This is a sentence-transformers model finetuned from zacbrld/MNLP_M2_document_encoder. It maps sentences & paragraphs to a 384-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: zacbrld/MNLP_M2_document_encoder
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("zacbrld/MNLP_M3_document_encoder_V1")
# Run inference
sentences = [
    'synthesis method',
    'STEMNET used to receive funding from the Department for Education and Skills Since June 2007, it receives funding from the Department for Children, Schools and Families and Department for Innovation, Universities and Skills, since STEMNET sits on the chronological dividing point (age 16) of both of the new departments',
    'The Arab States of the Persian Gulf plan to start their own joint civilian nuclear program An agreement in the final days of the Bush administration provided for cooperation between the United Arab Emirates and the United States of America in which the United States would sell the UAE nuclear reactors and nuclear fuel The UAE would, in return, renounce their right to enrich uranium for their civilian nuclear program At the time of signing, this agreement was touted as a way to reduce risks of nuclear proliferation in the Persian Gulf However, Mustafa Alani of the Dubai-based Gulf Research Center stated that, should the Nuclear Non-Proliferation Treaty collapse, nuclear reactors such as those slated to be sold to the UAE under this agreement could provide the UAE with a path toward a nuclear weapon, raising the specter of further nuclear proliferation In March 2007, foreign ministers of the six-member Gulf Cooperation Council met in Saudi Arabia to discuss progress in plans agreed in December 2006, for a joint civilian nuclear program',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 34,235 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: 3 tokens
mean: 21.24 tokens
max: 256 tokens
min: 34 tokens
mean: 133.62 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`chemistry experiment`	`Since 1982, research has been conducted to develop technologies, commonly referred to as electronic noses, that could detect and recognize odors and flavors Application areas include food, medicine and the environment`
`quantum physics`	Hydro electric - Hydro-electric turbomachinery uses potential energy stored in water to flow over an open impeller to turn a generator which creates electricity[object Object]Steam turbines - Steam turbines used in power generation come in many different variations The overall principle is high pressure steam is forced over blades attached to a shaft, which turns a generator As the steam travels through the turbine, it passes through smaller blades causing the shaft to spin faster, creating more electricity Gas turbines - Gas turbines work much like steam turbines Air is forced in through a series of blades that turn a shaft Then fuel is mixed with the air and causes a combustion reaction, increasing the power This then causes the shaft to spin faster, creating more electricity Windmills - Also known as a wind turbine, windmills are increasing in popularity for their ability to efficiently use the wind to generate electricity Although they come in many shapes and sizes, the most common one is the la...
`physics law`	Backlash in gear couplings allows for slight angular misalignment There can be significant backlash in unsynchronized transmissions because of the intentional gap between the dogs in dog clutches The gap is necessary to engage dogs when input shaft (engine) speed and output shaft (driveshaft) speed are imperfectly synchronized If there was a smaller clearance, it would be nearly impossible to engage the gears because the dogs would interfere with each other in most configurations In synchronized transmissions, synchromesh solves this problem However, backlash is undesirable in precision positioning applications such as machine tool tables It can be minimized by choosing ball screws or leadscrews with preloaded nuts, and mounting them in preloaded bearings A preloaded bearing uses a spring and/or a second bearing to provide a compressive axial force that maintains bearing surfaces in contact despite reversal of the load direction

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

Training Hyperparameters

Non-Default Hyperparameters

num_train_epochs: 2
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: 8
per_device_eval_batch_size: 8
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: 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: 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
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
0.1168	500	1.465
0.2336	1000	1.189
0.3505	1500	1.1209
0.4673	2000	1.0333
0.5841	2500	0.993
0.7009	3000	0.9573
0.8178	3500	0.9275
0.9346	4000	0.9177
1.0514	4500	0.8241
1.1682	5000	0.7726
1.2850	5500	0.7685
1.4019	6000	0.7623
1.5187	6500	0.7668
1.6355	7000	0.7556
1.7523	7500	0.7002
1.8692	8000	0.7363
1.9860	8500	0.7396

Framework Versions

Python: 3.12.8
Sentence Transformers: 3.4.1
Transformers: 4.52.2
PyTorch: 2.7.0+cu126
Accelerate: 1.3.0
Datasets: 3.2.0
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

bibtex

@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}
}

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 21.24 tokens max: 256 tokens	min: 34 tokens mean: 133.62 tokens max: 256 tokens