This is a sentence-transformers model finetuned from sentence-transformers/all-distilroberta-v1 on the trec dataset. 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: sentence-transformers/all-distilroberta-v1
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- trec

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, 'architecture': '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})
  (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("tomaarsen/all-distilroberta-v1-trec-baseline")
# Run inference
sentences = [
    "What country contains Africa 's northernmost point ?",
    'What is the difference between classical conditioning and operant conditioning ?',
    'Where can stocks be traded on-line ?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, -0.3543,  0.4898],
#         [-0.3543,  1.0000, -0.1897],
#         [ 0.4898, -0.1897,  1.0000]])

Evaluation

Metrics

Triplet

Datasets: trec-dev and trec-test
Evaluated with TripletEvaluator

Metric	trec-dev	trec-test
cosine_accuracy	0.9268	0.9313

Training Details

Training Dataset

trec

Dataset: trec at a073d2e
Size: 4,952 training samples
Columns: text and label

Approximate statistics based on the first 1000 samples:

	text	label
type	string	int
details	min: 6 tokens mean: 13.29 tokens max: 39 tokens	0: ~0.30% 1: ~1.50% 2: ~3.10% 3: ~0.60% 4: ~0.80% 5: ~3.90% 7: ~1.80% 8: ~1.20% 9: ~1.70% 10: ~0.20% 11: ~0.40% 12: ~0.20% 13: ~4.30% 14: ~0.40% 15: ~0.90% 16: ~0.10% 17: ~1.20% 18: ~0.70% 19: ~0.10% 20: ~0.70% 21: ~1.40% 22: ~0.20% 23: ~0.50% 24: ~7.90% 25: ~4.40% 26: ~4.90% 27: ~3.90% 28: ~3.30% 29: ~17.50% 30: ~0.50% 31: ~0.70% 32: ~2.70% 33: ~2.80% 34: ~0.80% 35: ~7.90% 36: ~1.40% 37: ~0.10% 38: ~6.30% 39: ~4.80% 40: ~0.40% 41: ~1.10% 42: ~0.10% 43: ~0.70% 44: ~0.40% 45: ~0.40% 46: ~0.50% 47: ~0.10% 48: ~0.20%

Samples:
text label
How did serfdom develop in and then leave Russia ? 26
What films featured the character Popeye Doyle ? 5
How can I find a list of celebrities ' real names ? 26
Loss: BatchAllTripletLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
num_train_epochs: 1
warmup_steps: 0.1
eval_strategy: steps
batch_sampler: group_by_label

All Hyperparameters

Click to expand

per_device_train_batch_size: 32
num_train_epochs: 1
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: steps
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: group_by_label
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	trec-dev_cosine_accuracy	trec-test_cosine_accuracy
-1	-1	-	0.7683	-
0.1032	16	0.6087	-	-
0.2	31	-	0.8699	-
0.2065	32	0.2989	-	-
0.3097	48	1.1776	-	-
0.4	62	-	0.9065	-
0.4129	64	1.9968	-	-
0.5161	80	1.1380	-	-
0.6	93	-	0.9207	-
0.6194	96	0.0	-	-
0.7226	112	0.5931	-	-
0.8	124	-	0.9248	-
0.8258	128	1.8914	-	-
0.9290	144	0.8833	-	-
1.0	155	-	0.9268	-
-1	-1	-	-	0.9313

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.3.0.dev0
Transformers: 5.3.0.dev0
PyTorch: 2.10.0+cu126
Accelerate: 1.12.0
Datasets: 4.3.0
Tokenizers: 0.22.2

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",
}

BatchAllTripletLoss