|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
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:56355 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: "\n Given the Column informations, generate an SQL query for\ |
|
\ the following question:\n Column: Finishing position | Points awarded (Platinum)\ |
|
\ | Points awarded (Gold) | Points awarded (Silver) | Points awarded (Satellite)\n\ |
|
\ Question: How many platinum points were awarded when 6 gold points were awarded?\n\ |
|
\ SQL Query: SELECT MAX Points awarded (Platinum) FROM table WHERE Points awarded\ |
|
\ (Gold) = 6\n " |
|
sentences: |
|
- How many platinum points were awarded when 6 gold points were awarded? |
|
- Did any team score games that totaled up to 860.5? |
|
- Who had the pole position at the German Grand Prix? |
|
- source_sentence: "\n Given the Column informations, generate an SQL query for\ |
|
\ the following question:\n Column: Player | No. | Nationality | Position | Years\ |
|
\ in Toronto | School/Club Team\n Question: What's Dell Curry nationality?\n\ |
|
\ SQL Query: SELECT Nationality FROM table WHERE Player = Dell Curry\n " |
|
sentences: |
|
- What is the title when original air date is may15,2008? |
|
- What's Dell Curry nationality? |
|
- What's the minimum total attendance of the Premier League association football? |
|
- source_sentence: "\n Given the Column informations, generate an SQL query for\ |
|
\ the following question:\n Column: Sepal length | Sepal width | Petal length\ |
|
\ | Petal width | Species\n Question: Name the species when petal width is 2.0\ |
|
\ and petal length is 4.9\n SQL Query: SELECT Species FROM table WHERE Petal\ |
|
\ width = 2.0 AND Petal length = 4.9\n " |
|
sentences: |
|
- What year was the championship in Wimbledon (2)? |
|
- Who wrote Series 38? |
|
- Name the species when petal width is 2.0 and petal length is 4.9 |
|
- source_sentence: "\n Given the Column informations, generate an SQL query for\ |
|
\ the following question:\n Column: No. in season | No. in series | Title | Directed\ |
|
\ by | Written by | Original air date | U.S. viewers (million)\n Question: How\ |
|
\ many millions of U.S. viewers watched the episode that first aired on March\ |
|
\ 31, 2013?\n SQL Query: SELECT U.S. viewers (million) FROM table WHERE Original\ |
|
\ air date = March 31, 2013\n " |
|
sentences: |
|
- How many millions of U.S. viewers watched the episode that first aired on March |
|
31, 2013? |
|
- How many viewers were there for the premier with 34 |
|
- What is Bruce Cerone overall? |
|
- source_sentence: "\n Given the Column informations, generate an SQL query for\ |
|
\ the following question:\n Column: Nomination | Actors Name | Film Name | Director\ |
|
\ | Country\n Question: What was the film Falling up nominated for?\n SQL Query:\ |
|
\ SELECT Nomination FROM table WHERE Film Name = Falling Up\n " |
|
sentences: |
|
- What was the film Falling up nominated for? |
|
- Who wrote an episode watched by 19.01 million US viewers? |
|
- What player is on the Montreal Alouettes CFl team? |
|
model-index: |
|
- name: BGE base SQL Matryoshka |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.4676281647562665 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.4676281647562665 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.15656883738506108 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09394130243103667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.046970651215518334 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.4676281647562665 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.46889822604232273 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4686148549355503 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4686406337350657 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.15656883738506108 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09394130243103667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.046970651215518334 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4689612062665323 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.46869882856782963 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4687237988187482 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.46750220430784734 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.46976949237939286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.46750220430784734 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.15656883738506108 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09394130243103667 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04697694923793929 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.46750220430784734 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.46976949237939286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4688906637675648 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4685833648234455 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.468602927990512 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.46769114498047615 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4696435319309737 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.46976949237939286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.46976949237939286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.46769114498047615 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1565478439769912 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09395389847587858 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.04697694923793929 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.46769114498047615 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4696435319309737 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.46976949237939286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.46976949237939286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4689469541953942 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.468661040433304 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4686773555936371 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.4696435319309737 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4696435319309737 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1565478439769912 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09392870638619474 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.046970651215518334 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.46775412520468573 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.4696435319309737 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4696435319309737 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.4697065121551833 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.4689578301883334 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.468696204391821 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.46870770760703784 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base SQL Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 dimensions |
|
- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
|
- json |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### 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': 768, '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("dat-ai/bge-base-for_text2sql") |
|
# Run inference |
|
sentences = [ |
|
'\n Given the Column informations, generate an SQL query for the following question:\n Column: Nomination | Actors Name | Film Name | Director | Country\n Question: What was the film Falling up nominated for?\n SQL Query: SELECT Nomination FROM table WHERE Film Name = Falling Up\n ', |
|
'What was the film Falling up nominated for?', |
|
'Who wrote an episode watched by 19.01 million US viewers?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
|
|:--------------------|:-----------|:----------|:-----------|:-----------|:----------| |
|
| cosine_accuracy@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | |
|
| cosine_accuracy@3 | 0.4697 | 0.4697 | 0.4697 | 0.4696 | 0.4696 | |
|
| cosine_accuracy@5 | 0.4697 | 0.4697 | 0.4697 | 0.4698 | 0.4696 | |
|
| cosine_accuracy@10 | 0.4697 | 0.4697 | 0.4698 | 0.4698 | 0.4697 | |
|
| cosine_precision@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | |
|
| cosine_precision@3 | 0.1566 | 0.1566 | 0.1566 | 0.1565 | 0.1565 | |
|
| cosine_precision@5 | 0.0939 | 0.0939 | 0.0939 | 0.094 | 0.0939 | |
|
| cosine_precision@10 | 0.047 | 0.047 | 0.047 | 0.047 | 0.047 | |
|
| cosine_recall@1 | 0.4676 | 0.4678 | 0.4675 | 0.4677 | 0.4678 | |
|
| cosine_recall@3 | 0.4697 | 0.4697 | 0.4697 | 0.4696 | 0.4696 | |
|
| cosine_recall@5 | 0.4697 | 0.4697 | 0.4697 | 0.4698 | 0.4696 | |
|
| cosine_recall@10 | 0.4697 | 0.4697 | 0.4698 | 0.4698 | 0.4697 | |
|
| **cosine_ndcg@10** | **0.4689** | **0.469** | **0.4689** | **0.4689** | **0.469** | |
|
| cosine_mrr@10 | 0.4686 | 0.4687 | 0.4686 | 0.4687 | 0.4687 | |
|
| cosine_map@100 | 0.4686 | 0.4687 | 0.4686 | 0.4687 | 0.4687 | |
|
|
|
<!-- |
|
## 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 |
|
|
|
#### json |
|
|
|
* Dataset: json |
|
* Size: 56,355 training samples |
|
* Columns: <code>context</code> and <code>question</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | context | question | |
|
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 45 tokens</li><li>mean: 72.61 tokens</li><li>max: 196 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.41 tokens</li><li>max: 36 tokens</li></ul> | |
|
* Samples: |
|
| context | question | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
|
| <code><br> Given the Column informations, generate an SQL query for the following question:<br> Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes<br> Question: Tell me what the notes are for South Australia <br> SQL Query: SELECT Notes FROM table WHERE Current slogan = SOUTH AUSTRALIA<br> </code> | <code>Tell me what the notes are for South Australia </code> | |
|
| <code><br> Given the Column informations, generate an SQL query for the following question:<br> Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes<br> Question: What is the current series where the new series began in June 2011?<br> SQL Query: SELECT Current series FROM table WHERE Notes = New series began in June 2011<br> </code> | <code>What is the current series where the new series began in June 2011?</code> | |
|
| <code><br> Given the Column informations, generate an SQL query for the following question:<br> Column: State/territory | Text/background colour | Format | Current slogan | Current series | Notes<br> Question: What is the format for South Australia?<br> SQL Query: SELECT Format FROM table WHERE State/territory = South Australia<br> </code> | <code>What is the format for South Australia?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 8 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `fp16`: 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`: 16 |
|
- `per_device_eval_batch_size`: 8 |
|
- `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`: 4 |
|
- `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`: False |
|
- `fp16`: True |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: None |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: 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 |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:----------:|:--------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.0227 | 10 | 1.773 | - | - | - | - | - | |
|
| 0.0454 | 20 | 1.3231 | - | - | - | - | - | |
|
| 0.0681 | 30 | 0.713 | - | - | - | - | - | |
|
| 0.0908 | 40 | 0.286 | - | - | - | - | - | |
|
| 0.1135 | 50 | 0.1013 | - | - | - | - | - | |
|
| 0.1362 | 60 | 0.0635 | - | - | - | - | - | |
|
| 0.1590 | 70 | 0.0453 | - | - | - | - | - | |
|
| 0.1817 | 80 | 0.041 | - | - | - | - | - | |
|
| 0.2044 | 90 | 0.039 | - | - | - | - | - | |
|
| 0.2271 | 100 | 0.027 | - | - | - | - | - | |
|
| 0.2498 | 110 | 0.0193 | - | - | - | - | - | |
|
| 0.2725 | 120 | 0.0167 | - | - | - | - | - | |
|
| 0.2952 | 130 | 0.016 | - | - | - | - | - | |
|
| 0.3179 | 140 | 0.0197 | - | - | - | - | - | |
|
| 0.3406 | 150 | 0.0217 | - | - | - | - | - | |
|
| 0.3633 | 160 | 0.0162 | - | - | - | - | - | |
|
| 0.3860 | 170 | 0.012 | - | - | - | - | - | |
|
| 0.4087 | 180 | 0.013 | - | - | - | - | - | |
|
| 0.4315 | 190 | 0.0255 | - | - | - | - | - | |
|
| 0.4542 | 200 | 0.0229 | - | - | - | - | - | |
|
| 0.4769 | 210 | 0.0181 | - | - | - | - | - | |
|
| 0.4996 | 220 | 0.0195 | - | - | - | - | - | |
|
| 0.5223 | 230 | 0.0199 | - | - | - | - | - | |
|
| 0.5450 | 240 | 0.0144 | - | - | - | - | - | |
|
| 0.5677 | 250 | 0.0102 | - | - | - | - | - | |
|
| 0.5904 | 260 | 0.0101 | - | - | - | - | - | |
|
| 0.6131 | 270 | 0.0095 | - | - | - | - | - | |
|
| 0.6358 | 280 | 0.0173 | - | - | - | - | - | |
|
| 0.6585 | 290 | 0.01 | - | - | - | - | - | |
|
| 0.6812 | 300 | 0.0129 | - | - | - | - | - | |
|
| 0.7039 | 310 | 0.0177 | - | - | - | - | - | |
|
| 0.7267 | 320 | 0.0106 | - | - | - | - | - | |
|
| 0.7494 | 330 | 0.0146 | - | - | - | - | - | |
|
| 0.7721 | 340 | 0.0185 | - | - | - | - | - | |
|
| 0.7948 | 350 | 0.0203 | - | - | - | - | - | |
|
| 0.8175 | 360 | 0.0146 | - | - | - | - | - | |
|
| 0.8402 | 370 | 0.0072 | - | - | - | - | - | |
|
| 0.8629 | 380 | 0.0102 | - | - | - | - | - | |
|
| 0.8856 | 390 | 0.0075 | - | - | - | - | - | |
|
| 0.9083 | 400 | 0.0064 | - | - | - | - | - | |
|
| 0.9310 | 410 | 0.0163 | - | - | - | - | - | |
|
| 0.9537 | 420 | 0.0069 | - | - | - | - | - | |
|
| 0.9764 | 430 | 0.0072 | - | - | - | - | - | |
|
| 0.9991 | 440 | 0.0147 | 0.4688 | 0.4689 | 0.4688 | 0.4689 | 0.4689 | |
|
| 1.0219 | 450 | 0.0151 | - | - | - | - | - | |
|
| 1.0446 | 460 | 0.0135 | - | - | - | - | - | |
|
| 1.0673 | 470 | 0.0189 | - | - | - | - | - | |
|
| 1.0900 | 480 | 0.0121 | - | - | - | - | - | |
|
| 1.1127 | 490 | 0.0064 | - | - | - | - | - | |
|
| 1.1354 | 500 | 0.0111 | - | - | - | - | - | |
|
| 1.1581 | 510 | 0.0103 | - | - | - | - | - | |
|
| 1.1808 | 520 | 0.0144 | - | - | - | - | - | |
|
| 1.2035 | 530 | 0.0151 | - | - | - | - | - | |
|
| 1.2262 | 540 | 0.0062 | - | - | - | - | - | |
|
| 1.2489 | 550 | 0.0104 | - | - | - | - | - | |
|
| 1.2716 | 560 | 0.0046 | - | - | - | - | - | |
|
| 1.2944 | 570 | 0.0056 | - | - | - | - | - | |
|
| 1.3171 | 580 | 0.0073 | - | - | - | - | - | |
|
| 1.3398 | 590 | 0.007 | - | - | - | - | - | |
|
| 1.3625 | 600 | 0.0074 | - | - | - | - | - | |
|
| 1.3852 | 610 | 0.0057 | - | - | - | - | - | |
|
| 1.4079 | 620 | 0.0052 | - | - | - | - | - | |
|
| 1.4306 | 630 | 0.0114 | - | - | - | - | - | |
|
| 1.4533 | 640 | 0.0075 | - | - | - | - | - | |
|
| 1.4760 | 650 | 0.0116 | - | - | - | - | - | |
|
| 1.4987 | 660 | 0.0092 | - | - | - | - | - | |
|
| 1.5214 | 670 | 0.0137 | - | - | - | - | - | |
|
| 1.5441 | 680 | 0.0066 | - | - | - | - | - | |
|
| 1.5668 | 690 | 0.0042 | - | - | - | - | - | |
|
| 1.5896 | 700 | 0.0036 | - | - | - | - | - | |
|
| 1.6123 | 710 | 0.0039 | - | - | - | - | - | |
|
| 1.6350 | 720 | 0.0065 | - | - | - | - | - | |
|
| 1.6577 | 730 | 0.0051 | - | - | - | - | - | |
|
| 1.6804 | 740 | 0.0054 | - | - | - | - | - | |
|
| 1.7031 | 750 | 0.0086 | - | - | - | - | - | |
|
| 1.7258 | 760 | 0.0062 | - | - | - | - | - | |
|
| 1.7485 | 770 | 0.0071 | - | - | - | - | - | |
|
| 1.7712 | 780 | 0.0108 | - | - | - | - | - | |
|
| 1.7939 | 790 | 0.009 | - | - | - | - | - | |
|
| 1.8166 | 800 | 0.0075 | - | - | - | - | - | |
|
| 1.8393 | 810 | 0.0039 | - | - | - | - | - | |
|
| 1.8620 | 820 | 0.0047 | - | - | - | - | - | |
|
| 1.8848 | 830 | 0.0037 | - | - | - | - | - | |
|
| 1.9075 | 840 | 0.0037 | - | - | - | - | - | |
|
| 1.9302 | 850 | 0.0064 | - | - | - | - | - | |
|
| 1.9529 | 860 | 0.0047 | - | - | - | - | - | |
|
| 1.9756 | 870 | 0.0034 | - | - | - | - | - | |
|
| 1.9983 | 880 | 0.0061 | 0.4689 | 0.4689 | 0.4689 | 0.4690 | 0.4690 | |
|
| 2.0210 | 890 | 0.0096 | - | - | - | - | - | |
|
| 2.0437 | 900 | 0.0071 | - | - | - | - | - | |
|
| 2.0664 | 910 | 0.0101 | - | - | - | - | - | |
|
| 2.0891 | 920 | 0.0054 | - | - | - | - | - | |
|
| 2.1118 | 930 | 0.0039 | - | - | - | - | - | |
|
| 2.1345 | 940 | 0.0074 | - | - | - | - | - | |
|
| 2.1573 | 950 | 0.0044 | - | - | - | - | - | |
|
| 2.1800 | 960 | 0.0088 | - | - | - | - | - | |
|
| 2.2027 | 970 | 0.0096 | - | - | - | - | - | |
|
| 2.2254 | 980 | 0.0057 | - | - | - | - | - | |
|
| 2.2481 | 990 | 0.0063 | - | - | - | - | - | |
|
| 2.2708 | 1000 | 0.0026 | - | - | - | - | - | |
|
| 2.2935 | 1010 | 0.0032 | - | - | - | - | - | |
|
| 2.3162 | 1020 | 0.0027 | - | - | - | - | - | |
|
| 2.3389 | 1030 | 0.0041 | - | - | - | - | - | |
|
| 2.3616 | 1040 | 0.0052 | - | - | - | - | - | |
|
| 2.3843 | 1050 | 0.0035 | - | - | - | - | - | |
|
| 2.4070 | 1060 | 0.0025 | - | - | - | - | - | |
|
| 2.4297 | 1070 | 0.0059 | - | - | - | - | - | |
|
| 2.4525 | 1080 | 0.0048 | - | - | - | - | - | |
|
| 2.4752 | 1090 | 0.0064 | - | - | - | - | - | |
|
| 2.4979 | 1100 | 0.0066 | - | - | - | - | - | |
|
| 2.5206 | 1110 | 0.0078 | - | - | - | - | - | |
|
| 2.5433 | 1120 | 0.0057 | - | - | - | - | - | |
|
| 2.5660 | 1130 | 0.0026 | - | - | - | - | - | |
|
| 2.5887 | 1140 | 0.0021 | - | - | - | - | - | |
|
| 2.6114 | 1150 | 0.0021 | - | - | - | - | - | |
|
| 2.6341 | 1160 | 0.0047 | - | - | - | - | - | |
|
| 2.6568 | 1170 | 0.0034 | - | - | - | - | - | |
|
| 2.6795 | 1180 | 0.0044 | - | - | - | - | - | |
|
| 2.7022 | 1190 | 0.0058 | - | - | - | - | - | |
|
| 2.7250 | 1200 | 0.0043 | - | - | - | - | - | |
|
| 2.7477 | 1210 | 0.0056 | - | - | - | - | - | |
|
| 2.7704 | 1220 | 0.0076 | - | - | - | - | - | |
|
| 2.7931 | 1230 | 0.0063 | - | - | - | - | - | |
|
| 2.8158 | 1240 | 0.0033 | - | - | - | - | - | |
|
| 2.8385 | 1250 | 0.0025 | - | - | - | - | - | |
|
| 2.8612 | 1260 | 0.0019 | - | - | - | - | - | |
|
| 2.8839 | 1270 | 0.0052 | - | - | - | - | - | |
|
| 2.9066 | 1280 | 0.0021 | - | - | - | - | - | |
|
| 2.9293 | 1290 | 0.0041 | - | - | - | - | - | |
|
| 2.9520 | 1300 | 0.0035 | - | - | - | - | - | |
|
| 2.9747 | 1310 | 0.0044 | - | - | - | - | - | |
|
| 2.9974 | 1320 | 0.0035 | - | - | - | - | - | |
|
| **2.9997** | **1321** | **-** | **0.469** | **0.469** | **0.469** | **0.469** | **0.469** | |
|
| 3.0202 | 1330 | 0.0062 | - | - | - | - | - | |
|
| 3.0429 | 1340 | 0.0047 | - | - | - | - | - | |
|
| 3.0656 | 1350 | 0.008 | - | - | - | - | - | |
|
| 3.0883 | 1360 | 0.0033 | - | - | - | - | - | |
|
| 3.1110 | 1370 | 0.0025 | - | - | - | - | - | |
|
| 3.1337 | 1380 | 0.0069 | - | - | - | - | - | |
|
| 3.1564 | 1390 | 0.0035 | - | - | - | - | - | |
|
| 3.1791 | 1400 | 0.0085 | - | - | - | - | - | |
|
| 3.2018 | 1410 | 0.007 | - | - | - | - | - | |
|
| 3.2245 | 1420 | 0.007 | - | - | - | - | - | |
|
| 3.2472 | 1430 | 0.0052 | - | - | - | - | - | |
|
| 3.2699 | 1440 | 0.0019 | - | - | - | - | - | |
|
| 3.2926 | 1450 | 0.0022 | - | - | - | - | - | |
|
| 3.3154 | 1460 | 0.0019 | - | - | - | - | - | |
|
| 3.3381 | 1470 | 0.0028 | - | - | - | - | - | |
|
| 3.3608 | 1480 | 0.0042 | - | - | - | - | - | |
|
| 3.3835 | 1490 | 0.0023 | - | - | - | - | - | |
|
| 3.4062 | 1500 | 0.0024 | - | - | - | - | - | |
|
| 3.4289 | 1510 | 0.0036 | - | - | - | - | - | |
|
| 3.4516 | 1520 | 0.0038 | - | - | - | - | - | |
|
| 3.4743 | 1530 | 0.0063 | - | - | - | - | - | |
|
| 3.4970 | 1540 | 0.0044 | - | - | - | - | - | |
|
| 3.5197 | 1550 | 0.0064 | - | - | - | - | - | |
|
| 3.5424 | 1560 | 0.0053 | - | - | - | - | - | |
|
| 3.5651 | 1570 | 0.0019 | - | - | - | - | - | |
|
| 3.5879 | 1580 | 0.0019 | - | - | - | - | - | |
|
| 3.6106 | 1590 | 0.0017 | - | - | - | - | - | |
|
| 3.6333 | 1600 | 0.004 | - | - | - | - | - | |
|
| 3.6560 | 1610 | 0.0026 | - | - | - | - | - | |
|
| 3.6787 | 1620 | 0.0031 | - | - | - | - | - | |
|
| 3.7014 | 1630 | 0.0043 | - | - | - | - | - | |
|
| 3.7241 | 1640 | 0.0032 | - | - | - | - | - | |
|
| 3.7468 | 1650 | 0.0041 | - | - | - | - | - | |
|
| 3.7695 | 1660 | 0.0069 | - | - | - | - | - | |
|
| 3.7922 | 1670 | 0.0063 | - | - | - | - | - | |
|
| 3.8149 | 1680 | 0.0038 | - | - | - | - | - | |
|
| 3.8376 | 1690 | 0.0024 | - | - | - | - | - | |
|
| 3.8603 | 1700 | 0.0018 | - | - | - | - | - | |
|
| 3.8831 | 1710 | 0.0034 | - | - | - | - | - | |
|
| 3.9058 | 1720 | 0.0016 | - | - | - | - | - | |
|
| 3.9285 | 1730 | 0.0026 | - | - | - | - | - | |
|
| 3.9512 | 1740 | 0.0037 | - | - | - | - | - | |
|
| 3.9739 | 1750 | 0.0024 | - | - | - | - | - | |
|
| 3.9966 | 1760 | 0.0027 | 0.4689 | 0.4690 | 0.4689 | 0.4689 | 0.4690 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
</details> |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.3.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.34.2 |
|
- 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} |
|
} |
|
``` |
|
|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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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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