This is a sentence-transformers model finetuned from distilbert/distilbert-base-uncased. 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: distilbert/distilbert-base-uncased
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 = [
    'Salinity gradients in oceans affect local wildlife habitats.',
    'The distribution of wildlife in different habitats has fascinated ecologists for decades.',
    '[SYNTAX] Bioenergy plants can convert agricultural waste into valuable electricity.',
]
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	0.9117
spearman_cosine	0.8442
pearson_manhattan	0.9157
spearman_manhattan	0.8441
pearson_euclidean	0.916
spearman_euclidean	0.8446
pearson_dot	0.9046
spearman_dot	0.8328
pearson_max	0.916
spearman_max	0.8446

Semantic Similarity

Dataset: custom-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9198
spearman_cosine	0.8501
pearson_manhattan	0.9282
spearman_manhattan	0.8494
pearson_euclidean	0.9284
spearman_euclidean	0.8498
pearson_dot	0.9141
spearman_dot	0.8411
pearson_max	0.9284
spearman_max	0.8501

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: 19.92 tokens
max: 42 tokens
min: 10 tokens
mean: 20.53 tokens
max: 42 tokens
0: ~50.50%
1: ~49.50%

Samples:

s1	s2	label
`According to labeling theory, individuals are considered deviant once society has tagged them with that label.`	`Labeling theory posits that corporations become powerful when labeled as such by stakeholders.`	`0`
`Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.`	`Employers must classify workers correctly as either employees or independent contractors to comply with tax and labor laws.`	`1`
`Higher education institutions play a critical role in advancing research and innovation.`	`Advancement in research and innovation is significantly driven by the contributions of higher education institutions.`	`1`

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: 11 tokens
mean: 19.91 tokens
max: 37 tokens
min: 11 tokens
mean: 20.46 tokens
max: 42 tokens
0: ~49.70%
1: ~50.30%

Samples:

s1	s2	label
`Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.`	`Acoustic tomography is an innovative geophysical technique used to image the Earth's interior.`	`1`
`Urban areas frequently exhibit a different age distribution pattern compared to rural areas.`	`Urban areas frequently exhibit a different age distribution pattern compared to rural areas.`	`1`
`Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.`	`Radiocarbon dating is a critical tool for assessing the duration of battery life in modern electronic devices.`	`1`

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	0.2961	0.1185	0.8063	-
0.6601	200	0.0772	0.0504	0.8461	-
0.9901	300	0.0502	0.0454	0.8486	-
1.3201	400	0.0376	0.0402	0.8481	-
1.6502	500	0.0344	0.0400	0.8501	-
1.9802	600	0.0329	0.0390	0.8518	-
2.3102	700	0.0185	0.0387	0.8496	-
2.6403	800	0.0164	0.0371	0.8492	-
2.9703	900	0.0179	0.0393	0.8428	-
3.3003	1000	0.0099	0.0389	0.8466	-
3.6304	1100	0.0092	0.0395	0.8480	-
3.9604	1200	0.0101	0.0368	0.8492	-
4.2904	1300	0.0067	0.0385	0.8474	-
4.6205	1400	0.0056	0.0393	0.8456	-
4.9505	1500	0.0068	0.0401	0.8466	-
5.2805	1600	0.0041	0.0410	0.8462	-
5.6106	1700	0.0043	0.0399	0.8469	-
5.9406	1800	0.0039	0.0406	0.8463	-
6.2706	1900	0.003	0.0400	0.8456	-
6.6007	2000	0.0026	0.0416	0.8438	-
6.9307	2100	0.0027	0.0420	0.8437	-
7.2607	2200	0.0028	0.0424	0.8449	-
7.5908	2300	0.0021	0.0422	0.8458	-
7.9208	2400	0.002	0.0414	0.8451	-
8.2508	2500	0.0015	0.0421	0.8451	-
8.5809	2600	0.0015	0.0427	0.8451	-
8.9109	2700	0.0016	0.0429	0.8444	-
9.2409	2800	0.0011	0.0432	0.8442	-
9.5710	2900	0.0014	0.0432	0.8444	-
9.9010	3000	0.0011	0.0432	0.8442	-
10.0	3030	-	-	-	0.8501

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: 19.92 tokens max: 42 tokens	min: 10 tokens mean: 20.53 tokens max: 42 tokens	0: ~50.50% 1: ~49.50%

pierreinalco/custom-v1

SentenceTransformer based on distilbert/distilbert-base-uncased

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-v1