Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,445 @@
+---
+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]
+```
+
+<!--
+### 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: 118.85 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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/distilbert-base-nli-mean-tokens",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertModel"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:cbd41238e2f19b61122e3fabb102a917b3d9ba6ec38217fb1ee2d0baa48ee840
+size 265462608

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 128,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}