This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. 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: sentence-transformers/all-MiniLM-L6-v2
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, 'architecture': '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'install new Actuator Switch',
    'replace fan switch',
    'replace hi limit switch',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8039, 0.8263],
#         [0.8039, 1.0000, 0.7918],
#         [0.8263, 0.7918, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 78,106 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string int
details
min: 4 tokens
mean: 6.97 tokens
max: 16 tokens
min: 4 tokens
mean: 7.08 tokens
max: 19 tokens
1: 100.00%

Samples:

sentence1	sentence2	label
`Solid state relay contacts not making contact`	`Solid state relay not closing`	`1`
`Single pole relay is stuck closed`	`Relay is stuck closed`	`1`
`Relay potentially damaged due to wiring issue`	`Relay faulty`	`1`

Loss: OnlineContrastiveLoss

Evaluation Dataset

Unnamed Dataset

Size: 818 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 818 samples:
sentence1 sentence2 label
type string string int
details
min: 3 tokens
mean: 5.53 tokens
max: 10 tokens
min: 3 tokens
mean: 5.93 tokens
max: 14 tokens
0: ~50.00%
1: ~50.00%
Samples:
sentence1 sentence2 label
Power Relay needs replacement relay needs replacement 1
Target needs replacement ceramic tile needs replacement 1
install new Infinite Switch install new inf switch 1
Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 1e-05
num_train_epochs: 4
warmup_ratio: 0.02
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 1e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.02
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: True
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
hub_revision: None
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
1.0	7	-	28.7758
2.0	14	-	20.5461
3.0	21	-	14.5468
4.0	28	-	12.2789
5.0	35	-	7.4563
6.0	42	-	4.7709
7.0	49	-	3.7263
8.0	56	-	3.2799
9.0	63	-	3.4937
10.0	70	-	3.3956
11.0	77	-	3.2518
12.0	84	-	2.4912
13.0	91	-	1.7859
14.0	98	-	1.4185
14.2857	100	9.9923	-
15.0	105	-	1.4582
16.0	112	-	1.4355
17.0	119	-	1.2700
18.0	126	-	0.9766
19.0	133	-	0.9087
20.0	140	-	0.8227
21.0	147	-	0.7897
22.0	154	-	0.6956
23.0	161	-	0.7913
24.0	168	-	0.7902
25.0	175	-	0.7534
26.0	182	-	0.6562
27.0	189	-	0.5444
28.0	196	-	0.4464
28.5714	200	0.2576	-
29.0	203	-	0.4410
30.0	210	-	0.4314
31.0	217	-	0.3471
32.0	224	-	0.3472
33.0	231	-	0.3445
34.0	238	-	0.3404
35.0	245	-	0.3378
36.0	252	-	0.3370
37.0	259	-	0.3355
38.0	266	-	0.3339
39.0	273	-	0.3326
40.0	280	-	0.3328
41.0	287	-	0.3308
42.0	294	-	0.3308
42.8571	300	0.1918	-
43.0	301	-	0.3306
44.0	308	-	0.3304
45.0	315	-	0.3294
46.0	322	-	0.3295
47.0	329	-	0.3295
48.0	336	-	0.3297
49.0	343	-	0.3295
50.0	350	-	0.3295
1.0	4	-	0.1957
2.0	8	-	0.1227
3.0	12	-	0.1132
4.0	16	-	0.0720
5.0	20	-	0.0629
6.0	24	-	0.0591
7.0	28	-	0.0
8.0	32	-	0.0
9.0	36	-	0.0
10.0	40	-	0.0
11.0	44	-	0.0
12.0	48	-	0.0
12.5	50	0.441	-
13.0	52	-	0.0
14.0	56	-	0.0
15.0	60	-	0.0
16.0	64	-	0.0
17.0	68	-	0.0
18.0	72	-	0.0
19.0	76	-	0.0
20.0	80	-	0.0
21.0	84	-	0.0
22.0	88	-	0.0
23.0	92	-	0.0
24.0	96	-	0.0
25.0	100	0.3348	0.0
26.0	104	-	0.0
27.0	108	-	0.0
28.0	112	-	0.0
29.0	116	-	0.0
30.0	120	-	0.0
0.6536	200	7.707	-
1.0	306	-	0.7959
1.3072	400	6.7597	-
1.9608	600	6.4126	-
2.0	612	-	1.0730
2.6144	800	6.2623	-
3.0	918	-	1.1429
3.2680	1000	6.1651	-
3.9216	1200	6.12	-
4.0	1224	-	1.2153

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.11.0rc1
Sentence Transformers: 5.0.0
Transformers: 4.53.1
PyTorch: 2.7.1+cu126
Accelerate: 1.8.1
Datasets: 3.6.0
Tokenizers: 0.21.2

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",
}

	sentence1	sentence2	label
type	string	string	int
details	min: 4 tokens mean: 6.97 tokens max: 16 tokens	min: 4 tokens mean: 7.08 tokens max: 19 tokens	1: 100.00%

sentence1	sentence2	label
`Power Relay needs replacement`	`relay needs replacement`	`1`
`Target needs replacement`	`ceramic tile needs replacement`	`1`
`install new Infinite Switch`	`install new inf switch`	`1`

liamwilbur/sentence-transformer-synonyms

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

liamwilbur/
sentence-transformer-synonyms