|
--- |
|
base_model: BAAI/bge-base-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:76 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: ply C Tris pH8.0 Dextran Trehalose dNTPS Na2 SO4 Triton X-100 |
|
sentences: |
|
- NE |
|
- Assignee |
|
- ID NO |
|
- source_sentence: Certain isothermal amplification methods are able to amplify |
|
a target nucleic acid from trace levels to very high 25 and detectable levels within a matter of minutes. Such |
|
isothermal methods, e.g., Nicking and Extension Amplifi cation Reaction (NEAR), allow |
|
users to detect a particular nucleotide sequence in trace amounts, facilitating |
|
point-of care testing and increasing the accessibility and speed of diagnostics. |
|
Streptococcus pyogenes is the causative agent of group A streptococcal (GAS) infections such as pharyngitis, impe |
|
tigo, and life-threatening necrotizing fasciitis and sepsis. The most common GAS infection, pharyngitis, can be |
|
diagnosed by collecting a throat swab sample from a patient and culturing the |
|
sample under conditions that would enable bacterial, specifically S. pyogenes, growth, |
|
which takes 2-3 days. Culturing S. pyogenes is an accurate and reliable |
|
method of diagnosing GAS, but it is slow. A 2-3 day delay in prescribing appropriate antibiotic |
|
treatment can result in unnecessary patient suffering and potentially the onset |
|
oflife threatening conditions such as rheumatic fever. In the recent past, biochemical |
|
methods have been developed to detect S. pyogenes, but these methods do not provide the necessary |
|
characteristics to be deployed in the point-of-care setting, either due to a lack |
|
of sensitivity or time to result (speed). Accordingly, a highly sensitive and |
|
rapid qualitative assay for the detection and diagnosis of a S. pyogenes infection |
|
is desired. |
|
sentences: |
|
- ABSTRACT |
|
- TACTGTTCCTGTTTGA |
|
- BACKGROUND |
|
- source_sentence: W02010/141940 12/2010 CA (US) |
|
sentences: |
|
- WO |
|
- DESCRIPTION OF DRAWINGS |
|
- SUMMARY |
|
- source_sentence: 9 6 nucleotide sequence at least 80, 85 or 95% identity to SEQ |
|
sentences: |
|
- '2' |
|
- NEAR |
|
- Ph. Dissertation published Jan. 1, 2008. (Year |
|
- source_sentence: One potential inhibitor of the NEAR technology is human gDNA. When a throat swab sample is collected from a |
|
patient symptomatic for GAS infection, it is possible that human gDNA |
|
is also collected on the swab (from immune cells such as white blood cells |
|
or from local epithelial cells). In order to assess the impact that human gDNA |
|
has on the GAS assay, a study was performed using three different levels of |
|
GAS gDNA (25, 250 and 1000 copies) in the presence of 0, 10, 50, 100, 250, 500 or 1000 |
|
ng of human gDNA. As shown in FIG. 6, the presence of human gDNA does have an impact on |
|
GAS assay performance, and the impact is GAS target concentration dependent. When |
|
there is a low copy number of target GAS present in the reaction, 10 ng of |
|
human gDNA or more significantly inhibits the assay. At 250 copies of GAS |
|
target, the impact of 10 ng of 60 human gDNA is less, and at 1,000 copies of |
|
GAS target, the effect of 10 ng of human gDNA on the assay is significantly less. In |
|
fact, when 1,000 copies of target is present in the assay, up to 100 ng |
|
of human gDNA can be tolerated, albeit with a slower amplification speed and reduced |
|
fluorescence signal. Testing of the 501 (IC only) mix showed a more robust response to human gDNA. When the 501 mix was |
|
tested in the presence of O copies of target GAS and up to US 10,329,601 B2 |
|
23 1,000 ng of human gDNA, the assay still produced a clearly positive signal |
|
at 500 ng of human gDNA (even at 1,000 ng of human gDNA the fluorescence signal was still above |
|
background). Other Embodiments It is to be understood that while the invention |
|
has been described in conjunction with the detailed description 24 thereof, the foregoing description is intended to illustrate |
|
and not limit the scope of the invention, which is defined by the scope of the |
|
appended claims. Other aspects, advantages, and modifications are within the scope of |
|
the following claims. |
|
sentences: |
|
- '75' |
|
- TGTAGCTGACACCACCAAGCTACA |
|
- Impact of Human Genomic DNA (gDNA) on GAS Assay |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 768 |
|
type: dim_768 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.875 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29166666666666663 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.875 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8202007889556063 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7604166666666666 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7604166666666666 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.875 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.875 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29166666666666663 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17500000000000002 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.875 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.875 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.810892117584935 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.75 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.75 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.625 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.875 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.875 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.625 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29166666666666663 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17500000000000002 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.625 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.875 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.875 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8057993287946483 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7447916666666666 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7447916666666666 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.75 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.875 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.75 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29166666666666663 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.75 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.875 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8608566009043177 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8166666666666667 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8166666666666667 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.75 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.875 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 1.0 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 1.0 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.75 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29166666666666663 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.2 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.1 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.75 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.875 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 1.0 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 1.0 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8608566009043177 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.8166666666666667 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.8166666666666667 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
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). 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 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': 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("kr-manish/fine-tuned-bge-base-raw_pdf-v1") |
|
# Run inference |
|
sentences = [ |
|
'One potential inhibitor of the NEAR technology is human gDNA. When a throat swab sample is collected from a patient symptomatic for GAS infection, it is possible that human gDNA is also collected on the swab (from immune cells such as white blood cells or from local epithelial cells). In order to assess the impact that human gDNA has on the GAS assay, a study was performed using three different levels of GAS gDNA (25, 250 and 1000 copies) in the presence of 0, 10, 50, 100, 250, 500 or 1000 ng of human gDNA. As shown in FIG. 6, the presence of human gDNA does have an impact on GAS assay performance, and the impact is GAS target concentration dependent. When there is a low copy number of target GAS present in the reaction, 10 ng of human gDNA or more significantly inhibits the assay. At 250 copies of GAS target, the impact of 10 ng of 60 human gDNA is less, and at 1,000 copies of GAS target, the effect of 10 ng of human gDNA on the assay is significantly less. In fact, when 1,000 copies of target is present in the assay, up to 100 ng of human gDNA can be tolerated, albeit with a slower amplification speed and reduced fluorescence signal. Testing of the 501 (IC only) mix showed a more robust response to human gDNA. When the 501 mix was tested in the presence of O copies of target GAS and up to US 10,329,601 B2 23 1,000 ng of human gDNA, the assay still produced a clearly positive signal at 500 ng of human gDNA (even at 1,000 ng of human gDNA the fluorescence signal was still above background). Other Embodiments It is to be understood that while the invention has been described in conjunction with the detailed description 24 thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.', |
|
'Impact of Human Genomic DNA (gDNA) on GAS Assay', |
|
'TGTAGCTGACACCACCAAGCTACA', |
|
] |
|
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 |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.625 | |
|
| cosine_accuracy@3 | 0.875 | |
|
| cosine_accuracy@5 | 1.0 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.625 | |
|
| cosine_precision@3 | 0.2917 | |
|
| cosine_precision@5 | 0.2 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.625 | |
|
| cosine_recall@3 | 0.875 | |
|
| cosine_recall@5 | 1.0 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8202 | |
|
| cosine_mrr@10 | 0.7604 | |
|
| **cosine_map@100** | **0.7604** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:---------| |
|
| cosine_accuracy@1 | 0.625 | |
|
| cosine_accuracy@3 | 0.875 | |
|
| cosine_accuracy@5 | 0.875 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.625 | |
|
| cosine_precision@3 | 0.2917 | |
|
| cosine_precision@5 | 0.175 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.625 | |
|
| cosine_recall@3 | 0.875 | |
|
| cosine_recall@5 | 0.875 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8109 | |
|
| cosine_mrr@10 | 0.75 | |
|
| **cosine_map@100** | **0.75** | |
|
|
|
#### 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 | 0.625 | |
|
| cosine_accuracy@3 | 0.875 | |
|
| cosine_accuracy@5 | 0.875 | |
|
| cosine_accuracy@10 | 1.0 | |
|
| cosine_precision@1 | 0.625 | |
|
| cosine_precision@3 | 0.2917 | |
|
| cosine_precision@5 | 0.175 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.625 | |
|
| cosine_recall@3 | 0.875 | |
|
| cosine_recall@5 | 0.875 | |
|
| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.8058 | |
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| cosine_mrr@10 | 0.7448 | |
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| **cosine_map@100** | **0.7448** | |
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#### Information Retrieval |
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* 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 | 0.75 | |
|
| cosine_accuracy@3 | 0.875 | |
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| cosine_accuracy@5 | 1.0 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.75 | |
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| cosine_precision@3 | 0.2917 | |
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| cosine_precision@5 | 0.2 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.75 | |
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| cosine_recall@3 | 0.875 | |
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| cosine_recall@5 | 1.0 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.8609 | |
|
| cosine_mrr@10 | 0.8167 | |
|
| **cosine_map@100** | **0.8167** | |
|
|
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#### 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 | 0.75 | |
|
| cosine_accuracy@3 | 0.875 | |
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| cosine_accuracy@5 | 1.0 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.75 | |
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| cosine_precision@3 | 0.2917 | |
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| cosine_precision@5 | 0.2 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.75 | |
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| cosine_recall@3 | 0.875 | |
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| cosine_recall@5 | 1.0 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.8609 | |
|
| cosine_mrr@10 | 0.8167 | |
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| **cosine_map@100** | **0.8167** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
|
|
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### Training Dataset |
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|
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#### Unnamed Dataset |
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|
|
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* Size: 76 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 148.89 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.54 tokens</li><li>max: 18 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------| |
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| <code>1 (AGACTCCATATGGAGTCTAGC CAAACAGGAACA); a reverse template comprising a nucleotide sequence having at least 80, 85 or 95% identity 55 (CGACTCCATATGGAGTC to GAAAGCAATCTGAGGA); and a probe oligonucleotide comprising a nucleotide sequence at least 80, 85 or 95%</code> | <code>to SEQ ID NO</code> | |
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| <code>One potential inhibitor of the NEAR technology is human gDNA. When a throat swab sample is collected from a patient symptomatic for GAS infection, it is possible that human gDNA is also collected on the swab (from immune cells such as white blood cells or from local epithelial cells). In order to assess the impact that human gDNA has on the GAS assay, a study was performed using three different levels of GAS gDNA (25, 250 and 1000 copies) in the presence of 0, 10, 50, 100, 250, 500 or 1000 ng of human gDNA. As shown in FIG. 6, the presence of human gDNA does have an impact on GAS assay performance, and the impact is GAS target concentration dependent. When there is a low copy number of target GAS present in the reaction, 10 ng of human gDNA or more significantly inhibits the assay. At 250 copies of GAS target, the impact of 10 ng of 60 human gDNA is less, and at 1,000 copies of GAS target, the effect of 10 ng of human gDNA on the assay is significantly less. In fact, when 1,000 copies of target is present in the assay, up to 100 ng of human gDNA can be tolerated, albeit with a slower amplification speed and reduced fluorescence signal. Testing of the 501 (IC only) mix showed a more robust response to human gDNA. When the 501 mix was tested in the presence of O copies of target GAS and up to US 10,329,601 B2 23 1,000 ng of human gDNA, the assay still produced a clearly positive signal at 500 ng of human gDNA (even at 1,000 ng of human gDNA the fluorescence signal was still above background). Other Embodiments It is to be understood that while the invention has been described in conjunction with the detailed description 24 thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.</code> | <code>Impact of Human Genomic DNA (gDNA) on GAS Assay</code> | |
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| <code>25C-4x lyophilization mix, single tube assay format; 50C-2x lyophilization mix, single tube assay format; 25T-4x lyophilization mix, target assay only; 50T-2x lyophilization mix, target assay only; 25I- 4x lyophilization mix, IC assay only; 50I-2x lyophilization mix, internal control (IC) assay only. The GAS NEAR assay can be run on an appropriate platform. For example, the GAS NEAR assay can be run on an Al ere i platform (www.alere.com/ww/en/product-details/ alere-i-strep-a.html). AnA!ere i system consists of an instru ment which provides heating, mixing and fluorescence detection with automated result output, and a set of dispos ables, consisting of the sample receiver (where the elution buffer is stored), a test base ( containing two tubes of lyophilized NEAR reagents) and a transfer device ( designed to transfer 100 µI aliquots of eluted sample from the sample receiver to each of the two tubes containing lyophilized NEAR reagents located in the test base). Suitable dispos ables for use with the Alere i GAS NEAR test include those 60 described in, for example U.S. application Ser. No. 13/242, 999, incorporated herein by reference in its entirety. In addition to containing the reagents necessary for driv ing the GAS NEAR assay, the lyophilized material also contains the lytic agent for GAS, the protein plyC; therefore, 65 GAS lysis does not occur until the lyophilized material is re-suspended. In some cases, the lyophilized material does not contain a lytic agent for GAS, for example, in some US 10,329,601 B2 19 examples, the lyophilized material does not contain the protein plyC. The elution buffer was designed to allow for the rapid release of GAS organisms from clinical sample throat swabs as well as to provide the necessary salts for driving the NEAR assay (both MgSO4 and (NH4)2SO4), in 5 a slightly basic environment. In some examples, the elution buffer also includes an anti-microbial agent or preservative (e.g., ProClin® 950). For the present examples, GAS assay was performed as a two tube assay-a GAS target specific assay in one tube, and an internal control (IC) assay in a second tube (tested side by side on the Alere i).</code> | <code>annotated as follows</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 3e-05 |
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- `num_train_epochs`: 40 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.2 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 3e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 40 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.2 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:--------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0 | 0 | - | 0.2103 | 0.1702 | 0.1888 | 0.1783 | 0.1815 | |
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| 1.0 | 1 | - | 0.2102 | 0.1702 | 0.1888 | 0.1783 | 0.1815 | |
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| 2.0 | 2 | - | 0.2104 | 0.1705 | 0.1890 | 0.1797 | 0.1815 | |
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| 3.0 | 3 | - | 0.2841 | 0.1733 | 0.2524 | 0.1997 | 0.2465 | |
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| 4.0 | 5 | - | 0.3285 | 0.2747 | 0.2865 | 0.3281 | 0.2901 | |
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| 5.0 | 6 | - | 0.3311 | 0.3045 | 0.2996 | 0.3930 | 0.3001 | |
|
| 6.0 | 7 | - | 0.3948 | 0.3808 | 0.3193 | 0.4576 | 0.3147 | |
|
| 7.0 | 9 | - | 0.5308 | 0.4366 | 0.4222 | 0.5445 | 0.4367 | |
|
| 8.0 | 10 | 3.233 | 0.5352 | 0.5240 | 0.5224 | 0.5867 | 0.4591 | |
|
| 9.0 | 11 | - | 0.5438 | 0.5864 | 0.5228 | 0.6519 | 0.6475 | |
|
| 10.0 | 13 | - | 0.6540 | 0.5906 | 0.6554 | 0.6684 | 0.6511 | |
|
| 11.0 | 14 | - | 0.6585 | 0.6020 | 0.6684 | 0.6857 | 0.6621 | |
|
| 12.0 | 15 | - | 0.6632 | 0.6661 | 0.6798 | 0.7063 | 0.6685 | |
|
| 13.0 | 17 | - | 0.7292 | 0.7210 | 0.6971 | 0.7396 | 0.7062 | |
|
| 14.0 | 18 | - | 0.7396 | 0.7375 | 0.7229 | 0.8333 | 0.7068 | |
|
| 15.0 | 19 | - | 0.75 | 0.7438 | 0.7021 | 0.8333 | 0.7083 | |
|
| 16.0 | 20 | 1.4113 | 0.7604 | 0.7292 | 0.7042 | 0.8229 | 0.7104 | |
|
| 17.0 | 21 | - | 0.7542 | 0.7262 | 0.7095 | 0.8229 | 0.7158 | |
|
| 18.0 | 22 | - | 0.7438 | 0.7344 | 0.7054 | 0.8167 | 0.7188 | |
|
| 19.0 | 23 | - | 0.8063 | 0.7344 | 0.7125 | 0.8125 | 0.7021 | |
|
| 20.0 | 25 | - | 0.7958 | 0.7344 | 0.7262 | 0.8125 | 0.7333 | |
|
| 21.0 | 26 | - | 0.8021 | 0.7344 | 0.7470 | 0.8095 | 0.7333 | |
|
| 22.0 | 27 | - | 0.8021 | 0.7344 | 0.7470 | 0.8095 | 0.7333 | |
|
| 23.0 | 29 | - | 0.8021 | 0.7344 | 0.7470 | 0.8095 | 0.7438 | |
|
| 24.0 | 30 | 0.6643 | 0.8021 | 0.7448 | 0.7470 | 0.8125 | 0.7438 | |
|
| 25.0 | 31 | - | 0.8125 | 0.7448 | 0.7470 | 0.8125 | 0.7604 | |
|
| 26.0 | 33 | - | 0.8125 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
| 27.0 | 34 | - | 0.8125 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
| 28.0 | 35 | - | 0.8125 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
| **29.0** | **37** | **-** | **0.8167** | **0.7448** | **0.75** | **0.8167** | **0.7604** | |
|
| 30.0 | 38 | - | 0.8167 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
| 31.0 | 39 | - | 0.8167 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
| 32.0 | 40 | 0.4648 | 0.8167 | 0.7448 | 0.75 | 0.8167 | 0.7604 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.42.4 |
|
- PyTorch: 2.3.1+cu121 |
|
- Accelerate: 0.32.1 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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