|
--- |
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datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
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- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:42333 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: 'Tag: chicken & broccoli alfredo |
|
|
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|
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For chicken & broccoli alfredo, these dietary tags go well with it: dinner, italian |
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cuisine, meat recipes, lunch, italian american cuisine, american cuisine, pasta |
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recipes, contains dairy, european cuisine, vegetarian' |
|
sentences: |
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- 'Tag: chicken & broccoli alfredo |
|
|
|
|
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What dietary classifications are suitable for chicken & broccoli alfredo?' |
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- 'Tag: vegan pad thai |
|
|
|
|
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What dietary labels fit vegan pad thai?' |
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- 'Tag: apple pie filling |
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|
|
|
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Which dietary tags apply to apple pie filling?' |
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- source_sentence: 'Tag: beef and broccoli |
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|
|
|
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A small description of beef and broccoli: Stir fried broccoli and tender beef |
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strips stir-fried in a rich savory sauce.' |
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sentences: |
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- 'Tag: chicken lettuce wrap |
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|
|
|
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What are the principal macro ingredients of chicken lettuce wrap?' |
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- 'Tag: teriyaki tofu |
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|
|
|
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What are the micro ingredients used in teriyaki tofu?' |
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- 'Tag: beef and broccoli |
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|
|
|
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What’s the best way to describe beef and broccoli?' |
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- source_sentence: 'Tag: scrambled eggs with veggies |
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|
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|
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For scrambled eggs with veggies, these dietary tags go well with it: breakfast, |
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american cuisine, protein rich recipes, stir fry recipes, gluten free recipes' |
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sentences: |
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- 'Tag: kimchi fried rice (chicken) |
|
|
|
|
|
What are the vital macro ingredients in kimchi fried rice (chicken)?' |
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- 'Tag: scrambled eggs with veggies |
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|
|
|
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What are the key macro ingredients for scrambled eggs with veggies?' |
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- 'Tag: scrambled eggs with veggies |
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|
|
|
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How should I label scrambled eggs with veggies in terms of dietary categories?' |
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- source_sentence: 'Tag: mixed vegetable stir-fry |
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|
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|
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Micro ingredients required to cook mixed vegetable stir-fry: |
|
|
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Salt, Cornstarch, Black Pepper Powder' |
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sentences: |
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- 'Tag: vegan pad thai |
|
|
|
|
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Can you provide a thorough explanation of how to cook vegan pad thai?' |
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- 'Tag: chicken & broccoli alfredo |
|
|
|
|
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What’s involved in preparing the ingredients for chicken & broccoli alfredo?' |
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- 'Tag: mixed vegetable stir-fry |
|
|
|
|
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What are the main components of mixed vegetable stir-fry?' |
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- source_sentence: 'Tag: vegan pad thai |
|
|
|
|
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Cook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins, |
|
Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins' |
|
sentences: |
|
- 'Tag: vegan pad thai |
|
|
|
|
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What’s the expected cook time for vegan pad thai?' |
|
- 'Tag: scrambled eggs with veggies |
|
|
|
|
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What dietary classifications suit scrambled eggs with veggies?' |
|
- 'Tag: vegetable pulao |
|
|
|
|
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What are some creative garnishing tips for vegetable pulao?' |
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model-index: |
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- name: SentenceTransformer |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 384 |
|
type: dim_384 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9688300597779675 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9701110162254484 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9748078565328779 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9946626814688301 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9688300597779675 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.8469968687731283 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.8014944491887276 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.4411614005123826 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3285582123541133 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6209009393680616 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8938791122768492 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9605094343458989 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9592536302802654 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9733707623385245 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9539794228951505 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9679760888129804 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9692570452604612 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9752348420153715 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9948761742100769 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9679760888129804 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.8459294050668943 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.7992741246797609 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.43917591801878736 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.32842427107478345 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6204243930706356 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8918949005733805 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9569316518238379 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9566533189656364 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9727438392094664 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9511517923410544 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9694705380017079 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9705380017079419 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9760888129803587 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9948761742100769 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9694705380017079 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.8471391972672928 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.798462852263023 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.43800170794193005 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3286967284778983 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6210852039363994 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8912874628929282 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9550379203773738 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9558695124747556 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9739451594756885 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9499982560169666 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9698975234842016 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9720324508966696 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9771562766865927 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9938087105038429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9698975234842016 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.8472815257614573 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.7965841161400511 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.4339666951323655 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3288006791102436 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.621300984099874 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.889481670123216 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9478284738318897 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9517343805870713 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.974398746831496 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9459942940005901 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 32 |
|
type: dim_32 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9690435525192144 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9707514944491887 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9769427839453458 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9929547395388557 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9690435525192144 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.8464987190435526 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.7940222032450898 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.4318531169940221 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3286197185962344 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6208008011060958 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8871009719002887 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9440570228945548 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9489614439178549 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9734810669215016 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9417483259746888 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-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:** [Unknown](https://huggingface.co/unknown) --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 384 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': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## 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("Adi-0-0-Gupta/Embedding-v2") |
|
# Run inference |
|
sentences = [ |
|
'Tag: vegan pad thai\n\nCook time of vegan pad thai based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins', |
|
'Tag: vegan pad thai\n\nWhat’s the expected cook time for vegan pad thai?', |
|
'Tag: scrambled eggs with veggies\n\nWhat dietary classifications suit scrambled eggs with veggies?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 384] |
|
|
|
# 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.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_384` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.9688 | |
|
| cosine_accuracy@3 | 0.9701 | |
|
| cosine_accuracy@5 | 0.9748 | |
|
| cosine_accuracy@10 | 0.9947 | |
|
| cosine_precision@1 | 0.9688 | |
|
| cosine_precision@3 | 0.847 | |
|
| cosine_precision@5 | 0.8015 | |
|
| cosine_precision@10 | 0.4412 | |
|
| cosine_recall@1 | 0.3286 | |
|
| cosine_recall@3 | 0.6209 | |
|
| cosine_recall@5 | 0.8939 | |
|
| cosine_recall@10 | 0.9605 | |
|
| cosine_ndcg@10 | 0.9593 | |
|
| cosine_mrr@10 | 0.9734 | |
|
| **cosine_map@100** | **0.954** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.968 | |
|
| cosine_accuracy@3 | 0.9693 | |
|
| cosine_accuracy@5 | 0.9752 | |
|
| cosine_accuracy@10 | 0.9949 | |
|
| cosine_precision@1 | 0.968 | |
|
| cosine_precision@3 | 0.8459 | |
|
| cosine_precision@5 | 0.7993 | |
|
| cosine_precision@10 | 0.4392 | |
|
| cosine_recall@1 | 0.3284 | |
|
| cosine_recall@3 | 0.6204 | |
|
| cosine_recall@5 | 0.8919 | |
|
| cosine_recall@10 | 0.9569 | |
|
| cosine_ndcg@10 | 0.9567 | |
|
| cosine_mrr@10 | 0.9727 | |
|
| **cosine_map@100** | **0.9512** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:---------| |
|
| cosine_accuracy@1 | 0.9695 | |
|
| cosine_accuracy@3 | 0.9705 | |
|
| cosine_accuracy@5 | 0.9761 | |
|
| cosine_accuracy@10 | 0.9949 | |
|
| cosine_precision@1 | 0.9695 | |
|
| cosine_precision@3 | 0.8471 | |
|
| cosine_precision@5 | 0.7985 | |
|
| cosine_precision@10 | 0.438 | |
|
| cosine_recall@1 | 0.3287 | |
|
| cosine_recall@3 | 0.6211 | |
|
| cosine_recall@5 | 0.8913 | |
|
| cosine_recall@10 | 0.955 | |
|
| cosine_ndcg@10 | 0.9559 | |
|
| cosine_mrr@10 | 0.9739 | |
|
| **cosine_map@100** | **0.95** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.9699 | |
|
| cosine_accuracy@3 | 0.972 | |
|
| cosine_accuracy@5 | 0.9772 | |
|
| cosine_accuracy@10 | 0.9938 | |
|
| cosine_precision@1 | 0.9699 | |
|
| cosine_precision@3 | 0.8473 | |
|
| cosine_precision@5 | 0.7966 | |
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| cosine_precision@10 | 0.434 | |
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| cosine_recall@1 | 0.3288 | |
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| cosine_recall@3 | 0.6213 | |
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| cosine_recall@5 | 0.8895 | |
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| cosine_recall@10 | 0.9478 | |
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| cosine_ndcg@10 | 0.9517 | |
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| cosine_mrr@10 | 0.9744 | |
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| **cosine_map@100** | **0.946** | |
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#### Information Retrieval |
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* Dataset: `dim_32` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.969 | |
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| cosine_accuracy@3 | 0.9708 | |
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| cosine_accuracy@5 | 0.9769 | |
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| cosine_accuracy@10 | 0.993 | |
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| cosine_precision@1 | 0.969 | |
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| cosine_precision@3 | 0.8465 | |
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| cosine_precision@5 | 0.794 | |
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| cosine_precision@10 | 0.4319 | |
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| cosine_recall@1 | 0.3286 | |
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| cosine_recall@3 | 0.6208 | |
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| cosine_recall@5 | 0.8871 | |
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| cosine_recall@10 | 0.9441 | |
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| cosine_ndcg@10 | 0.949 | |
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| cosine_mrr@10 | 0.9735 | |
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| **cosine_map@100** | **0.9417** | |
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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|
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### Training Dataset |
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|
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#### Unnamed Dataset |
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* Size: 42,333 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 17 tokens</li><li>mean: 71.13 tokens</li><li>max: 433 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 22.97 tokens</li><li>max: 41 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| |
|
| <code>Tag: beef and broccoli<br><br>A small description of beef and broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.</code> | <code>Tag: beef and broccoli<br><br>How do you describe beef and broccoli?</code> | |
|
| <code>Tag: beef and broccoli<br><br>Garnishing tips for beef and broccoli: Best served on it's own or on top of hot rice with chopped scallions!</code> | <code>Tag: beef and broccoli<br><br>What are some classic garnishes for beef and broccoli?</code> | |
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| <code>Tag: beef and broccoli<br><br>For beef and broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes</code> | <code>Tag: beef and broccoli<br><br>What dietary labels suit beef and broccoli?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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384, |
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256, |
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128, |
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64, |
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32 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 100 |
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- `lr_scheduler_type`: constant |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 100 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: constant |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
|
- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `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_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `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`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:| |
|
| 0.3023 | 25 | 2.7893 | 0.9106 | 0.9169 | 0.8833 | 0.9193 | 0.9013 | |
|
| 0.6047 | 50 | 1.6554 | 0.9061 | 0.9153 | 0.8858 | 0.9199 | 0.8970 | |
|
| 0.9070 | 75 | 0.7514 | 0.9361 | 0.9382 | 0.9216 | 0.9423 | 0.9292 | |
|
| 1.2079 | 100 | 1.2044 | 0.9334 | 0.9370 | 0.9186 | 0.9413 | 0.9263 | |
|
| 1.5102 | 125 | 1.4103 | 0.9312 | 0.9342 | 0.9146 | 0.9382 | 0.9222 | |
|
| 1.8125 | 150 | 0.6925 | 0.9444 | 0.9463 | 0.9326 | 0.9502 | 0.9385 | |
|
| 2.1134 | 175 | 0.7937 | 0.9333 | 0.9376 | 0.9196 | 0.9410 | 0.9256 | |
|
| 2.4157 | 200 | 1.3185 | 0.9321 | 0.9355 | 0.9191 | 0.9399 | 0.9245 | |
|
| 2.7181 | 225 | 1.0296 | 0.9400 | 0.9426 | 0.9293 | 0.9466 | 0.9345 | |
|
| 3.0189 | 250 | 0.3606 | 0.9342 | 0.9373 | 0.9231 | 0.9417 | 0.9282 | |
|
| 3.3212 | 275 | 1.2364 | 0.9381 | 0.9410 | 0.9273 | 0.9444 | 0.9312 | |
|
| 3.6236 | 300 | 1.2507 | 0.9305 | 0.9340 | 0.9193 | 0.9385 | 0.9233 | |
|
| 3.9259 | 325 | 0.3211 | 0.9500 | 0.9512 | 0.9417 | 0.9540 | 0.9460 | |
|
|
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- 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", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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<!-- |
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*Clearly define terms in order to be accessible across audiences.* |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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