This is a sentence-transformers model finetuned from pierreinalco/distilbert-base-uncased-sts. 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: pierreinalco/distilbert-base-uncased-sts
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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 = [
    'Fossil fuel reserves are finite and will eventually be depleted.',
    'Trace fossils, like footprints and burrows, reveal the behavior of ancient organisms.',
    'Electric trains are more environmentally friendly compared to diesel-powered ones.',
]
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

Semantic Similarity

Dataset: custom-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.92
spearman_cosine	0.8477
pearson_manhattan	0.9223
spearman_manhattan	0.8456
pearson_euclidean	0.9226
spearman_euclidean	0.8456
pearson_dot	0.9113
spearman_dot	0.8382
pearson_max	0.9226
spearman_max	0.8477

Semantic Similarity

Dataset: custom-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9125
spearman_cosine	0.8454
pearson_manhattan	0.9161
spearman_manhattan	0.8454
pearson_euclidean	0.9165
spearman_euclidean	0.8457
pearson_dot	0.903
spearman_dot	0.8319
pearson_max	0.9165
spearman_max	0.8457

Training Details

Training Dataset

Unnamed Dataset

Size: 19,352 training samples
Columns: s1, s2, and label
Approximate statistics based on the first 1000 samples:
s1 s2 label
type string string int
details
min: 10 tokens
mean: 19.85 tokens
max: 38 tokens
min: 11 tokens
mean: 20.47 tokens
max: 34 tokens
0: ~51.40%
1: ~48.60%

Samples:

s1	s2	label
`Resources and funding are essential for the successful rollout of any new curriculum.`	`For any new curriculum to be successfully rolled out, it is essential to have resources and funding.`	`1`
`Upgrading to LED lighting is a simple step toward improving energy efficiency in buildings.`	`Upgrading to new software is a simple step toward improving technology adoption in companies.`	`0`
`Ethnicity and language often intersect in interesting and complex ways.`	`Ethnicity and culture often diverge in unexpected and straightforward ways.`	`0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Evaluation Dataset

Unnamed Dataset

Size: 2,419 evaluation samples
Columns: s1, s2, and label
Approximate statistics based on the first 1000 samples:
s1 s2 label
type string string int
details
min: 10 tokens
mean: 19.91 tokens
max: 39 tokens
min: 11 tokens
mean: 20.41 tokens
max: 38 tokens
0: ~52.90%
1: ~47.10%

Samples:

s1	s2	label
`[SYNTAX] Consuming too much processed sugar can lead to insulin resistance and diabetes.`	`[SYNTAX] Drinking too much water can help maintain proper hydration and overall health.`	`1`
`Neutral tones and minimalist designs are staples of gender-neutral fashion.`	`Colorful patterns and intricate designs are staples of traditional ceremonial attire.`	`0`
`[SYNTAX] Policies focusing on sustainable agriculture practices are essential for ensuring food security in the face of climate change.`	`[SYNTAX] Ensuring food security amidst climate change requires critical policies that emphasize sustainable agricultural practices.`	`0`

Loss: CosineSimilarityLoss with these parameters:
json
1{ 2 "loss_fct": "torch.nn.modules.loss.MSELoss" 3}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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
learning_rate: 5e-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: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 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: proportional

Training Logs

Epoch	Step	Training Loss	loss	custom-dev_spearman_cosine	custom-test_spearman_cosine
0.3300	100	0.2137	0.0971	0.8252	-
0.6601	200	0.0722	0.0516	0.8445	-
0.9901	300	0.0503	0.0440	0.8480	-
1.3201	400	0.0353	0.0417	0.8479	-
1.6502	500	0.032	0.0388	0.8500	-
1.9802	600	0.0312	0.0375	0.8484	-
2.3102	700	0.0175	0.0380	0.8494	-
2.6403	800	0.016	0.0368	0.8486	-
2.9703	900	0.0158	0.0367	0.8486	-
3.3003	1000	0.0087	0.0394	0.8463	-
3.6304	1100	0.0086	0.0371	0.8463	-
3.9604	1200	0.0098	0.0368	0.8475	-
4.2904	1300	0.0055	0.0384	0.8496	-
4.6205	1400	0.0057	0.0379	0.8466	-
4.9505	1500	0.0057	0.0389	0.8473	-
5.2805	1600	0.0037	0.0391	0.8482	-
5.6106	1700	0.0042	0.0379	0.8477	-
5.9406	1800	0.0039	0.0380	0.8479	-
6.2706	1900	0.0026	0.0390	0.8477	-
6.6007	2000	0.0028	0.0390	0.8475	-
6.9307	2100	0.0031	0.0385	0.8473	-
7.2607	2200	0.0022	0.0393	0.8473	-
7.5908	2300	0.0021	0.0391	0.8470	-
7.9208	2400	0.002	0.0387	0.8482	-
8.2508	2500	0.0013	0.0389	0.8482	-
8.5809	2600	0.0014	0.0392	0.8484	-
8.9109	2700	0.0018	0.0390	0.8479	-
9.2409	2800	0.0015	0.0393	0.8480	-
9.5710	2900	0.0012	0.0393	0.8479	-
9.9010	3000	0.0013	0.0394	0.8477	-
10.0	3030	-	-	-	0.8454

Framework Versions

Python: 3.11.9
Sentence Transformers: 3.0.0
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.30.1
Datasets: 2.19.1
Tokenizers: 0.19.1

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",
}

	s1	s2	label
type	string	string	int
details	min: 10 tokens mean: 19.85 tokens max: 38 tokens	min: 11 tokens mean: 20.47 tokens max: 34 tokens	0: ~51.40% 1: ~48.60%

pierreinalco/custom-v2

SentenceTransformer based on pierreinalco/distilbert-base-uncased-sts

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Semantic Similarity

Semantic Similarity

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

pierreinalco/
custom-v2