|
--- |
|
base_model: BAAI/bge-small-en-v1.5 |
|
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:60323 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
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- source_sentence: No recipes found with these beef stock powder and orange juice! |
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sentences: |
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- Can you provide recipe ideas with beef stock powder and orange juice? |
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- What are some recipes that utilize jasmine rice and thai red curry paste effectively? |
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- What recipes incorporate broccoli and bacon into meals? |
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- source_sentence: No recipes found with these nutmeg flower and angel hair rice noodles! |
|
sentences: |
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- What dishes can be created with kale and bok choy? |
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- What recipes incorporate green zucchini and vegan ground beef into meals? |
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- Can you provide me with meal ideas using nutmeg flower and angel hair rice noodles? |
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- source_sentence: No recipes found with these cinnamon and ground lamb! |
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sentences: |
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- Can you suggest dishes where cinnamon and ground lamb is key? |
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- What diet tags are relevant to Sneha's Aloo Baingan ? |
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- What recipes are there with toasted sesame oil and red lentils/masoor? |
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- source_sentence: No recipes found with these red lentils/masoor and bok choy! |
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sentences: |
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- What are the culinary uses of chili sauce and sriracha? |
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- What are some ways to use canned tomato puree and frozen ube in recipes? |
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- What are some ideas for dishes with red lentils/masoor and bok choy? |
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- source_sentence: No recipes found with these red onion and cubed stuffing! |
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sentences: |
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- Can you provide meal suggestions involving vanilla extract and brown lentil/black |
|
masoor dal? |
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- What recipes incorporate methi (fenugreek) and honey in their ingredients? |
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- What culinary preparations can be made with red onion and cubed stuffing? |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-small-en-v1.5 |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 384 |
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type: dim_384 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9819483813217962 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9976130091004028 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9995524392063255 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9819483813217962 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.33253766970013426 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1999104878412651 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9819483813217962 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9976130091004028 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9995524392063255 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9923670621371893 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9897597379993318 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9897597379993323 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9812024466656721 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.997463822169178 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9998508130687752 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9812024466656721 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3324879407230593 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19997016261375503 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9812024466656721 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.997463822169178 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9998508130687752 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9921395779775503 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9894450246158434 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9894450246158436 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.979561390422199 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9970162613755035 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9998508130687752 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.979561390422199 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3323387537918345 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19997016261375505 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.979561390422199 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9970162613755035 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9998508130687752 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9913010184783637 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9883310955293644 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9883310955293649 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.9816500074593466 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.9968670744442787 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9997016261375503 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.9816500074593466 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.3322890248147595 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.19994032522751004 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09999999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.9816500074593466 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.9968670744442787 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9997016261375503 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.9920343842432707 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.9893333120209138 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.9893333120209146 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-small-en-v1.5 |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> |
|
- **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") |
|
# Run inference |
|
sentences = [ |
|
'No recipes found with these red onion and cubed stuffing!', |
|
'What culinary preparations can be made with red onion and cubed stuffing?', |
|
'Can you provide meal suggestions involving vanilla extract and brown lentil/black masoor dal?', |
|
] |
|
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) |
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|
|
<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 | 0.9819 | |
|
| cosine_accuracy@3 | 0.9976 | |
|
| cosine_accuracy@5 | 0.9996 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9819 | |
|
| cosine_precision@3 | 0.3325 | |
|
| cosine_precision@5 | 0.1999 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.9819 | |
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| cosine_recall@3 | 0.9976 | |
|
| cosine_recall@5 | 0.9996 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9924 | |
|
| cosine_mrr@10 | 0.9898 | |
|
| **cosine_map@100** | **0.9898** | |
|
|
|
#### 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 | 0.9812 | |
|
| cosine_accuracy@3 | 0.9975 | |
|
| cosine_accuracy@5 | 0.9999 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9812 | |
|
| cosine_precision@3 | 0.3325 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.9812 | |
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| cosine_recall@3 | 0.9975 | |
|
| cosine_recall@5 | 0.9999 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9921 | |
|
| cosine_mrr@10 | 0.9894 | |
|
| **cosine_map@100** | **0.9894** | |
|
|
|
#### 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 | 0.9796 | |
|
| cosine_accuracy@3 | 0.997 | |
|
| cosine_accuracy@5 | 0.9999 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9796 | |
|
| cosine_precision@3 | 0.3323 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.9796 | |
|
| cosine_recall@3 | 0.997 | |
|
| cosine_recall@5 | 0.9999 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.9913 | |
|
| cosine_mrr@10 | 0.9883 | |
|
| **cosine_map@100** | **0.9883** | |
|
|
|
#### 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 | 0.9817 | |
|
| cosine_accuracy@3 | 0.9969 | |
|
| cosine_accuracy@5 | 0.9997 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.9817 | |
|
| cosine_precision@3 | 0.3323 | |
|
| cosine_precision@5 | 0.1999 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.9817 | |
|
| cosine_recall@3 | 0.9969 | |
|
| cosine_recall@5 | 0.9997 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.992 | |
|
| cosine_mrr@10 | 0.9893 | |
|
| **cosine_map@100** | **0.9893** | |
|
|
|
<!-- |
|
## 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: 60,323 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: 21.41 tokens</li><li>max: 503 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.8 tokens</li><li>max: 31 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| |
|
| <code>No recipes found with these indian cottage cheese (paneer) and bitter melon!</code> | <code>What are some culinary options with indian cottage cheese (paneer) and bitter melon?</code> | |
|
| <code>No recipes found with these curry leaf and rice cakes!</code> | <code>What recipes can be made using curry leaf and rice cakes?</code> | |
|
| <code>No recipes found with these bacon and rosemary!</code> | <code>What are the different culinary recipes that use bacon and rosemary?</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 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 64 |
|
- `per_device_eval_batch_size`: 64 |
|
- `gradient_accumulation_steps`: 8 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 10 |
|
- `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`: 64 |
|
- `per_device_eval_batch_size`: 64 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 8 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-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`: 10 |
|
- `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_384_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.0848 | 10 | 3.9258 | - | - | - | - | |
|
| 0.1697 | 20 | 3.0513 | - | - | - | - | |
|
| 0.2545 | 30 | 1.6368 | - | - | - | - | |
|
| 0.3393 | 40 | 0.5491 | - | - | - | - | |
|
| 0.4242 | 50 | 0.1541 | - | - | - | - | |
|
| 0.5090 | 60 | 0.0615 | - | - | - | - | |
|
| 0.5938 | 70 | 0.0426 | - | - | - | - | |
|
| 0.6787 | 80 | 0.037 | - | - | - | - | |
|
| 0.7635 | 90 | 0.0312 | - | - | - | - | |
|
| 0.8484 | 100 | 0.0246 | - | - | - | - | |
|
| 0.9332 | 110 | 0.029 | - | - | - | - | |
|
| 0.9926 | 117 | - | 0.9855 | 0.9869 | 0.9869 | 0.9855 | |
|
| 1.0180 | 120 | 0.0205 | - | - | - | - | |
|
| 1.1029 | 130 | 0.0212 | - | - | - | - | |
|
| 1.1877 | 140 | 0.0196 | - | - | - | - | |
|
| 1.2725 | 150 | 0.0157 | - | - | - | - | |
|
| 1.3574 | 160 | 0.0174 | - | - | - | - | |
|
| 1.4422 | 170 | 0.0152 | - | - | - | - | |
|
| 1.5270 | 180 | 0.0155 | - | - | - | - | |
|
| 1.6119 | 190 | 0.0133 | - | - | - | - | |
|
| 1.6967 | 200 | 0.0173 | - | - | - | - | |
|
| 1.7815 | 210 | 0.014 | - | - | - | - | |
|
| 1.8664 | 220 | 0.0127 | - | - | - | - | |
|
| 1.9512 | 230 | 0.0116 | - | - | - | - | |
|
| 1.9936 | 235 | - | 0.9883 | 0.9894 | 0.9898 | 0.9893 | |
|
|
|
|
|
### 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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