={Adaptation models;Analytical models;Reviews;Semantics;Smart homes;Manuals;Transformers;Recommender systems;Next generation networking;Testing;Personalized food recommendations;BERT;SBERT;RecipeBERT},
doi={10.1109/WI-IAT62293.2024.00130}}
@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",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |