This is a sentence-transformers model finetuned from distilbert/distilroberta-base. 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/distilroberta-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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: RobertaModel 
  (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("quanap5/distilroberta-base-sentence-transformer")
# Run inference
sentences = [
    'Are cats color blind?',
    'Are cats or dogs color blind?',
    'What does color mean to a blind person?',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 50,881 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 6 tokens
mean: 13.85 tokens
max: 42 tokens
min: 6 tokens
mean: 13.61 tokens
max: 55 tokens
min: 6 tokens
mean: 14.51 tokens
max: 52 tokens

Samples:

sentence_0	sentence_1	sentence_2
`What does Japan think of China?`	`What do the Japanese think about China?`	`What do people think about China?`
`How does it feel to be dead?`	`What is it feel like to die?`	`How does it feel to die and then come back to life?`
`Why do India's nuclear scientists keep dying mysteriously?`	`Why do most of India's nuclear scientists get murdered?`	`How many nuclear weapons does India have?`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 10
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: 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: round_robin

Training Logs

Epoch	Step	Training Loss
0.1572	500	3.5433
0.3144	1000	1.6687
0.4715	1500	1.1779
0.6287	2000	0.92
0.7859	2500	0.7807
0.9431	3000	0.7031
1.1003	3500	0.5542
1.2575	4000	0.4848
1.4146	4500	0.449
1.5718	5000	0.4402
1.7290	5500	0.3828
1.8862	6000	0.3825
2.0434	6500	0.33
2.2006	7000	0.1923
2.3577	7500	0.2137
2.5149	8000	0.208
2.6721	8500	0.2011
2.8293	9000	0.2054
2.9865	9500	0.192
3.1437	10000	0.1133
3.3008	10500	0.1068
3.4580	11000	0.1143
3.6152	11500	0.1088
3.7724	12000	0.1063
3.9296	12500	0.1101
4.0868	13000	0.077
4.2439	13500	0.0582
4.4011	14000	0.0612
4.5583	14500	0.0625
4.7155	15000	0.0631
4.8727	15500	0.0641
5.0299	16000	0.0605
5.1870	16500	0.0371
5.3442	17000	0.0372
5.5014	17500	0.0337
5.6586	18000	0.0414
5.8158	18500	0.0495
5.9730	19000	0.0365
6.1301	19500	0.0281
6.2873	20000	0.0225
6.4445	20500	0.0219
6.6017	21000	0.027
6.7589	21500	0.0217
6.9161	22000	0.0308
7.0732	22500	0.0176
7.2304	23000	0.0192
7.3876	23500	0.0164
7.5448	24000	0.0184
7.7020	24500	0.0153
7.8592	25000	0.0191
8.0163	25500	0.0142
8.1735	26000	0.0096
8.3307	26500	0.0101
8.4879	27000	0.0104
8.6451	27500	0.0103
8.8023	28000	0.0117
8.9594	28500	0.0131
9.1166	29000	0.0103
9.2738	29500	0.0074
9.4310	30000	0.0077
9.5882	30500	0.0063
9.7454	31000	0.0074
9.9025	31500	0.0068

Framework Versions

Python: 3.11.13
Sentence Transformers: 4.1.0
Transformers: 4.54.0
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
Datasets: 4.0.0
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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 6 tokens mean: 13.85 tokens max: 42 tokens	min: 6 tokens mean: 13.61 tokens max: 55 tokens	min: 6 tokens mean: 14.51 tokens max: 52 tokens