metadata

base_model: BAAI/bge-m3
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:24000
  - loss:TripletLoss
  - loss:MultipleNegativesRankingLoss
  - loss:CoSENTLoss
widget:
  - source_sentence: Fjerne trapp i mur utvendig
    sentences:
      - 'query: Montere nytt dusjkabinett'
      - 'query: installasjon av beslag på portaldører'
      - 'query: fjerne utvendig trapp i mur'
  - source_sentence: Drenering av hus
    sentences:
      - Grave drenering rundt huset
      - Legge nytt tak
      - Renovere kjeller
  - source_sentence: Montere 9 IKEA Pax garderobeskap med innhold
    sentences:
      - Sette opp IKEA garderobeskap
      - Bygge nytt bad på loftet
      - Montere kjøkkeninnredning
  - source_sentence: Flettverksgjerde
    sentences:
      - Planlegge kjøkkenløsning i nytt hus
      - Galvaniserte gjerdestolper montert i Asker
      - Service på Volvo V70 2004
  - source_sentence: Takoppløft + div oppussing av enebolig
    sentences:
      - Feilsøking av elektriske problemer i huset
      - Støpe fundament til garasje
      - Renovering av enebolig
model-index:
  - name: SentenceTransformer based on BAAI/bge-m3
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: test triplet evaluation
          type: test-triplet-evaluation
        metrics:
          - type: cosine_accuracy
            value: 0.9725158562367865
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.02748414376321353
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9725158562367865
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9725158562367865
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9725158562367865
            name: Max Accuracy

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-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-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("ostoveland/test9")
# Run inference
sentences = [
    'Takoppløft + div oppussing av enebolig',
    'Renovering av enebolig',
    'Feilsøking av elektriske problemer i huset',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

Dataset: test-triplet-evaluation
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9725
dot_accuracy	0.0275
manhattan_accuracy	0.9725
euclidean_accuracy	0.9725
max_accuracy	0.9725

Training Details

Training Datasets

Unnamed Dataset

Size: 8,000 training samples
Columns: sentence_0, sentence_1, and sentence_2

Approximate statistics based on the first 1000 samples:

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 3 tokens mean: 9.81 tokens max: 32 tokens	min: 4 tokens mean: 7.7 tokens max: 18 tokens	min: 3 tokens mean: 7.14 tokens max: 31 tokens

Samples:

sentence_0	sentence_1	sentence_2
`Varmekabler i gang`	`Legge varmekabler i entré`	`Installere gulvvarme i stue`
`Gulvsliping med lakkering`	`Slipe og lakke gulv`	`Legge nytt gulv`
`Graving til svømmebasseng og rundtliggende område`	`Grave til svømmebasseng og området rundt`	`Grave til hagedam`

Loss: TripletLoss with these parameters:

{
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Unnamed Dataset

Size: 8,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 4 tokens
mean: 9.57 tokens
max: 35 tokens

min: 6 tokens
mean: 11.8 tokens
max: 26 tokens

	sentence_0	sentence_1
type	string	string
details	min: 4 tokens mean: 9.57 tokens max: 35 tokens	min: 6 tokens mean: 11.8 tokens max: 26 tokens

Samples:

sentence_0	sentence_1
`Støping av 15,5m2 gulv 75mm tykkelse`	`query: støpe 15,5m2 gulv med 75mm tykkelse`
`montere gjerde rundt hagen`	`query: gjerde rundt hagen`
`rive gammel garasje`	`query: fjerning av gammel garasje`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Unnamed Dataset

Size: 8,000 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: 3 tokens
mean: 10.23 tokens
max: 48 tokens

min: 4 tokens
mean: 8.12 tokens
max: 20 tokens

min: 0.05
mean: 0.5
max: 0.95

	sentence_0	sentence_1	label
type	string	string	float
details	min: 3 tokens mean: 10.23 tokens max: 48 tokens	min: 4 tokens mean: 8.12 tokens max: 20 tokens	min: 0.05 mean: 0.5 max: 0.95

Samples:

sentence_0	sentence_1	label
`Utskiftning av vinduer og terassedører`	`Bytte vinduer og terassedører`	`0.95`
`Kjøkkenventilator`	`Installere kjøkkenapparater`	`0.35`
`Speil på treningsrom`	`Installere speil på bad`	`0.55`

Loss: CoSENTLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "pairwise_cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1
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: 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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	test-triplet-evaluation_max_accuracy
0.6667	500	5.2926	-
1.0	750	-	0.9725

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
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",
}

TripletLoss

@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}
}

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}
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}