metadata
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: >-
Enzalutamide ( brand name Xtandi ) is a synthetic non-steroidal
antiandrogen ( NSAA ) which was developed by the pharmaceutical company
Medivation for the treatment of metastatic , castration-resistant prostate
cancer . Medivation has reported up to an 89 % decrease in serum prostate
specific antigen ( PSA ) levels after a month of taking the drug .
Research suggests that enzalutamide may also be effective in the treatment
of certain types of breast cancer . In August 2012 , the United States (
U.S. ) Food and Drug Administration ( FDA ) approved enzalutamide for the
treatment of castration-resistant prostate cancer .
sentences:
- what type of cancer is enzalutamide
- who is simon cho
- who is dr william farone
- source_sentence: >-
Sohel Rana is a Bangladeshi footballer who plays as a midfielder . He
currently plays for Sheikh Jamal Dhanmondi Club .
sentences:
- who is sohel rana
- who is olympicos
- who is roberto laserna
- source_sentence: >-
Qarah Qayeh ( قره قيه , also Romanized as Qareh Qīyeh ) is a village in
Chaharduli Rural District , Keshavarz District , Shahin Dezh County , West
Azerbaijan Province , Iran . At the 2006 census , its population was 465 ,
in 93 families .
sentences:
- what was the knoxville riot
- what language is kbif
- where is qarah qayeh
- source_sentence: >-
Martin Severin Janus From ( 8 April 1828 -- 6 May 1895 ) was a Danish
chess master . Born in Nakskov , From received his first education at
the grammar school of Nykøbing Falster . He entered the army as a
volunteer during the Prussian-Danish War ( Schleswig-Holstein War of
Succession ) , where he served in the brigade of Major-General Olaf Rye
and partook in the Battle of Fredericia on July 6 , 1849 . After the war
From settled in Copenhagen . He was employed by the Statistical Bureau ,
where he met Magnus Oscar Møllerstrøm , then the strongest chess player in
Copenhagen . Next , he worked in the central office for prison management
, and in 1890 he became an inspector of the penitentiary of Christianshavn
. In 1891 he received the order Ridder af Dannebrog ( `` Knight of the
Danish cloth '' , i.e. flag of Denmark ) , which is the second highest of
Danish orders . In 1895 Severin From died of cancer . He is interred at
Vestre Cemetery , Copenhagen .
sentences:
- when did martin from die
- what is hymenoxys lemmonii
- where is macomb square il
- source_sentence: >-
The Recession of 1937 -- 1938 was an economic downturn that occurred
during the Great Depression in the United States . By the spring of 1937
, production , profits , and wages had regained their 1929 levels .
Unemployment remained high , but it was slightly lower than the 25 % rate
seen in 1933 . The American economy took a sharp downturn in mid-1937 ,
lasting for 13 months through most of 1938 . Industrial production
declined almost 30 percent and production of durable goods fell even
faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 .
Manufacturing output fell by 37 % from the 1937 peak and was back to 1934
levels . Producers reduced their expenditures on durable goods , and
inventories declined , but personal income was only 15 % lower than it had
been at the peak in 1937 . In most sectors , hourly earnings continued to
rise throughout the recession , which partly compensated for the reduction
in the number of hours worked . As unemployment rose , consumers
expenditures declined , thereby leading to further cutbacks in production
.
sentences:
- when did the great depression peak in the u.s. economy?
- what is tom mount's specialty
- where is poulton
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: 0.9175
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9565
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.965
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.977
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9175
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31883333333333325
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19300000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09770000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9175
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9565
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.965
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.977
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9481552613003054
name: Cosine Ndcg@10
- type: cosine_ndcg@100
value: 0.9518775022084042
name: Cosine Ndcg@100
- type: cosine_mrr@10
value: 0.938853373015873
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9396524466438041
name: Cosine Map@100
SentenceTransformer based on BAAI/bge-base-en-v1.5
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("MugheesAwan11/bge-base-climate_fever-dataset-10k-2k-v1")
# Run inference
sentences = [
'The Recession of 1937 -- 1938 was an economic downturn that occurred during the Great Depression in the United States . By the spring of 1937 , production , profits , and wages had regained their 1929 levels . Unemployment remained high , but it was slightly lower than the 25 % rate seen in 1933 . The American economy took a sharp downturn in mid-1937 , lasting for 13 months through most of 1938 . Industrial production declined almost 30 percent and production of durable goods fell even faster . Unemployment jumped from 14.3 % in 1937 to 19.0 % in 1938 . Manufacturing output fell by 37 % from the 1937 peak and was back to 1934 levels . Producers reduced their expenditures on durable goods , and inventories declined , but personal income was only 15 % lower than it had been at the peak in 1937 . In most sectors , hourly earnings continued to rise throughout the recession , which partly compensated for the reduction in the number of hours worked . As unemployment rose , consumers expenditures declined , thereby leading to further cutbacks in production .',
'when did the great depression peak in the u.s. economy?',
'where is poulton',
]
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 | 0.9175 |
cosine_accuracy@3 | 0.9565 |
cosine_accuracy@5 | 0.965 |
cosine_accuracy@10 | 0.977 |
cosine_precision@1 | 0.9175 |
cosine_precision@3 | 0.3188 |
cosine_precision@5 | 0.193 |
cosine_precision@10 | 0.0977 |
cosine_recall@1 | 0.9175 |
cosine_recall@3 | 0.9565 |
cosine_recall@5 | 0.965 |
cosine_recall@10 | 0.977 |
cosine_ndcg@10 | 0.9482 |
cosine_ndcg@100 | 0.9519 |
cosine_mrr@10 | 0.9389 |
cosine_map@100 | 0.9397 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 10,000 training samples
- Columns:
positive
andanchor
- Approximate statistics based on the first 1000 samples:
positive anchor type string string details - min: 2 tokens
- mean: 116.45 tokens
- max: 512 tokens
- min: 4 tokens
- mean: 8.6 tokens
- max: 19 tokens
- Samples:
positive anchor Professor Maurice Cockrill , RA , FBA ( 8 October 1936 -- 1 December 2013 ) was a British painter and poet . Born in Hartlepool , County Durham , he studied at Wrexham School of Art , north east Wales , then Denbigh Technical College and later the University of Reading from 1960 -- 64 . In Liverpool , where he lived for nearly twenty years from 1964 , he taught at Liverpool College of Art and Liverpool Polytechnic . He was a central figure in Liverpool 's artistic life , regularly exhibiting at the Walker Art Gallery , before his departure for London in 1982 . Cockrill 's Liverpool work was in line with that of John Baum , Sam Walsh and Adrian Henri , employing Pop and Photo-Realist styles , but later he moved towards Romantic Expressionism , as it was shown in his retrospective at the Walker Art Gallery , Liverpool in 1995 . His poetry was published in magazines such as
Ambit '' and
Poetry Review '' . He was formerly the Keeper of the Royal Academy , and as such managed the RA Schools of the Establishment as well as being a member of the Board and Executive Committee .who was maurice cockrill
Nowa Dąbrowa -LSB-
nowa-dom
browa -RSB- is a village in the administrative district of Gmina Kwilcz , within Międzychód County , Greater Poland Voivodeship , in west-central Poland . It lies approximately 16 km south-east of Międzychód and 59 km west of the regional capital Poznań . The village has a population of 40 .where is nowa dbrowa poland
Hymenoxys lemmonii is a species of flowering plant in the daisy family known by the common names Lemmon 's rubberweed , Lemmon 's bitterweed , and alkali hymenoxys . It is native to the western United States in and around the Great Basin in Utah , Nevada , northern California , and southeastern Oregon . Hymenoxys lemmonii is a biennial or perennial herb with one or more branching stems growing erect to a maximum height near 50 centimeters ( 20 inches ) . It produces straight , dark green leaves up to 9 centimeters ( 3.6 inches ) long and divided into a number of narrow , pointed lobes . The foliage and stem may be hairless to quite woolly . The daisy-like flower head is generally at least 1.5 centimeters ( 0.6 inches ) wide , with a center of 50 -- 125 thick golden disc florets and a shaggy fringe of 9 -- 12 golden ray florets . The species is named for John Gill Lemmon , husband of prominent American botanist Sarah Plummer Lemmon .
what is hymenoxys lemmonii
- Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768 ], "matryoshka_weights": [ 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: epochper_device_train_batch_size
: 32per_device_eval_batch_size
: 16learning_rate
: 2e-05num_train_epochs
: 1lr_scheduler_type
: cosinewarmup_ratio
: 0.1bf16
: Truetf32
: Trueload_best_model_at_end
: Trueoptim
: adamw_torch_fusedbatch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: epochprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_steps
: -1lr_scheduler_type
: cosinelr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Truelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torch_fusedoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falsebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
Training Logs
Epoch | Step | Training Loss | dim_768_cosine_map@100 |
---|---|---|---|
0.0319 | 10 | 0.1626 | - |
0.0639 | 20 | 0.1168 | - |
0.0958 | 30 | 0.0543 | - |
0.1278 | 40 | 0.1227 | - |
0.1597 | 50 | 0.061 | - |
0.1917 | 60 | 0.0537 | - |
0.2236 | 70 | 0.0693 | - |
0.2556 | 80 | 0.1115 | - |
0.2875 | 90 | 0.0541 | - |
0.3195 | 100 | 0.0774 | - |
0.3514 | 110 | 0.0639 | - |
0.3834 | 120 | 0.0639 | - |
0.4153 | 130 | 0.0567 | - |
0.4473 | 140 | 0.0385 | - |
0.4792 | 150 | 0.0452 | - |
0.5112 | 160 | 0.0641 | - |
0.5431 | 170 | 0.042 | - |
0.5751 | 180 | 0.0243 | - |
0.6070 | 190 | 0.0405 | - |
0.6390 | 200 | 0.062 | - |
0.6709 | 210 | 0.0366 | - |
0.7029 | 220 | 0.0399 | - |
0.7348 | 230 | 0.0382 | - |
0.7668 | 240 | 0.0387 | - |
0.7987 | 250 | 0.0575 | - |
0.8307 | 260 | 0.0391 | - |
0.8626 | 270 | 0.0776 | - |
0.8946 | 280 | 0.0258 | - |
0.9265 | 290 | 0.0493 | - |
0.9585 | 300 | 0.037 | - |
0.9904 | 310 | 0.0499 | - |
1.0 | 313 | - | 0.9397 |
- 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
@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}
}