BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("anishareddyalla/bge-base-aws-case-studies")
# Run inference
sentences = [
    'CU Coventry’s bachelor of science in cloud computing course officially began in September 2020 and has already seen success from the program’s industry-driven framework. Overview Validate technical skills and cloud expertise to grow your career and business. Learn more » Get Started on AWS services using AWS Academy Learner Labs Build your cloud skills at your own pace, on your own time, and completely for free. Looking ahead, Coventry University Group plans to expand bachelor of science degree in cloud computing courses to its campuses in London and Wroclaw. “The ability to have hands-on experience with AWS services—the same ones that companies use in the real world—is invaluable,” said Tomasz, a student of the Cloud Computing Course. “Once we join the workforce, we can apply our skill sets and hit the ground running. ” Türkçe English Students successfully engaging in the program graduate with in-demand skills for careers in the cloud, including valuable experience with AWS services through AWS Academy Learner Labs. AWS Academy provides higher education institutions with ready-to-teach cloud computing curriculum to prepare students for AWS Certifications, which validate technical skills and cloud expertise for in-demand cloud jobs. “The most important thing is for the modules to reflect what the industry needs. We want students to add value to the global workforce,” says Flood. Taking advantage of AWS Education Programs, CU Coventry’s BSc degree in cloud computing innovates on AWS to track the IT industry’s rapid pace. AWS Certification Deutsch Coventry University Group is based in the United Kingdom with more than 30,000 students and more than 200 undergraduate and postgraduate degrees across its schools, faculties, and campuses. Tiếng Việt AWS Training and Certification Italiano ไทย Outcome | Looking to the Future of Coventry University Group’s Cloud Computing Program Learn more » Increases employability Coventry University Group used AWS Education Programs to create a comprehensive and flexible degree to help students meet growing IT industry cloud skills demand. Both the 3-year bachelor of science degree in cloud computing and its accelerated version were developed in collaboration with AWS. These programs were designed by working backwards from the cloud skills employers are currently seeking in the UK and across the global labor market. “The approach gave us insights into what skill gaps were lacking in the industry. From there, we designed the courses, with the AWS team providing helpful inputs,” says Flood. “For example, the AWS team pointed out that there was an industry need for serverless computing skills, and we integrated that into our curriculum. ” Português.',
    "How does CU Coventry's Bachelor of Science in Cloud Computing program incorporate AWS services and industry-driven insights to prepare students for in-demand cloud jobs?",
    "How does RUSH University System for Health use HECAP and Amazon HealthLake to address healthcare disparities and improve patient outcomes for residents of Chicago's West Side?",
]
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]

Evaluation

Metrics

Information Retrieval

Dataset: dim_768
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4597
cosine_accuracy@3	0.8024
cosine_accuracy@5	0.8992
cosine_accuracy@10	0.9597
cosine_precision@1	0.4597
cosine_precision@3	0.2675
cosine_precision@5	0.1798
cosine_precision@10	0.096
cosine_recall@1	0.4597
cosine_recall@3	0.8024
cosine_recall@5	0.8992
cosine_recall@10	0.9597
cosine_ndcg@10	0.7185
cosine_mrr@10	0.6395
cosine_map@100	0.6409

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4677
cosine_accuracy@3	0.7984
cosine_accuracy@5	0.8952
cosine_accuracy@10	0.9597
cosine_precision@1	0.4677
cosine_precision@3	0.2661
cosine_precision@5	0.179
cosine_precision@10	0.096
cosine_recall@1	0.4677
cosine_recall@3	0.7984
cosine_recall@5	0.8952
cosine_recall@10	0.9597
cosine_ndcg@10	0.7214
cosine_mrr@10	0.6433
cosine_map@100	0.6448

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4597
cosine_accuracy@3	0.7984
cosine_accuracy@5	0.9113
cosine_accuracy@10	0.9637
cosine_precision@1	0.4597
cosine_precision@3	0.2661
cosine_precision@5	0.1823
cosine_precision@10	0.0964
cosine_recall@1	0.4597
cosine_recall@3	0.7984
cosine_recall@5	0.9113
cosine_recall@10	0.9637
cosine_ndcg@10	0.7207
cosine_mrr@10	0.6411
cosine_map@100	0.6422

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4315
cosine_accuracy@3	0.7581
cosine_accuracy@5	0.8831
cosine_accuracy@10	0.9476
cosine_precision@1	0.4315
cosine_precision@3	0.2527
cosine_precision@5	0.1766
cosine_precision@10	0.0948
cosine_recall@1	0.4315
cosine_recall@3	0.7581
cosine_recall@5	0.8831
cosine_recall@10	0.9476
cosine_ndcg@10	0.6948
cosine_mrr@10	0.6125
cosine_map@100	0.6146

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4032
cosine_accuracy@3	0.746
cosine_accuracy@5	0.871
cosine_accuracy@10	0.9516
cosine_precision@1	0.4032
cosine_precision@3	0.2487
cosine_precision@5	0.1742
cosine_precision@10	0.0952
cosine_recall@1	0.4032
cosine_recall@3	0.746
cosine_recall@5	0.871
cosine_recall@10	0.9516
cosine_ndcg@10	0.68
cosine_mrr@10	0.592
cosine_map@100	0.5935

Training Details

Training Dataset

Unnamed Dataset

Size: 2,231 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 3 tokens
mean: 434.98 tokens
max: 512 tokens

min: 13 tokens
mean: 33.46 tokens
max: 65 tokens

	positive	anchor
type	string	string
details	min: 3 tokens mean: 434.98 tokens max: 512 tokens	min: 13 tokens mean: 33.46 tokens max: 65 tokens

Samples:

positive	anchor
`”.`	`What specific event or topic is being discussed in the given information?`
On AWS, Rackspace solved a major industry challenge with a solution that saved time, cut costs, and reduced complexity for its customers and itself. “When things go wrong, customers expect Rackspace to step in and act swiftly to solve their problem,” says Prewitt. “Using AWS Systems Manager, we can do that much more quickly. ” Português Rackspace needed a solution that could run both on premises and on the cloud. “We wanted one tool to use across the full suite of solutions that Rackspace manages,” says Gignac. AWS Systems Manager met that requirement and offered programmability. “That’s a key differentiator of AWS: we can use AWS Systems Manager to run shell scripts on individual VMs and do advanced orchestration,” Gignac continues. .	`How did Rackspace use AWS Systems Manager to solve major industry challenges and improve their ability to quickly address customer issues?`
Français Shortly after the onset of the pandemic in early 2020, Valant began offering a telehealth solution to provide virtual capabilities to practices and their patients. The solution was based on a digital communications platform that lacked a multi-user experience and many other requested features. “The platform we used offered peer-to-peer video only, and we needed group capabilities, chat, screen and file sharing, and a whiteboard,” says James Jay, chief technology officer at Valant Medical Solutions. “In behavioral health, it’s common to have parents, spouses, or other guests attend sessions, and we saw a significant demand from practices for multi-user functionality, as well as other features critical to engaging effectively with patients. We also had strong demand to integrate co-payment collection into telehealth check-in workflows in advance of sessions. ” 2023 Amazon Simple Email Service Español by using voice, video, messaging, and automated reminders Valant Medical Solutions, Inc. provides electronic health record software to behavioral health providers and practices. To add enhanced telehealth capabilities and improve patient communication, the company turned to Amazon Web Services to add capabilities in voice, video, messaging, and email through AWS Communication Developer Services to build a new telehealth solution for more than 2,500 behavioral health practices. AWS Communication Developer Services (CDS) are cloud-based APIs and SDKs that help builders add communication capabilities into their apps or websites with minimal coding. 日本語 Valant Medical Solutions, Inc. designs and develops web-based electronic health record (EHR) software to help behavioral health providers and practices streamline administration tasks and improve patient outcomes. More than 20,000 behavioral health professionals in group and solo private practices across the United States use the Valant platform to treat individuals seeking behavioral healthcare. The Valant IO system has extensive capabilities to enable providers to deliver value-based care through measurement-based assessment and ongoing outcome assessments. 5% Get Started 한국어 Overview	Opportunity

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 32
per_device_eval_batch_size: 16
gradient_accumulation_steps: 16
learning_rate: 2e-05
num_train_epochs: 10
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

Click to expand

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: 16
eval_accumulation_steps: None
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
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
eval_on_start: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

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
0.9143	4	-	0.6055	0.6308	0.646	0.5623	0.6339
1.8286	8	-	0.6255	0.6505	0.6517	0.5791	0.6558
2.2857	10	2.0293	-	-	-	-	-
2.9714	13	-	0.6096	0.6472	0.6471	0.5935	0.6490
3.8857	17	-	0.6125	0.6410	0.6468	0.6020	0.6422
4.5714	20	0.5008	-	-	-	-	-
4.8	21	-	0.6156	0.6351	0.6409	0.6014	0.6391
5.9429	26	-	0.6143	0.6350	0.6367	0.6015	0.6406
6.8571	30	0.2964	0.6167	0.6371	0.6390	0.5981	0.6387
8.0	35	-	0.6138	0.6364	0.6391	0.5986	0.6392
8.9143	39	-	0.6173	0.6378	0.6389	0.6021	0.6394
9.1429	40	0.2382	0.6161	0.6376	0.6391	0.5982	0.6398
0.9143	4	-	0.6273	0.6535	0.6608	0.5949	0.66
1.8286	8	-	0.6177	0.6439	0.6515	0.6074	0.6508
2.2857	10	0.554	-	-	-	-	-
2.9714	13	-	0.6070	0.6300	0.6339	0.5923	0.6366
3.8857	17	-	0.6071	0.6332	0.6362	0.5976	0.6362
4.5714	20	0.2694	-	-	-	-	-
4.8	21	-	0.6124	0.6397	0.6455	0.5988	0.6404
5.9429	26	-	0.6155	0.6411	0.6446	0.6007	0.6429
6.8571	30	0.1746	0.6167	0.6429	0.6467	0.5942	0.6424
8.0	35	-	0.6166	0.6398	0.6462	0.5928	0.6429
8.9143	39	-	0.6108	0.6426	0.6448	0.5943	0.6432
9.1429	40	0.1419	0.6146	0.6422	0.6448	0.5935	0.6409

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

@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

@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

@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}
}

anishareddyalla
/

bge-base-aws-case-studies

BGE base Financial Matryoshka

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Model tree for anishareddyalla/bge-base-aws-case-studies

Evaluation results