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---
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:111
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Template la - Spy cepA s3062 F30 Sequence ( 5' /3') Oligo [ l AGACTCCATATGGAGTCTAGCCAAACAG500
nM GAACA (SEQ ID NO, 1) 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
sentences:
- (45) Date of Patent
- http
- ID
- source_sentence: :-"<-------t 40000 -1-----/-f-~~-----I 35000 -----+-IN----------
§ 30000 ----t+t---=~--- ~ 25000 ----~---++------t ~ 20000 -1----ff-r-ff.,.__----->t''n-\--------l
sentences:
- 45000 -------,-----=.....
- -~' ~-- -~<
- comprises
- source_sentence: 55 1. A composition comprising i) a forward template comprising
a nucleic acid sequence comprising a recognition region at the 3' end that is
complementary to the 3' end of the Streptococcus pyogenes (S. pyogenes) cell envelope
proteinase A 60 (cepA) gene antisense strand; a nicking enzyme bind ing site
and a nicking site upstream of said recognition region; and a stabilizing region
upstream of said nick ing site, the forward template comprising a nucleotide
sequence having at least 80, 85, or 95% identity to SEQ 65
sentences:
- ''' -- ,'' ,.,,,..,,,. _..,,,,.,,, .... ~-__ .... , , _,. ........-----.'
- What is claimed is
- annotated as follows
- source_sentence: 0 1 2 3 4 5 6 7 8 9 10 Time (minutes) FIG. 1 (Cont.)
sentences:
- ',-;.-'
- I I I I I I I I I
- (21) Appl. No.
- source_sentence: '~ " ''"-''-en 25000 1 ,.,,µ,· ,, · .,-,.. •~h • 1 (1) ,\ II J
} 7; . \ \(9,i, .,u, 4\:'
sentences:
- 80, 85, or 95% identity to SEQ ID NO
- u
- en 25000 I ' 'lJVL' • -. • . .,.. ""~" '' ' I Q) l!J "667 7 ..._7 ... -,
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: 0.0
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.07692307692307693
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.07692307692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.23076923076923078
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.02564102564102564
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.015384615384615385
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02307692307692308
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07692307692307693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.07692307692307693
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23076923076923078
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.10157463646252407
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.06227106227106227
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08137504276350917
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: 0.07692307692307693
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.07692307692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.23076923076923078
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.02564102564102564
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.015384615384615385
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02307692307692308
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07692307692307693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.07692307692307693
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23076923076923078
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.09595574046316672
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.05662393162393163
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.0744997471979569
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: 0.07692307692307693
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.07692307692307693
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.23076923076923078
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.02564102564102564
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.015384615384615385
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.02307692307692308
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07692307692307693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.07692307692307693
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23076923076923078
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.0981693666921052
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.05897435897435897
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08277736107354086
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.07692307692307693
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.23076923076923078
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.23076923076923078
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38461538461538464
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.07692307692307693
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.07692307692307693
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.04615384615384616
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.038461538461538464
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07692307692307693
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23076923076923078
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.23076923076923078
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.38461538461538464
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21938110224036803
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.1700854700854701
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1860790779646314
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.0
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.07692307692307693
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.15384615384615385
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3076923076923077
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.0
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.02564102564102564
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.03076923076923077
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.03076923076923077
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07692307692307693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15384615384615385
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3076923076923077
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1299580480538269
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.07628205128205127
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.10015432076692518
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/bge-base-raw_pdf_finetuned_vf1")
# Run inference
sentences = [
'~ " \'"-\'-en 25000 1 ,.,,µ,· ,, · .,-,.. •~h • 1 (1) ,\\ II J } 7; . \\ \\(9,i, .,u, 4\\:',
'en 25000 I \' \'lJVL\' • -. • . .,.. ""~" \'\' \' I Q) l!J "667 7 ..._7 ... -,',
'80, 85, or 95% identity to SEQ ID NO',
]
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 | 0.0 |
| cosine_accuracy@3 | 0.0769 |
| cosine_accuracy@5 | 0.0769 |
| cosine_accuracy@10 | 0.2308 |
| cosine_precision@1 | 0.0 |
| cosine_precision@3 | 0.0256 |
| cosine_precision@5 | 0.0154 |
| cosine_precision@10 | 0.0231 |
| cosine_recall@1 | 0.0 |
| cosine_recall@3 | 0.0769 |
| cosine_recall@5 | 0.0769 |
| cosine_recall@10 | 0.2308 |
| cosine_ndcg@10 | 0.1016 |
| cosine_mrr@10 | 0.0623 |
| **cosine_map@100** | **0.0814** |
#### 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 | 0.0 |
| cosine_accuracy@3 | 0.0769 |
| cosine_accuracy@5 | 0.0769 |
| cosine_accuracy@10 | 0.2308 |
| cosine_precision@1 | 0.0 |
| cosine_precision@3 | 0.0256 |
| cosine_precision@5 | 0.0154 |
| cosine_precision@10 | 0.0231 |
| cosine_recall@1 | 0.0 |
| cosine_recall@3 | 0.0769 |
| cosine_recall@5 | 0.0769 |
| cosine_recall@10 | 0.2308 |
| cosine_ndcg@10 | 0.096 |
| cosine_mrr@10 | 0.0566 |
| **cosine_map@100** | **0.0745** |
#### 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 | 0.0 |
| cosine_accuracy@3 | 0.0769 |
| cosine_accuracy@5 | 0.0769 |
| cosine_accuracy@10 | 0.2308 |
| cosine_precision@1 | 0.0 |
| cosine_precision@3 | 0.0256 |
| cosine_precision@5 | 0.0154 |
| cosine_precision@10 | 0.0231 |
| cosine_recall@1 | 0.0 |
| cosine_recall@3 | 0.0769 |
| cosine_recall@5 | 0.0769 |
| cosine_recall@10 | 0.2308 |
| cosine_ndcg@10 | 0.0982 |
| cosine_mrr@10 | 0.059 |
| **cosine_map@100** | **0.0828** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.0769 |
| cosine_accuracy@3 | 0.2308 |
| cosine_accuracy@5 | 0.2308 |
| cosine_accuracy@10 | 0.3846 |
| cosine_precision@1 | 0.0769 |
| cosine_precision@3 | 0.0769 |
| cosine_precision@5 | 0.0462 |
| cosine_precision@10 | 0.0385 |
| cosine_recall@1 | 0.0769 |
| cosine_recall@3 | 0.2308 |
| cosine_recall@5 | 0.2308 |
| cosine_recall@10 | 0.3846 |
| cosine_ndcg@10 | 0.2194 |
| cosine_mrr@10 | 0.1701 |
| **cosine_map@100** | **0.1861** |
#### 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 | 0.0 |
| cosine_accuracy@3 | 0.0769 |
| cosine_accuracy@5 | 0.1538 |
| cosine_accuracy@10 | 0.3077 |
| cosine_precision@1 | 0.0 |
| cosine_precision@3 | 0.0256 |
| cosine_precision@5 | 0.0308 |
| cosine_precision@10 | 0.0308 |
| cosine_recall@1 | 0.0 |
| cosine_recall@3 | 0.0769 |
| cosine_recall@5 | 0.1538 |
| cosine_recall@10 | 0.3077 |
| cosine_ndcg@10 | 0.13 |
| cosine_mrr@10 | 0.0763 |
| **cosine_map@100** | **0.1002** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 111 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 124.53 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.15 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------|
| <code>ply C Tris pH8.0 Dextran Trehalose dNTPS Na2SO4 Triton X-100 DTT TABLE 3 GAS Lyophilization Mix -Reagent Composition vl.0 v2.0 Strep A (Target) Lyo Conditions 500 nM F30 500 nM F30b.5om 100 nM R41m 100 nM R41m.lb.5om 200 nM MB4 FAM 200 nM MB4_ Fam 3.0. ug 5.0 ug 30U 0.7 ug 1 ug 1 ug 50mM 50 mM Dextran 150 Dextran 500 5% in 2x Iyo 5% in 2x Iyo 100 mM in 2x Iyo 100 mM in 2x Iyo 0.3 mM 0.3 mM 15 mM 22.5 mM 0.10% 0.10% 2mM 2mM Strep A (IC) Lyo Conditions</code> | <code>NE</code> |
| <code>CTGTTTG (SEQ ID NO, 5) To confirm that the targeted sequence was conserved among all GAS cepA sequences found in the public domain as well as unique to GAS, multiple sequence alignments and BLAST analyses were performed. Multiple alignment analysis of these sequences showed complete homology for the region of the gene targeted by the 3062 assay. Further, there are currently 24 complete GAS genomes (including whole genome shotgun sequence) available for sequence analysis in NCBI Genome. The cepA gene is present in all 24 genomes, and the 3062 target region within cepA is conserved among all 24 genomes. Upon BLAST analysis, it was confirmed that no other species contain significant homology to the 3062 target sequence. Assay Development As a reference, the reagent mixtures discussed below are</code> | <code>GCAATCTGAGGAGAGGCCATACTTGTTC</code> |
| <code>AGATTGC (SEQ ID NO, 4)</code> | <code>CAAACAGGAACAAGTATGGCCTCTCCTC</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`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 32
- `num_train_epochs`: 15
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
#### 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`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 32
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-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`: 15
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### 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 | - | 0.0747 | 0.0694 | 0.0681 | 0.1224 | 0.0705 |
| 1.0 | 1 | - | 0.0750 | 0.0694 | 0.0681 | 0.1224 | 0.0705 |
| 2.0 | 2 | - | 0.1008 | 0.0724 | 0.0696 | 0.0719 | 0.0710 |
| **3.0** | **3** | **-** | **0.1861** | **0.0828** | **0.0745** | **0.1002** | **0.0814** |
| 4.0 | 4 | - | 0.1711 | 0.0968 | 0.0825 | 0.0861 | 0.1001 |
| 5.0 | 6 | - | 0.1505 | 0.1140 | 0.1094 | 0.1534 | 0.1502 |
| 6.0 | 7 | - | 0.1222 | 0.1143 | 0.1108 | 0.1528 | 0.1520 |
| 7.0 | 8 | - | 0.1589 | 0.1536 | 0.1512 | 0.1513 | 0.1516 |
| 8.0 | 9 | - | 0.1561 | 0.1550 | 0.1531 | 0.1495 | 0.1520 |
| 9.0 | 10 | 1.8482 | 0.1565 | 0.1558 | 0.1544 | 0.1483 | 0.1522 |
| 10.0 | 12 | - | 0.1562 | 0.1551 | 0.1557 | 0.1416 | 0.1531 |
| 11.0 | 13 | - | 0.1561 | 0.1558 | 0.1562 | 0.1401 | 0.1533 |
| 12.0 | 14 | - | 0.1559 | 0.1559 | 0.1562 | 0.1402 | 0.1533 |
| 13.0 | 15 | - | 0.1861 | 0.0828 | 0.0745 | 0.1002 | 0.0814 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+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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