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IlhamEbdesk/
bge-base-financial-matryoshka_test_my

Name: bge-base-financial-matryoshka_test_my
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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: my
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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("IlhamEbdesk/bge-base-financial-matryoshka_test_my")
# Run inference
sentences = [
    'Penyelaras kempen iklan adalah individu yang menyelaraskan semua aspek kempen iklan, termasuk jadual, pelaksanaan, dan laporan prestasi.',
    'Apakah itu penyelaras kempen iklan?',
    'Apakah itu pembuat roti?',
]
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	0.8226
cosine_accuracy@3	0.9769
cosine_accuracy@5	0.9871
cosine_accuracy@10	0.9974
cosine_precision@1	0.8226
cosine_precision@3	0.3256
cosine_precision@5	0.1974
cosine_precision@10	0.0997
cosine_recall@1	0.8226
cosine_recall@3	0.9769
cosine_recall@5	0.9871
cosine_recall@10	0.9974
cosine_ndcg@10	0.9255
cosine_mrr@10	0.901
cosine_map@100	0.9011

Information Retrieval

Dataset: dim_512
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8046
cosine_accuracy@3	0.9743
cosine_accuracy@5	0.9871
cosine_accuracy@10	0.9923
cosine_precision@1	0.8046
cosine_precision@3	0.3248
cosine_precision@5	0.1974
cosine_precision@10	0.0992
cosine_recall@1	0.8046
cosine_recall@3	0.9743
cosine_recall@5	0.9871
cosine_recall@10	0.9923
cosine_ndcg@10	0.9159
cosine_mrr@10	0.8896
cosine_map@100	0.89

Information Retrieval

Dataset: dim_256
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7892
cosine_accuracy@3	0.9666
cosine_accuracy@5	0.9743
cosine_accuracy@10	0.9871
cosine_precision@1	0.7892
cosine_precision@3	0.3222
cosine_precision@5	0.1949
cosine_precision@10	0.0987
cosine_recall@1	0.7892
cosine_recall@3	0.9666
cosine_recall@5	0.9743
cosine_recall@10	0.9871
cosine_ndcg@10	0.9046
cosine_mrr@10	0.8764
cosine_map@100	0.8771

Information Retrieval

Dataset: dim_128
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7481
cosine_accuracy@3	0.9409
cosine_accuracy@5	0.9537
cosine_accuracy@10	0.9692
cosine_precision@1	0.7481
cosine_precision@3	0.3136
cosine_precision@5	0.1907
cosine_precision@10	0.0969
cosine_recall@1	0.7481
cosine_recall@3	0.9409
cosine_recall@5	0.9537
cosine_recall@10	0.9692
cosine_ndcg@10	0.8765
cosine_mrr@10	0.845
cosine_map@100	0.8461

Information Retrieval

Dataset: dim_64
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7224
cosine_accuracy@3	0.8972
cosine_accuracy@5	0.9254
cosine_accuracy@10	0.9434
cosine_precision@1	0.7224
cosine_precision@3	0.2991
cosine_precision@5	0.1851
cosine_precision@10	0.0943
cosine_recall@1	0.7224
cosine_recall@3	0.8972
cosine_recall@5	0.9254
cosine_recall@10	0.9434
cosine_ndcg@10	0.8455
cosine_mrr@10	0.8127
cosine_map@100	0.8146

Training Details

Training Dataset

Unnamed Dataset

Size: 389 training samples
Columns: positive and anchor
Approximate statistics based on the first 1000 samples:
positive anchor
type string string
details
min: 27 tokens
mean: 61.59 tokens
max: 139 tokens
min: 8 tokens
mean: 15.26 tokens
max: 24 tokens

Samples:

positive	anchor
`Dokter adalah profesional medis yang mendiagnosis dan merawat penyakit serta cedera pasien.`	`Apa itu dokter?`
`Pereka sistem akuakultur adalah individu yang merancang dan membangunkan sistem untuk membiakkan ikan secara berkesan, termasuk reka bentuk kolam, sistem aliran air, dan pemantauan kualiti air.`	`Apakah itu pereka sistem akuakultur?`
`Ahli sejarah seni adalah individu yang mengkaji perkembangan seni sepanjang sejarah dan konteks sosial, politik, dan budaya yang mempengaruhi penciptaannya. Mereka bekerja di muzium, galeri, dan institusi akademik, menganalisis karya seni`	`Apakah itu ahli sejarah seni?`

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
tf32: False
load_best_model_at_end: True
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: 4
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: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: False
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: True
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	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
1.0	1	0.6375	0.7065	0.7339	0.5984	0.7483
2.0	3	0.8282	0.8712	0.8821	0.7994	0.8929
2.4615	4	0.8461	0.8771	0.89	0.8146	0.9011

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.1.2+cu121
Accelerate: 0.32.1
Datasets: 2.19.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	positive	anchor
type	string	string
details	min: 27 tokens mean: 61.59 tokens max: 139 tokens	min: 8 tokens mean: 15.26 tokens max: 24 tokens