This is a sentence-transformers model finetuned from BAAI/bge-base-en. 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

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("vijay-delete/finetuned-transaction-classification-bge-base-en")
# Run inference
sentences = [
    '\nName : Innovative Design Systems GmbH\nCategory: Consulting & Professional Fees, Computer Hardware & Software\nDepartment: Product Development\nLocation: Berlin, Germany\nAmount: 1423.77\nCard: Tech Innovation Fund\nTrip Name: unknown\n',
    '\nName : Learning Innovators Group\nCategory: Consulting & Professional Fees, Training & Development\nDepartment: Human Resources\nLocation: Chicago, IL\nAmount: 359.99\nCard: Skills Enhancement Program\nTrip Name: unknown\n',
    '\nName : Cultural Cafe & Event Space\nCategory: Meals & Entertainment, Consulting & Professional Fees\nDepartment: Cultural Initiatives\nLocation: Paris, France\nAmount: 764.32\nCard: Art Networking Event\nTrip Name: unknown\n',
]
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

Triplet

Dataset: bge-base-en-train
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9971
dot_accuracy	0.0029
manhattan_accuracy	0.9971
euclidean_accuracy	0.9971
max_accuracy	0.9971

Triplet

Dataset: bge-base-en-eval
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	1.0
euclidean_accuracy	1.0
max_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 685 training samples
Columns: sentence and label

Approximate statistics based on the first 685 samples:

	sentence	label
type	string	int
details	min: 36 tokens mean: 42.06 tokens max: 48 tokens	0: ~6.57% 1: ~7.88% 2: ~6.72% 3: ~7.01% 4: ~7.74% 5: ~7.45% 6: ~6.42% 7: ~7.01% 8: ~7.15% 9: ~7.15% 10: ~7.74% 11: ~7.15% 12: ~8.18% 13: ~5.84%

Samples:

sentence	label
`[object Object]Name : Summit Advisory Partners[object Object]Category: Consulting & Professional Fees, Training & Development[object Object]Department: Strategic Insights[object Object]Location: New York, NY[object Object]Amount: 985.45[object Object]Card: Leadership Development Program[object Object]Trip Name: unknown[object Object]`	`0`
`[object Object]Name : CloudWave Enterprises[object Object]Category: Telecommunications, Cloud Services & Hosting[object Object]Department: Infrastructure Management[object Object]Location: London, UK[object Object]Amount: 1450.95[object Object]Card: Network Optimization Project[object Object]Trip Name: unknown[object Object]`	`1`
`[object Object]Name : AeroTech Communication Solutions[object Object]Category: Telecommunications, Cloud Services & Hosting[object Object]Department: IT Operations[object Object]Location: Singapore[object Object]Amount: 915.45[object Object]Card: Networking Infrastructure Upgrade[object Object]Trip Name: unknown[object Object]`	`1`

Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

Size: 171 evaluation samples
Columns: sentence and label

Approximate statistics based on the first 171 samples:

	sentence	label
type	string	int
details	min: 37 tokens mean: 42.08 tokens max: 48 tokens	0: ~8.77% 1: ~7.02% 2: ~5.26% 3: ~7.60% 4: ~7.02% 5: ~8.77% 6: ~5.85% 7: ~4.09% 8: ~9.36% 9: ~7.60% 10: ~7.60% 11: ~5.26% 12: ~7.60% 13: ~8.19%

Samples:

sentence	label
`[object Object]Name : YOLOSTREAM MEDIA[object Object]Category: Subscriptions & Memberships, Cloud Services & Hosting[object Object]Department: Marketing & IT[object Object]Location: New York, NY[object Object]Amount: 927.4[object Object]Card: Digital Strategy Fund[object Object]Trip Name: unknown[object Object]`	`9`
`[object Object]Name : Learning Innovators Group[object Object]Category: Consulting & Professional Fees, Training & Development[object Object]Department: Human Resources[object Object]Location: Chicago, IL[object Object]Amount: 359.99[object Object]Card: Skills Enhancement Program[object Object]Trip Name: unknown[object Object]`	`6`
`[object Object]Name : Globetraining Solutions[object Object]Category: Subscriptions & Memberships, Training & Development[object Object]Department: Human Resources[object Object]Location: Newark, NJ[object Object]Amount: 523.47[object Object]Card: Employee Skill Enhancement[object Object]Trip Name: unknown[object Object]`	`6`

Loss: BatchSemiHardTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1
eval_accumulation_steps: None
torch_empty_cache_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: 5
max_steps: -1
lr_scheduler_type: linear
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: 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: False
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
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	bge-base-en-eval_max_accuracy	bge-base-en-train_max_accuracy
0	0	-	-	-	0.9650
2.3256	100	4.6242	4.0409	-	0.9956
4.6512	200	4.2332	3.9329	-	0.9971
5.0	215	-	-	1.0	-

Framework Versions

Python: 3.11.13
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.6.0+cu124
Accelerate: 1.8.1
Datasets: 2.14.4
Tokenizers: 0.20.3

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",
}

BatchSemiHardTripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}