This is a sentence-transformers model finetuned from intfloat/e5-large-v2. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: intfloat/e5-large-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', '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("MinhPhuc0804/clef-docling-e5-large-v2")
# Run inference
sentences = [
    'query: Link between vitamin D intake and COVID-19 contagion and death rate | Scientific Reports #cndpoli',
    'passage: title: Association between vitamin D supplementation and COVID-19 infection and mortality\nabstract: Abstract Vitamin D deficiency has long been associated with reduced immune function that can lead to viral infection. Several studies have shown that Vitamin D deficiency is associated with increases the risk of infection with COVID-19. However, it is unknown if treatment with Vitamin D can reduce the associated risk of COVID-19 infection, which is the focus of this study. In the population of US veterans, we show that Vitamin D 2 and D 3 fills were associated with reductions in COVID-19 infection of 28% and 20%, respectively [(D 3 Hazard Ratio (HR) = 0.80, [95% CI 0.77, 0.83]), D 2 HR = 0.72, [95% CI 0.65, 0.79]]. Mortality within 30-days of COVID-19 infection was similarly 33% lower with Vitamin D 3 and 25% lower with D 2 (D 3 HR = 0.67, [95% CI 0.59, 0.75]; D 2 HR = 0.75, [95% CI 0.55, 1.04]).',
    'passage: title: Harm to Nonhuman Animals from AI: a Systematic Account and Framework\nabstract: Abstract This paper provides a systematic account of how artificial intelligence (AI) technologies could harm nonhuman animals and explains why animal harms, often neglected in AI ethics, should be better recognised. After giving reasons for caring about animals and outlining the nature of animal harm, interests, and wellbeing, the paper develops a comprehensive ‘harms framework’ which draws on scientist David Fraser’s influential mapping of human activities that impact on sentient animals. The harms framework is fleshed out with examples inspired by both scholarly literature and media reports. This systematic account and framework should help inform ethical analyses of AI’s impact on animals and serve as a comprehensive and clear basis for the development and regulation of AI technologies to prevent and mitigate harm to nonhumans.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.6243, -0.0993],
#         [ 0.6243,  1.0000,  0.0158],
#         [-0.0993,  0.0158,  1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: 10-percent-dev-split
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5413
cosine_accuracy@3	0.7439
cosine_accuracy@5	0.813
cosine_accuracy@10	0.8722
cosine_precision@1	0.5413
cosine_precision@3	0.248
cosine_precision@5	0.1626
cosine_precision@10	0.0872
cosine_recall@1	0.5413
cosine_recall@3	0.7439
cosine_recall@5	0.813
cosine_recall@10	0.8722
cosine_ndcg@10	0.7072
cosine_mrr@10	0.6541
cosine_map@100	0.658

Training Details

Training Dataset

Unnamed Dataset

Size: 17,319 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: 21 tokens
mean: 57.37 tokens
max: 129 tokens
min: 26 tokens
mean: 186.91 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`query: Everyday chatter creates a coronavirus cloud that persists for 'tens of minutes or longer'. Teachers talk. Teachers move. Kids talk. Kids move. A blanket reopening of schools now makes no sense.`	passage: title: The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission[object Object]abstract: Speech droplets generated by asymptomatic carriers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are increasingly considered to be a likely mode of disease transmission. Highly sensitive laser light scattering observations have revealed that loud speech can emit thousands of oral fluid droplets per second. In a closed, stagnant air environment, they disappear from the window of view with time constants in the range of 8 to 14 min, which corresponds to droplet nuclei of ca. 4 μm diameter, or 12- to 21-μm droplets prior to dehydration. These observations confirm that there is a substantial probability that normal speaking causes airborne virus transmission in confined environments.
`query: Les avalanches de cytokines provoquées par les macrophages durant le COVID-19 grave débutent par une infection du SRAS‑CoV‑2 de cellules dendritiques particulières qui génèrent ensuite d’importantes quantités d’interféron pro‑inflammatoire de type I. 🤔`	passage: changes in macrophages at both transcriptional and epigenetic levels, which favored their hyperactivation by environmental stimuli.[object Object][object Object]title: Sensing of SARS-CoV-2 by pDCs and their subsequent production of IFN-I contribute to macrophage-induced cytokine storm during COVID-19[object Object]Together, these data indicate that the priming of macrophages can result from the response by pDCs to SARS-CoV-2, leading to macrophage activation in patients with severe COVID-19.
`query: Nye tal fra UK vedr. myocarditis. The risk of myocarditis was greater after vaccination than illness in young men.`	passage: ##2 ( irr 2. 02, 95 % ci 1. 40, 2. 91 ).[object Object][object Object]title: Risk of myocarditis following sequential COVID-19 vaccinations by age and sex[object Object]Associations were strongest in males younger than 40 years for all vaccine types with an additional 3 (95%CI 1, 5) and 12 (95% CI 1,17) events per million estimated in the 1-28 days following a first dose of BNT162b2 and mRNA-1273, respectively; 14 (95%CI 8, 17), 12 (95%CI 1, 7) and 101 (95%CI 95, 104) additional events following a second dose of ChAdOx1, BNT162b2 and mRNA-1273, respectively; and 13 (95%CI 7, 15) additional events following a third dose of BNT162b2, compared with 7 (95%CI 2, 11) additional events following COVID-19 infection. An association between COVID-19 infection and myocarditis was observed in all ages for both sexes but was substantially higher in those older than 40 years. These findings have important implications for public health and vaccination policy. Funding Health Data Research UK.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 10
fp16: True
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 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}
parallelism_config: 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	10-percent-dev-split_cosine_ndcg@10
0.9225	500	0.7284	-
1.0	542	-	0.6813
1.8450	1000	0.2798	-
2.0	1084	-	0.7030
2.7675	1500	0.1728	-
3.0	1626	-	0.6994
3.6900	2000	0.1095	-
4.0	2168	-	0.7063
4.6125	2500	0.0804	-
5.0	2710	-	0.7036
5.5351	3000	0.063	-
6.0	3252	-	0.7052
6.4576	3500	0.0499	-
7.0	3794	-	0.7064
7.3801	4000	0.0439	-
8.0	4336	-	0.7052
8.3026	4500	0.0392	-
9.0	4878	-	0.7069
9.2251	5000	0.0398	-
10.0	5420	-	0.7072

Training Time

Training: 1.1 hours

Framework Versions

Python: 3.12.6
Sentence Transformers: 5.4.1
Transformers: 4.56.0
PyTorch: 2.8.0+cu129
Accelerate: 1.10.1
Datasets: 4.8.4
Tokenizers: 0.22.0

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