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---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_ndcg@100
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10000
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Cashless transactions such as online transactions, credit card
transactions, and mobile wallet are becoming more popular in financial transactions
nowadays. With increased number of such cashless transaction, number of fraudulent
transactions are also increasing. Fraud can be distinguished by analyzing spending
behavior of customers (users) from previous transaction data. If any deviation
is noticed in spending behavior from available patterns, it is possibly of fraudulent
transaction. To detect fraud behavior, bank and credit card companies are using
various methods of data mining such as decision tree, rule based mining, neural
network, fuzzy clustering approach, hidden markov model or hybrid approach of
these methods. Any of these methods is applied to find out normal usage pattern
of customers (users) based on their past activities. The objective of this paper
is to provide comparative study of different techniques to detect fraud.
sentences:
- how fraud detection is done
- deep cnn image analysis definition
- what are intermediate representations
- source_sentence: 'We present a novel convolutional neural network (CNN) based approach
for one-class classification. The idea is to use a zero centered Gaussian noise
in the latent space as the pseudo-negative class and train the network using the
cross-entropy loss to learn a good representation as well as the decision boundary
for the given class. A key feature of the proposed approach is that any pre-trained
CNN can be used as the base network for one-class classification. The proposed
one-class CNN is evaluated on the UMDAA-02 Face, Abnormality-1001, and FounderType-200
datasets. These datasets are related to a variety of one-class application problems
such as user authentication, abnormality detection, and novelty detection. Extensive
experiments demonstrate that the proposed method achieves significant improvements
over the recent state-of-the-art methods. The source code is available at: github.com/otkupjnoz/oc-cnn.'
sentences:
- what is one class convolutional neural networks
- what is the use for sic carbide
- what is bayesopt
- source_sentence: 'While the field of educational data mining (EDM) has generated
many innovations for improving educational software and student learning, the
mining of student data has recently come under a great deal of scrutiny. Many
stakeholder groups, including public officials, media outlets, and parents, have
voiced concern over the privacy of student data and their efforts have garnered
national attention. The momentum behind and scrutiny of student privacy has made
it increasingly difficult for EDM applications to transition from academia to
industry. Based on experience as academic researchers transitioning into industry,
we present three primary areas of concern related to student privacy in practice:
policy, corporate social responsibility, and public opinion. Our discussion will
describe the key challenges faced within these categories, strategies for overcoming
them, and ways in which the academic EDM community can support the adoption of
innovative technologies in large-scale production.'
sentences:
- what is the purpose of artificial intelligence firewalls
- genetic crossover operator
- why is privacy important for students
- source_sentence: Autonomous vehicle research has been prevalent for well over a
decade but only recently has there been a small amount of research conducted on
the human interaction that occurs in autonomous vehicles. Although functional
software and sensor technology is essential for safe operation, which has been
the main focus of autonomous vehicle research, handling all elements of human
interaction is also a very salient aspect of their success. This paper will provide
an overview of the importance of human vehicle interaction in autonomous vehicles,
while considering relevant related factors that are likely to impact adoption.
Particular attention will be given to prior research conducted on germane areas
relating to control in the automobile, in addition to the different elements that
are expected to affect the likelihood of success for these vehicles initially
developed for human operation. This paper will also include a discussion of the
limited research conducted to consider interactions with humans and the current
state of published functioning software and sensor technology that exists.
sentences:
- when are human interaction in autonomous vehicles
- what is the purpose of evaluator guidelines
- definition of collaborative filtering
- source_sentence: J. Appl. Phys. 111, 07E328 (2012) A single-solenoid pulsed-magnet
system for single-crystal scattering studies Rev. Sci. Instrum. 83, 035101 (2012)
Solution to the problem of E-cored coil above a layered half-space using the method
of truncated region eigenfunction expansion J. Appl. Phys. 111, 07E717 (2012)
Array of 12 coils to measure the position, alignment, and sensitivity of magnetic
sensors over temperature J. Appl. Phys. 111, 07E501 (2012) Skin effect suppression
for Cu/CoZrNb multilayered inductor J. Appl. Phys. 111, 07A501 (2012)
sentences:
- which inductor can be used for multilayer scattering studies?
- which patch antennas use a microstrip line
- what kind of interaction is in mobile
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: 0.4995
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7685
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8205
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.873
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4995
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2561666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16410000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08730000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4995
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7685
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8205
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.873
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7001286552732331
name: Cosine Ndcg@10
- type: cosine_ndcg@100
value: 0.7182557103824586
name: Cosine Ndcg@100
- type: cosine_mrr@10
value: 0.6433079365079365
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6472568310800184
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:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("MugheesAwan11/bge-base-scidocs-dataset-10k-2k-e1")
# Run inference
sentences = [
'J. Appl. Phys. 111, 07E328 (2012) A single-solenoid pulsed-magnet system for single-crystal scattering studies Rev. Sci. Instrum. 83, 035101 (2012) Solution to the problem of E-cored coil above a layered half-space using the method of truncated region eigenfunction expansion J. Appl. Phys. 111, 07E717 (2012) Array of 12 coils to measure the position, alignment, and sensitivity of magnetic sensors over temperature J. Appl. Phys. 111, 07E501 (2012) Skin effect suppression for Cu/CoZrNb multilayered inductor J. Appl. Phys. 111, 07A501 (2012)',
'which inductor can be used for multilayer scattering studies?',
'what kind of interaction is in mobile',
]
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]
```
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<details><summary>Click to expand</summary>
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## 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 | 0.4995 |
| cosine_accuracy@3 | 0.7685 |
| cosine_accuracy@5 | 0.8205 |
| cosine_accuracy@10 | 0.873 |
| cosine_precision@1 | 0.4995 |
| cosine_precision@3 | 0.2562 |
| cosine_precision@5 | 0.1641 |
| cosine_precision@10 | 0.0873 |
| cosine_recall@1 | 0.4995 |
| cosine_recall@3 | 0.7685 |
| cosine_recall@5 | 0.8205 |
| cosine_recall@10 | 0.873 |
| cosine_ndcg@10 | 0.7001 |
| cosine_ndcg@100 | 0.7183 |
| cosine_mrr@10 | 0.6433 |
| **cosine_map@100** | **0.6473** |
<!--
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,000 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: 210.86 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.51 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------|
| <code>This article introduces a sentiment analysis approach that adopts the way humans read, interpret, and extract sentiment from text. Our motivation builds on the assumption that human interpretation should lead to the most accurate assessment of sentiment in text. We call this automated process Human Reading for Sentiment (HRS). Previous research in sentiment analysis has produced many frameworks that can fit one or more of the HRS aspects; however, none of these methods has addressed them all in one approach. HRS provides a meta-framework for developing new sentiment analysis methods or improving existing ones. The proposed framework provides a theoretical lens for zooming in and evaluating aspects of any sentiment analysis method to identify gaps for improvements towards matching the human reading process. Key steps in HRS include the automation of humans low-level and high-level cognitive text processing. This methodology paves the way towards the integration of psychology with computational linguistics and machine learning to employ models of pragmatics and discourse analysis for sentiment analysis. HRS is tested with two state-of-the-art methods; one is based on feature engineering, and the other is based on deep learning. HRS highlighted the gaps in both methods and showed improvements for both.</code> | <code>definition of sentiment analysis</code> |
| <code>Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent beneets for retrieval performance, and it is diicult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in IN-QUERY to evaluate diierent types of association thesauri, and thesauri constructed for a variety of collections.</code> | <code>what is association thesaurus</code> |
| <code>The choice of transfer functions may strongly influence complexity and performance of neural networks. Although sigmoidal transfer functions are the most common there is no a priori reason why models based on such functions should always provide optimal decision borders. A large number of alternative transfer functions has been described in the literature. A taxonomy of activation and output functions is proposed, and advantages of various non-local and local neural transfer functions are discussed. Several less-known types of transfer functions and new combinations of activation/output functions are described. Universal transfer functions, parametrized to change from localized to delocalized type, are of greatest interest. Other types of neural transfer functions discussed here include functions with activations based on nonEuclidean distance measures, bicentral functions, formed from products or linear combinations of pairs of sigmoids, and extensions of such functions making rotations of localized decision borders in highly dimensional spaces practical. Nonlinear input preprocessing techniques are briefly described, offering an alternative way to change the shapes of decision borders.</code> | <code>types of neural transfer functions</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
],
"matryoshka_weights": [
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_map@100 |
|:-------:|:-------:|:-------------:|:----------------------:|
| 0.0319 | 10 | 0.6581 | - |
| 0.0639 | 20 | 0.4842 | - |
| 0.0958 | 30 | 0.3555 | - |
| 0.1278 | 40 | 0.2398 | - |
| 0.1597 | 50 | 0.2917 | - |
| 0.1917 | 60 | 0.2286 | - |
| 0.2236 | 70 | 0.1903 | - |
| 0.2556 | 80 | 0.1832 | - |
| 0.2875 | 90 | 0.2899 | - |
| 0.3195 | 100 | 0.1744 | - |
| 0.3514 | 110 | 0.2148 | - |
| 0.3834 | 120 | 0.1379 | - |
| 0.4153 | 130 | 0.2123 | - |
| 0.4473 | 140 | 0.2445 | - |
| 0.4792 | 150 | 0.1481 | - |
| 0.5112 | 160 | 0.1392 | - |
| 0.5431 | 170 | 0.2218 | - |
| 0.5751 | 180 | 0.2225 | - |
| 0.6070 | 190 | 0.2874 | - |
| 0.6390 | 200 | 0.1927 | - |
| 0.6709 | 210 | 0.2469 | - |
| 0.7029 | 220 | 0.1915 | - |
| 0.7348 | 230 | 0.1711 | - |
| 0.7668 | 240 | 0.1982 | - |
| 0.7987 | 250 | 0.1783 | - |
| 0.8307 | 260 | 0.2016 | - |
| 0.8626 | 270 | 0.211 | - |
| 0.8946 | 280 | 0.1962 | - |
| 0.9265 | 290 | 0.1867 | - |
| 0.9585 | 300 | 0.195 | - |
| 0.9904 | 310 | 0.2161 | - |
| **1.0** | **313** | **-** | **0.6473** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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