|
---
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|
tags:
|
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- sentence-transformers
|
|
- sentence-similarity
|
|
- feature-extraction
|
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- generated_from_trainer
|
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- dataset_size:63
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- loss:MatryoshkaLoss
|
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- loss:MultipleNegativesRankingLoss
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base_model: keepitreal/vietnamese-sbert
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widget:
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- source_sentence: Samsung Galaxy S22 Ultra
|
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sentences:
|
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- Điện thoại camera 108MP
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- Điện thoại RAM 12GB
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- Điện thoại có zoom quang học 10x
|
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- source_sentence: Google Pixel 8 Pro
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sentences:
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- Điện thoại có jack cắm tai nghe 3.5mm
|
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- Điện thoại có bộ nhớ trong 256GB
|
|
- Điện thoại chụp ảnh đẹp
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- source_sentence: Google Pixel 8
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sentences:
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- Điện thoại màn hình 120Hz
|
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- Điện thoại giá rẻ
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- Điện thoại Android mới nhất
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- source_sentence: JBL Reflect Flow Pro
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sentences:
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- iPhone mới nhất
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- Điện thoại màn hình cong
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- Điện thoại có loa Harman Kardon
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- source_sentence: Asus ROG Phone 7
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sentences:
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- Điện thoại có bút
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- Điện thoại chơi game
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- Điện thoại có đèn flash kép
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pipeline_tag: sentence-similarity
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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
|
|
- cosine_precision@3
|
|
- cosine_precision@5
|
|
- cosine_precision@10
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|
- cosine_recall@1
|
|
- cosine_recall@3
|
|
- cosine_recall@5
|
|
- cosine_recall@10
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|
- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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model-index:
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- name: SentenceTransformer based on keepitreal/vietnamese-sbert
|
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results:
|
|
- task:
|
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type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 768
|
|
type: dim_768
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.0
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.0
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.09523809523809523
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.0
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.25679948860544627
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.1598639455782313
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.17696777071484332
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 512
|
|
type: dim_512
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.0
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.42857142857142855
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.5714285714285714
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.7142857142857143
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.0
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.14285714285714285
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.1142857142857143
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.07142857142857142
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.0
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.42857142857142855
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.5714285714285714
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.7142857142857143
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.3358736991627618
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.21564625850340136
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.22075481533609612
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 256
|
|
type: dim_256
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.0
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.0
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.0
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.0
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.0
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.0
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.22155623379830594
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.11564625850340135
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.13073998125841443
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 128
|
|
type: dim_128
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.0
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.14285714285714285
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.42857142857142855
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.0
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.047619047619047616
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.042857142857142864
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.0
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.14285714285714285
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.42857142857142855
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.18057284162953233
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.10374149659863945
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.11943368484517551
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 64
|
|
type: dim_64
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.14285714285714285
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.14285714285714285
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.09523809523809523
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.05714285714285715
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.14285714285714285
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.2857142857142857
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.5714285714285714
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.32106066086016677
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.24801587301587302
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.2591176744402551
|
|
name: Cosine Map@100
|
|
---
|
|
|
|
# SentenceTransformer based on keepitreal/vietnamese-sbert
|
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) 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:** [keepitreal/vietnamese-sbert](https://huggingface.co/keepitreal/vietnamese-sbert) <!-- at revision a9467ef2ef47caa6448edeabfd8e5e5ce0fa2a23 -->
|
|
- **Maximum Sequence Length:** 256 tokens
|
|
- **Output Dimensionality:** 768 dimensions
|
|
- **Similarity Function:** Cosine Similarity
|
|
- **Training Dataset:**
|
|
- json
|
|
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
|
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
|
)
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Direct Usage (Sentence Transformers)
|
|
|
|
First install the Sentence Transformers library:
|
|
|
|
```bash
|
|
pip install -U sentence-transformers
|
|
```
|
|
|
|
Then you can load this model and run inference.
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
# Download from the 🤗 Hub
|
|
model = SentenceTransformer("zxcvo/phone-search-model")
|
|
# Run inference
|
|
sentences = [
|
|
'Asus ROG Phone 7',
|
|
'Điện thoại chơi game',
|
|
'Điện thoại có đèn flash kép',
|
|
]
|
|
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 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 |
|
|
| cosine_accuracy@3 | 0.2857 | 0.4286 | 0.0 | 0.1429 | 0.2857 |
|
|
| cosine_accuracy@5 | 0.2857 | 0.5714 | 0.2857 | 0.2857 | 0.2857 |
|
|
| cosine_accuracy@10 | 0.5714 | 0.7143 | 0.5714 | 0.4286 | 0.5714 |
|
|
| cosine_precision@1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 |
|
|
| cosine_precision@3 | 0.0952 | 0.1429 | 0.0 | 0.0476 | 0.0952 |
|
|
| cosine_precision@5 | 0.0571 | 0.1143 | 0.0571 | 0.0571 | 0.0571 |
|
|
| cosine_precision@10 | 0.0571 | 0.0714 | 0.0571 | 0.0429 | 0.0571 |
|
|
| cosine_recall@1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1429 |
|
|
| cosine_recall@3 | 0.2857 | 0.4286 | 0.0 | 0.1429 | 0.2857 |
|
|
| cosine_recall@5 | 0.2857 | 0.5714 | 0.2857 | 0.2857 | 0.2857 |
|
|
| cosine_recall@10 | 0.5714 | 0.7143 | 0.5714 | 0.4286 | 0.5714 |
|
|
| **cosine_ndcg@10** | **0.2568** | **0.3359** | **0.2216** | **0.1806** | **0.3211** |
|
|
| cosine_mrr@10 | 0.1599 | 0.2156 | 0.1156 | 0.1037 | 0.248 |
|
|
| cosine_map@100 | 0.177 | 0.2208 | 0.1307 | 0.1194 | 0.2591 |
|
|
|
|
<!--
|
|
## 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: 63 training samples
|
|
* Columns: <code>positive</code> and <code>anchor</code>
|
|
* Approximate statistics based on the first 63 samples:
|
|
| | positive | anchor |
|
|
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
|
|
| type | string | string |
|
|
| details | <ul><li>min: 4 tokens</li><li>mean: 6.9 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.86 tokens</li><li>max: 12 tokens</li></ul> |
|
|
* Samples:
|
|
| positive | anchor |
|
|
|:-----------------------------------------------------------|:-----------------------------------------|
|
|
| <code>Google Pixel 8</code> | <code>Điện thoại Android mới nhất</code> |
|
|
| <code>Samsung Galaxy S22 Ultra</code> | <code>Điện thoại có sạc không dây</code> |
|
|
| <code>Samsung Galaxy Note 20 Ultra đi kèm bút S Pen</code> | <code>Điện thoại có bút</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,
|
|
256,
|
|
128,
|
|
64
|
|
],
|
|
"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
|
|
- `gradient_accumulation_steps`: 16
|
|
- `learning_rate`: 2e-05
|
|
- `num_train_epochs`: 4
|
|
- `bf16`: True
|
|
- `load_best_model_at_end`: True
|
|
|
|
#### 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`: 8
|
|
- `per_gpu_train_batch_size`: None
|
|
- `per_gpu_eval_batch_size`: None
|
|
- `gradient_accumulation_steps`: 16
|
|
- `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`: linear
|
|
- `lr_scheduler_kwargs`: {}
|
|
- `warmup_ratio`: 0.0
|
|
- `warmup_steps`: 0
|
|
- `log_level`: passive
|
|
- `log_level_replica`: warning
|
|
- `log_on_each_node`: True
|
|
- `logging_nan_inf_filter`: True
|
|
- `save_safetensors`: True
|
|
- `save_on_each_node`: False
|
|
- `save_only_model`: False
|
|
- `restore_callback_states_from_checkpoint`: False
|
|
- `no_cuda`: False
|
|
- `use_cpu`: False
|
|
- `use_mps_device`: False
|
|
- `seed`: 42
|
|
- `data_seed`: None
|
|
- `jit_mode_eval`: False
|
|
- `use_ipex`: False
|
|
- `bf16`: True
|
|
- `fp16`: False
|
|
- `fp16_opt_level`: O1
|
|
- `half_precision_backend`: auto
|
|
- `bf16_full_eval`: False
|
|
- `fp16_full_eval`: False
|
|
- `tf32`: None
|
|
- `local_rank`: 0
|
|
- `ddp_backend`: None
|
|
- `tpu_num_cores`: None
|
|
- `tpu_metrics_debug`: False
|
|
- `debug`: []
|
|
- `dataloader_drop_last`: False
|
|
- `dataloader_num_workers`: 0
|
|
- `dataloader_prefetch_factor`: None
|
|
- `past_index`: -1
|
|
- `disable_tqdm`: False
|
|
- `remove_unused_columns`: True
|
|
- `label_names`: None
|
|
- `load_best_model_at_end`: 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
|
|
- `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`: batch_sampler
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
|
</details>
|
|
|
|
### Training Logs
|
|
| Epoch | Step | 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 |
|
|
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
|
|
| **1.0** | **1** | **0.3188** | **0.3971** | **0.3073** | **0.1945** | **0.2442** |
|
|
| 2.0 | 2 | 0.3209 | 0.3886 | 0.2545 | 0.1838 | 0.3194 |
|
|
| 3.0 | 3 | 0.2542 | 0.3359 | 0.2391 | 0.1838 | 0.3211 |
|
|
| 4.0 | 4 | 0.2568 | 0.3359 | 0.2216 | 0.1806 | 0.3211 |
|
|
|
|
* The bold row denotes the saved checkpoint.
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.0
|
|
- Sentence Transformers: 3.3.1
|
|
- Transformers: 4.41.2
|
|
- PyTorch: 2.5.1+cu124
|
|
- Accelerate: 1.2.1
|
|
- 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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