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 dimensions
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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("GaniduA/bge-finetuned-olscience")
# Run inference
sentences = [
    'Discuss the principles and process of electrolysis, including the conventions adopted in electrolysis.',
    'The development of artificial intelligence has significantly impacted the tech industry, leading to advancements in machine learning and natural language processing.',
    "In the movie 'Inception', directed by Christopher Nolan, the plot revolves around a skilled thief who is given a chance at redemption if he can successfully perform inception by planting an idea into someone's subconscious.",
]
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

Binary Classification

Dataset: eval
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	1.0
cosine_accuracy_threshold	0.0571
cosine_f1	1.0
cosine_f1_threshold	0.0571
cosine_precision	1.0
cosine_recall	1.0
cosine_ap	1.0
cosine_mcc	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 34,969 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 6 tokens
mean: 17.43 tokens
max: 209 tokens
min: 3 tokens
mean: 25.94 tokens
max: 335 tokens
min: 0.0
mean: 0.25
max: 1.0

Samples:

sentence_0	sentence_1	label
`How does the reaction of zinc with copper sulfate demonstrate a single displacement reaction?`	`Julius Caesar crossed the Rubicon River in 49 BC, which led to a chain of events culminating in the Roman Civil War.`	`0.0`
`How do you investigate the effect of tightening a screw on the moment of force required to rotate a stick?`	`Explore the depths of the ocean with a team of deep-sea divers searching for mythical sea creatures and undiscovered shipwrecks.`	`0.0`
`Describe the operation of a photodiode in optical sensing.`	`A photodiode converts light into an electrical current by generating electron-hole pairs when exposed to light, used in optical sensing and communication applications.`	`1.0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 2
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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}
tp_size: 0
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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	eval_cosine_ap
0.0366	20	-	0.9892
0.0731	40	-	0.9978
0.1097	60	-	0.9989
0.1463	80	-	0.9997
0.1828	100	-	0.9999
0.2194	120	-	0.9998
0.2559	140	-	0.9998
0.2925	160	-	0.9998
0.3291	180	-	0.9998
0.3656	200	-	0.9999
0.4022	220	-	0.9998
0.4388	240	-	0.9999
0.4753	260	-	1.0000
0.5119	280	-	1.0000
0.5484	300	-	1.0000
0.5850	320	-	1.0000
0.6216	340	-	1.0000
0.6581	360	-	1.0000
0.6947	380	-	1.0
0.7313	400	-	1.0000
0.7678	420	-	1.0
0.8044	440	-	1.0
0.8410	460	-	1.0000
0.8775	480	-	1.0
0.9141	500	0.0199	1.0000
0.9506	520	-	1.0
0.9872	540	-	1.0000
1.0	547	-	1.0000
1.0238	560	-	1.0000
1.0603	580	-	1.0000
1.0969	600	-	1.0000
1.1335	620	-	1.0000
1.1700	640	-	1.0
1.2066	660	-	1.0000
1.2431	680	-	1.0000
1.2797	700	-	1.0000
1.3163	720	-	1.0000
1.3528	740	-	1.0000
1.3894	760	-	1.0
1.4260	780	-	1.0
1.4625	800	-	1.0000
1.4991	820	-	1.0
1.5356	840	-	1.0000
1.5722	860	-	1.0000
1.6088	880	-	1.0
1.6453	900	-	1.0
1.6819	920	-	1.0
1.7185	940	-	1.0000
1.7550	960	-	1.0000
1.7916	980	-	1.0000
1.8282	1000	0.0012	1.0000
1.8647	1020	-	1.0
1.9013	1040	-	1.0
1.9378	1060	-	1.0
1.9744	1080	-	1.0
2.0	1094	-	1.0

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.50.3
PyTorch: 2.6.0+cu124
Accelerate: 1.5.2
Datasets: 3.5.0
Tokenizers: 0.21.1

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",
}

	sentence_0	sentence_1	label
type	string	string	float
details	min: 6 tokens mean: 17.43 tokens max: 209 tokens	min: 3 tokens mean: 25.94 tokens max: 335 tokens	min: 0.0 mean: 0.25 max: 1.0