This is a sentence-transformers model finetuned from abkimc/distilroberta-base-sentence-transformer. 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: abkimc/distilroberta-base-sentence-transformer
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, '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})
)

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("abkimc/distilroberta-base-sentence-transformer")
# Run inference
sentences = [
    'The HTC Legend has made its official debut in India days after it was informally launched .',
    'HTC Legend makes official debut in India',
    'Britain, Bill Gates join forces',
]
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,  0.9061, -0.0382],
#         [ 0.9061,  1.0000, -0.0170],
#         [-0.0382, -0.0170,  1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 180,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 12 tokens
mean: 33.68 tokens
max: 293 tokens
min: 5 tokens
mean: 10.98 tokens
max: 28 tokens

Samples:

sentence_0	sentence_1
`Content is the king in today's world of journalism and a newspaper cannot survive if it compromises on the quality of the content, said Abhilash Khandekar, Maharashtra state head of Dainik Bhaskar Group on Tuesday.`	`'Content is king in today's journalism'`
`Sammons Pensions has launched its ninth annual salary survey which aims to document remuneration packages across the industry.`	`Sammons launches ninth salary survey`
`The state of Tennessee saw a major spike in foreclosure filings in 2008, according to a report by the Tennessee Housing Development Agency.`	`Tennessee sees major spike in foreclosure filings in 2008`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 64
per_device_eval_batch_size: 64
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_fused
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.1777	500	2.8662
0.3555	1000	0.0631
0.5332	1500	0.0149
0.7110	2000	0.0097
0.8887	2500	0.0079
1.0665	3000	0.0062
1.2442	3500	0.0041
1.4220	4000	0.0037
1.5997	4500	0.0038
1.7775	5000	0.0034
1.9552	5500	0.0038
2.1330	6000	0.0021
2.3107	6500	0.0015
2.4884	7000	0.0016
2.6662	7500	0.0015
2.8439	8000	0.0018
3.0217	8500	0.0015
3.1994	9000	0.0013
3.3772	9500	0.001
3.5549	10000	0.0011
3.7327	10500	0.0011
3.9104	11000	0.0014
4.0882	11500	0.0011
4.2659	12000	0.0007
4.4437	12500	0.0009
4.6214	13000	0.0009
4.7991	13500	0.0008
4.9769	14000	0.0008
5.1546	14500	0.0009
5.3324	15000	0.0007
5.5101	15500	0.0007
5.6879	16000	0.0007
5.8656	16500	0.0006
6.0434	17000	0.0007
6.2211	17500	0.0007
6.3989	18000	0.0005
6.5766	18500	0.0007
6.7544	19000	0.0005
6.9321	19500	0.0005
7.1098	20000	0.0005
7.2876	20500	0.0006
7.4653	21000	0.0005
7.6431	21500	0.0004
7.8208	22000	0.0004
7.9986	22500	0.0004
8.1763	23000	0.0004
8.3541	23500	0.0004
8.5318	24000	0.0005
8.7096	24500	0.0004
8.8873	25000	0.0004
9.0651	25500	0.0005
9.2428	26000	0.0004
9.4205	26500	0.0005
9.5983	27000	0.0004
9.7760	27500	0.0004
9.9538	28000	0.0004

Framework Versions

Python: 3.12.11
Sentence Transformers: 5.1.0
Transformers: 4.55.4
PyTorch: 2.8.0+cu126
Accelerate: 1.10.1
Datasets: 4.0.0
Tokenizers: 0.21.4

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence_0	sentence_1
type	string	string
details	min: 12 tokens mean: 33.68 tokens max: 293 tokens	min: 5 tokens mean: 10.98 tokens max: 28 tokens