|
--- |
|
datasets: [] |
|
language: [] |
|
library_name: sentence-transformers |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:14593 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'Macro ingredients needed to cook Poha: Orange Carrot, French Bean, |
|
Fresh Green Pea, Medium Poha, Red Onion, Curry Leaf, Green Chili Pepper' |
|
sentences: |
|
- Can you list recipes that contain canned chickpea and canned black bean? |
|
- What are the leading macro ingredients in Pigeon Pea Curry (Toor Dal)? |
|
- What macro ingredients form the base of Poha? |
|
- source_sentence: 'I do have some good recommendations for you! Here are few good |
|
alternatives to kashmiri pulao: |
|
|
|
Kashmiri Dum Aloo, Shivani''s Kashmiri Dum Aloo, Chicken Pulao, Chicken Rezala, |
|
Chicken Kheema Masala, Hyderabadi Chicken Masala, Masala Khichdi, Lentils and |
|
Rice (Dal Chawal), Homestyle Vegetable Pulao' |
|
sentences: |
|
- What recipes are comparable to kashmiri pulao in flavor profile? |
|
- Can you give me step-by-step instructions to cook Hariyali Chicken Curry? |
|
- What are some recipes that utilize baking soda and olive oil effectively? |
|
- source_sentence: 'Garnishing tip for Yellow Rice: Sprinkle with chopped cilantro.' |
|
sentences: |
|
- How can I make Yellow Rice look appealing with garnishes? |
|
- Describe General Tso's Tofu for me. |
|
- What are the best garnishing tips for Paneer Tikka Masala? |
|
- source_sentence: 'Recipes that can be made using green chili pepper and grated coconut: |
|
Kerala Mix Vegetables (Aviyal), Carrot Poriyal, Cauliflower Poriyal, Beetroot |
|
Poriyal, Maithilee''s Fish Curry, Mix Vegetable Poriyal, Ivy Gourd Curry (Tindora |
|
Masala), Spiced Indian Moth Beans (Matki Usal), Fish Curry, Andhra Garlic Chicken' |
|
sentences: |
|
- What are the culinary uses of ground pork and chayote? |
|
- What are the dishes prepared using green cardamom and clove? |
|
- Can you suggest recipes that include green chili pepper and grated coconut? |
|
- source_sentence: 'Recipes that can be made using red onion and paprika: Breakfast |
|
Potatoes with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac |
|
& Cheese, Tomato Chicken Curry' |
|
sentences: |
|
- Are there dishes that closely resemble spiced potatoes & fenugreek (aloo methi)? |
|
- What recipes incorporate black pepper and habanero chili in their ingredients? |
|
- What are some ways to use red onion and paprika in recipes? |
|
model-index: |
|
- name: SentenceTransformer |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 384 |
|
type: dim_384 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9926017262638718 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.998766954377312 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33086724208795726 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1997533908754624 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09993834771886559 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9926017262638718 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.998766954377312 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9865445143406266 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9822089131583582 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9822089131583582 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9728729963008631 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9932182490752158 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.998766954377312 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9728729963008631 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3310727496917386 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1997533908754624 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09993834771886559 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9728729963008631 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9932182490752158 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.998766954377312 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9875922381599775 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9836107685984382 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9836107685984381 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9722564734895192 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9944512946979038 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9722564734895192 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33148376489930126 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19987669543773118 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09993834771886559 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9722564734895192 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9944512946979038 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9873346466071089 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9832511302918208 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9832511302918209 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9944512946979038 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33148376489930126 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19987669543773118 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09993834771886559 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9704069050554871 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9944512946979038 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9867057287670639 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9823982737361283 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9823982737361281 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 32 |
|
type: dim_32 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.971023427866831 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9950678175092479 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.971023427866831 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3316892725030826 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19987669543773118 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09993834771886559 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.971023427866831 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9950678175092479 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9993834771886559 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9872988931953259 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9831689272503082 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9831689272503081 |
|
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-v1") |
|
# Run inference |
|
sentences = [ |
|
'Recipes that can be made using red onion and paprika: Breakfast Potatoes with Sausage, Peri Peri Chicken Pasta, Scrambled Egg Curry, Chili Mac & Cheese, Tomato Chicken Curry', |
|
'What are some ways to use red onion and paprika in recipes?', |
|
'Are there dishes that closely resemble spiced potatoes & fenugreek (aloo methi)?', |
|
] |
|
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 | 0.9704 | |
|
| cosine_accuracy@3 | 0.9926 | |
|
| cosine_accuracy@5 | 0.9988 | |
|
| cosine_accuracy@10 | 0.9994 | |
|
| cosine_precision@1 | 0.9704 | |
|
| cosine_precision@3 | 0.3309 | |
|
| cosine_precision@5 | 0.1998 | |
|
| cosine_precision@10 | 0.0999 | |
|
| cosine_recall@1 | 0.9704 | |
|
| cosine_recall@3 | 0.9926 | |
|
| cosine_recall@5 | 0.9988 | |
|
| cosine_recall@10 | 0.9994 | |
|
| cosine_ndcg@10 | 0.9865 | |
|
| cosine_mrr@10 | 0.9822 | |
|
| **cosine_map@100** | **0.9822** | |
|
|
|
#### 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 | 0.9729 | |
|
| cosine_accuracy@3 | 0.9932 | |
|
| cosine_accuracy@5 | 0.9988 | |
|
| cosine_accuracy@10 | 0.9994 | |
|
| cosine_precision@1 | 0.9729 | |
|
| cosine_precision@3 | 0.3311 | |
|
| cosine_precision@5 | 0.1998 | |
|
| cosine_precision@10 | 0.0999 | |
|
| cosine_recall@1 | 0.9729 | |
|
| cosine_recall@3 | 0.9932 | |
|
| cosine_recall@5 | 0.9988 | |
|
| cosine_recall@10 | 0.9994 | |
|
| cosine_ndcg@10 | 0.9876 | |
|
| cosine_mrr@10 | 0.9836 | |
|
| **cosine_map@100** | **0.9836** | |
|
|
|
#### 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 | 0.9723 | |
|
| cosine_accuracy@3 | 0.9945 | |
|
| cosine_accuracy@5 | 0.9994 | |
|
| cosine_accuracy@10 | 0.9994 | |
|
| cosine_precision@1 | 0.9723 | |
|
| cosine_precision@3 | 0.3315 | |
|
| cosine_precision@5 | 0.1999 | |
|
| cosine_precision@10 | 0.0999 | |
|
| cosine_recall@1 | 0.9723 | |
|
| cosine_recall@3 | 0.9945 | |
|
| cosine_recall@5 | 0.9994 | |
|
| cosine_recall@10 | 0.9994 | |
|
| cosine_ndcg@10 | 0.9873 | |
|
| cosine_mrr@10 | 0.9833 | |
|
| **cosine_map@100** | **0.9833** | |
|
|
|
#### 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 | 0.9704 | |
|
| cosine_accuracy@3 | 0.9945 | |
|
| cosine_accuracy@5 | 0.9994 | |
|
| cosine_accuracy@10 | 0.9994 | |
|
| cosine_precision@1 | 0.9704 | |
|
| cosine_precision@3 | 0.3315 | |
|
| cosine_precision@5 | 0.1999 | |
|
| cosine_precision@10 | 0.0999 | |
|
| cosine_recall@1 | 0.9704 | |
|
| cosine_recall@3 | 0.9945 | |
|
| cosine_recall@5 | 0.9994 | |
|
| cosine_recall@10 | 0.9994 | |
|
| cosine_ndcg@10 | 0.9867 | |
|
| cosine_mrr@10 | 0.9824 | |
|
| **cosine_map@100** | **0.9824** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_32` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.971 | |
|
| cosine_accuracy@3 | 0.9951 | |
|
| cosine_accuracy@5 | 0.9994 | |
|
| cosine_accuracy@10 | 0.9994 | |
|
| cosine_precision@1 | 0.971 | |
|
| cosine_precision@3 | 0.3317 | |
|
| cosine_precision@5 | 0.1999 | |
|
| cosine_precision@10 | 0.0999 | |
|
| cosine_recall@1 | 0.971 | |
|
| cosine_recall@3 | 0.9951 | |
|
| cosine_recall@5 | 0.9994 | |
|
| cosine_recall@10 | 0.9994 | |
|
| cosine_ndcg@10 | 0.9873 | |
|
| cosine_mrr@10 | 0.9832 | |
|
| **cosine_map@100** | **0.9832** | |
|
|
|
<!-- |
|
## 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: 14,593 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 11 tokens</li><li>mean: 53.46 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.83 tokens</li><li>max: 32 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| <code>Calories information of Hyderabadi Chicken Masala, based on different serving sizes: Serving 1 - 345 calories, Serving 2 - 580 calories, Serving 3 - 1220 calories, Serving 4 - 1450 calories</code> | <code>What’s the calorie content of Hyderabadi Chicken Masala?</code> | |
|
| <code>Recipes that can be made using dried herb mix and onion powder: Chorizo Queso Soup, Cheesy Chicken & Broccoli</code> | <code>What are some food items made using dried herb mix and onion powder?</code> | |
|
| <code>Recipes that can be made using roasted semolina/bombay rava and saffron: Rashmi's Kesari Bath, Pineapple Kesari Bath</code> | <code>What recipes have roasted semolina/bombay rava and saffron in them?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
384, |
|
256, |
|
128, |
|
64, |
|
32 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 1e-05 |
|
- `num_train_epochs`: 20 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 32 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 1e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 20 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `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`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `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`: True |
|
- `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_fused |
|
- `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`: 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 | |
|
|:-------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:| |
|
| 0.3501 | 10 | 0.0066 | - | - | - | - | - | |
|
| 0.7002 | 20 | 0.0056 | - | - | - | - | - | |
|
| 0.9803 | 28 | - | 0.9746 | 0.9771 | 0.9776 | 0.9758 | 0.9763 | |
|
| 1.0503 | 30 | 0.0057 | - | - | - | - | - | |
|
| 1.4004 | 40 | 0.0048 | - | - | - | - | - | |
|
| 1.7505 | 50 | 0.0039 | - | - | - | - | - | |
|
| 1.9956 | 57 | - | 0.9783 | 0.9787 | 0.9815 | 0.9788 | 0.9793 | |
|
| 2.1007 | 60 | 0.0046 | - | - | - | - | - | |
|
| 2.4508 | 70 | 0.0035 | - | - | - | - | - | |
|
| 2.8009 | 80 | 0.0028 | - | - | - | - | - | |
|
| 2.9759 | 85 | - | 0.9818 | 0.9811 | 0.9836 | 0.9803 | 0.9823 | |
|
| 3.1510 | 90 | 0.0036 | - | - | - | - | - | |
|
| 3.5011 | 100 | 0.0033 | - | - | - | - | - | |
|
| 3.8512 | 110 | 0.0026 | - | - | - | - | - | |
|
| 3.9912 | 114 | - | 0.9814 | 0.9818 | 0.9844 | 0.9814 | 0.9821 | |
|
| 4.2013 | 120 | 0.0025 | - | - | - | - | - | |
|
| 4.5514 | 130 | 0.003 | - | - | - | - | - | |
|
| 4.9015 | 140 | 0.0027 | - | - | - | - | - | |
|
| 4.9716 | 142 | - | 0.9825 | 0.9819 | 0.9844 | 0.9823 | 0.9825 | |
|
| 5.2516 | 150 | 0.0024 | - | - | - | - | - | |
|
| 5.6018 | 160 | 0.0023 | - | - | - | - | - | |
|
| 5.9519 | 170 | 0.0024 | - | - | - | - | - | |
|
| 5.9869 | 171 | - | 0.9831 | 0.9826 | 0.9846 | 0.9818 | 0.9831 | |
|
| 6.3020 | 180 | 0.0025 | - | - | - | - | - | |
|
| 6.6521 | 190 | 0.0025 | - | - | - | - | - | |
|
| 6.9672 | 199 | - | 0.9830 | 0.9825 | 0.9844 | 0.9823 | 0.9831 | |
|
| 7.0022 | 200 | 0.0019 | - | - | - | - | - | |
|
| 7.3523 | 210 | 0.0022 | - | - | - | - | - | |
|
| 7.7024 | 220 | 0.0026 | - | - | - | - | - | |
|
| 7.9825 | 228 | - | 0.9828 | 0.9825 | 0.9836 | 0.9821 | 0.9821 | |
|
| 8.0525 | 230 | 0.0022 | - | - | - | - | - | |
|
| 8.4026 | 240 | 0.0021 | - | - | - | - | - | |
|
| 8.7527 | 250 | 0.0021 | - | - | - | - | - | |
|
| 8.9978 | 257 | - | 0.9827 | 0.9826 | 0.9848 | 0.9827 | 0.9827 | |
|
| 9.1028 | 260 | 0.0025 | - | - | - | - | - | |
|
| 9.4530 | 270 | 0.0022 | - | - | - | - | - | |
|
| 9.8031 | 280 | 0.0019 | - | - | - | - | - | |
|
| 9.9781 | 285 | - | 0.9832 | 0.9833 | 0.9858 | 0.9825 | 0.9834 | |
|
| 10.1532 | 290 | 0.0021 | - | - | - | - | - | |
|
| 10.5033 | 300 | 0.0019 | - | - | - | - | - | |
|
| 10.8534 | 310 | 0.0024 | - | - | - | - | - | |
|
| 10.9934 | 314 | - | 0.9830 | 0.9827 | 0.9850 | 0.9825 | 0.9829 | |
|
| 11.2035 | 320 | 0.0017 | - | - | - | - | - | |
|
| 11.5536 | 330 | 0.0017 | - | - | - | - | - | |
|
| 11.9037 | 340 | 0.0018 | - | - | - | - | - | |
|
| 11.9737 | 342 | - | 0.9827 | 0.9835 | 0.9841 | 0.9826 | 0.9827 | |
|
| 12.2538 | 350 | 0.0018 | - | - | - | - | - | |
|
| 12.6039 | 360 | 0.0018 | - | - | - | - | - | |
|
| 12.9540 | 370 | 0.0023 | - | - | - | - | - | |
|
| 12.9891 | 371 | - | 0.9828 | 0.9834 | 0.9832 | 0.9826 | 0.9823 | |
|
| 13.3042 | 380 | 0.0017 | - | - | - | - | - | |
|
| 13.6543 | 390 | 0.0018 | - | - | - | - | - | |
|
| 13.9694 | 399 | - | 0.9830 | 0.9831 | 0.9838 | 0.9820 | 0.9826 | |
|
| 14.0044 | 400 | 0.0016 | - | - | - | - | - | |
|
| 14.3545 | 410 | 0.0018 | - | - | - | - | - | |
|
| 14.7046 | 420 | 0.0018 | - | - | - | - | - | |
|
| 14.9847 | 428 | - | 0.9827 | 0.9825 | 0.9832 | 0.9816 | 0.9826 | |
|
| 15.0547 | 430 | 0.0018 | - | - | - | - | - | |
|
| 15.4048 | 440 | 0.0015 | - | - | - | - | - | |
|
| 15.7549 | 450 | 0.0017 | - | - | - | - | - | |
|
| 16.0 | 457 | - | 0.9833 | 0.9836 | 0.9832 | 0.9822 | 0.9824 | |
|
|
|
|
|
### 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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## 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.* |
|
--> |
