This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large-instruct. 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: intfloat/multilingual-e5-large-instruct
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 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: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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("sentence_transformers_model_id")
# Run inference
sentences = [
    '(1) \n2.2.8.1.(4) \n2.2.8.7.(1) \n2.4.2.3.(4) \nA-2.2.8.4.(1) \n3.1.13.1.(1) \n3.2.3.21.(1) \n3.2.5.16.',
    '(1) \n2.2.8.1.(4) \n2.2.8.7.(1) \n2.4.2.3.(4) \nA-2.2.8.4.(1) \n3.1.13.1.(1) \n3.2.3.21.(1) \n3.2.5.16.',
    '5) T h ec l e a r h e i g h ti na storage garage shall be not less than 2 m. \n6) Where garage floors or ramps are 600 mm or more above the adjacent ground \nor floor level, every opening through such floors and the perimeter of floors and ramps \nshall be provided with \na) a continuous curb not less than 140 mm high, a guard not less than 1 070 mm \nhigh, and a vehicle guardrail not less than 500 mm high conforming to \nSentence (7), or \nb) a full-height wall conforming to Sentence (7). \n7) Vehicle guardrails and full-height walls required in Sentence (6) shall \nbe designed and constructed to withstand the loading values stipulated in \nSentence 4.1.5.15.(1). \n8) Except for open-air storeys,e v e r y storey of a storage garage or repair garage located \nbelow grade shall be sprinklered. \n3.3.5.5. Repair Garage Separation \n1) A repair garage and any ancillary spaces serving it, including waiting rooms, \nreception rooms, tool and parts storage areas and supervisory office space, shall be \nseparated from other occupancies by a fire separation having a fire-resistance rating not \nless than 2 h. \n3.3.5.6. ',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 2,119 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: 5 tokens
mean: 169.27 tokens
max: 512 tokens
min: 5 tokens
mean: 169.27 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
Barrier-Free Design Principles. This Section contains minimum requirements for the design [object Object]of buildings that accommodate people with diverse abilities, across their lifespan, including, but not limited [object Object]to, people who use wheelchairs or other assistive mobility devices (e.g., walking aids, canes, crutches, braces, [object Object]prosthetics), people with personal care providers, people with hearing or vision loss, and people with service [object Object]animals, so they can access and use buildings. [object Object]	Barrier-Free Design Principles. This Section contains minimum requirements for the design [object Object]of buildings that accommodate people with diverse abilities, across their lifespan, including, but not limited [object Object]to, people who use wheelchairs or other assistive mobility devices (e.g., walking aids, canes, crutches, braces, [object Object]prosthetics), people with personal care providers, people with hearing or vision loss, and people with service [object Object]animals, so they can access and use buildings. [object Object]
(1) [object Object]and (2) could require installation of an automatic sprinkler system throughout all storeys of a building [object Object]regardless of options in Articles 3.2.2.20. to 3.2.2.92. to construct one or more storeys without installation of [object Object]sprinklers. It is the intent of the Code that all storeys below a storey in which an automatic sprinkler system [object Object]is installed should also be protected by an automatic sprinkler system to ensure that a fire in a lower storey [object Object]does not incapacitate the automatic sprinkler system or overwhelm an automatic sprinkler system in an upper [object Object]storey. Persons in an upper storey in which waivers or reductions of other fire safety systems are permitted [object Object]would be exposed to an increased risk from a fire on a lower storey. This concept also applies to situations [object Object]in which an automatic sprinkler system has been installed within a floor area in order to modify other safety [object Object]requirements applying within the floor area. If the uppermost storey or storeys of a building can be construc...	(1) [object Object]and (2) could require installation of an automatic sprinkler system throughout all storeys of a building [object Object]regardless of options in Articles 3.2.2.20. to 3.2.2.92. to construct one or more storeys without installation of [object Object]sprinklers. It is the intent of the Code that all storeys below a storey in which an automatic sprinkler system [object Object]is installed should also be protected by an automatic sprinkler system to ensure that a fire in a lower storey [object Object]does not incapacitate the automatic sprinkler system or overwhelm an automatic sprinkler system in an upper [object Object]storey. Persons in an upper storey in which waivers or reductions of other fire safety systems are permitted [object Object]would be exposed to an increased risk from a fire on a lower storey. This concept also applies to situations [object Object]in which an automatic sprinkler system has been installed within a floor area in order to modify other safety [object Object]requirements applying within the floor area. If the uppermost storey or storeys of a building can be construc...
`(4)(a) and Articles 3.4.6.19. and 3.8.2.10. shall [object Object]be not less than 200 lx. [object Object]8) Lighting outlets in a building of residential occupancy shall be provided in [object Object]conformance with Subsection 9.34.2. [object Object]`	`(4)(a) and Articles 3.4.6.19. and 3.8.2.10. shall [object Object]be not less than 200 lx. [object Object]8) Lighting outlets in a building of residential occupancy shall be provided in [object Object]conformance with Subsection 9.34.2. [object Object]`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
fp16: True
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: 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: 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: 10
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}
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
3.7594	500	0.0272
7.5188	1000	0.0007

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.0
Tokenizers: 0.21.0

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",
}

MultipleNegativesRankingLoss