This is a sentence-transformers model finetuned from intfloat/e5-large. 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
Maximum Sequence Length: 512 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("sentence_transformers_model_id")
# Run inference
queries = [
    'query: factory reset in DSU mode without permission check?',
]
documents = [
    'passage: In rebootWipeUserData of RecoverySystem.java, there is a possible way to factory reset the device while in DSU mode due to a missing permission check. This could lead to physical denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.',
    'passage: A cross-site scripting (XSS) vulnerability in /index.php/?p=report of Online Fire Reporting System v1.0 allows attackers to execute arbitrary web scripts or HTML via a crafted payload injected into the "Contac #" text field.',
    'passage: Multiple stack-based buffer overflows in Mini-stream Easy RM-MP3 Converter 3.0.0.7 allow remote attackers to execute arbitrary code via (1) a long rtsp URL in a .ram file and (2) a long string in the HREF attribute of a REF element in a .asx file.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.6806, -0.1250, -0.1078]])

Evaluation

Metrics

Information Retrieval

Dataset: validation
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7087
cosine_accuracy@3	0.8211
cosine_accuracy@5	0.8555
cosine_accuracy@10	0.8954
cosine_precision@1	0.7087
cosine_precision@3	0.2737
cosine_precision@5	0.1711
cosine_precision@10	0.0895
cosine_recall@1	0.7087
cosine_recall@3	0.8211
cosine_recall@5	0.8555
cosine_recall@10	0.8954
cosine_ndcg@10	0.8023
cosine_mrr@10	0.7725
cosine_map@100	0.7765

Training Details

Training Dataset

Unnamed Dataset

Size: 861,607 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 100 samples:
sentence_0 sentence_1
type string string
modality text text
details
min: 9 tokens
mean: 14.16 tokens
max: 32 tokens
min: 22 tokens
mean: 94.61 tokens
max: 512 tokens

Samples:

sentence_0	sentence_1
`query: why are my data records disappearing?`	passage: ---[object Object]title: Cross-Tenant Legacy Correlation Disclosure and Deletion[object Object]draft: false[object Object]hero:[object Object] image: /static/img/heros/hero-legal2.svg[object Object] content: "# Cross-Tenant Legacy Correlation Disclosure and Deletion"[object Object]date: 2026-01-29[object Object]product: Grafana[object Object]severity: Low[object Object]cve: CVE-2026-21727[object Object]cvss_score: "3.3"[object Object]cvss_vector: "CVSS:3.3/AV:N/AC:H/PR:H/UI:N/S:U/C:L/I:L/A:N"[object Object]fixed_versions:[object Object] - ">=11.6.11 >=12.0.9 >=12.1.6 >=12.2.4"[object Object]---[object Object]A cross-tenant isolation vulnerability was found in Grafana’s Correlations feature affecting legacy correlation records. Due to a backward compatibility condition allowing org_id = 0 records to be returned across organizations, a user with datasource management privileges could read and permanently delete legacy correlation data belonging to another organization. This issue affects correlations created prior to Grafana 10.2 and is fixed in >=11.6.11, >=12.0.9, >=12.1.6, and >=12.2.4.[object Object][object Object]Thanks to Gyu-hyeok Lee (g2h) for reporting this vulnerability.
`query: Exim 4.7x DKIM identity issue`	`passage: The DKIM implementation in Exim 4.7x before 4.76 permits matching for DKIM identities to apply to lookup items, instead of only strings, which allows remote attackers to execute arbitrary code or access a filesystem via a crafted identity.`
`query: JMK Picture Gallery security issue`	`passage: JMK's Picture Gallery allows remote attackers to bypass authentication via a direct request to admin_gallery.php3, possibly related to the add action.`

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: 4
num_train_epochs: 1
fp16: True
per_device_eval_batch_size: 4
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

per_device_train_batch_size: 4
num_train_epochs: 1
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
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
label_smoothing_factor: 0.0
bf16: False
fp16: True
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: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 4
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_static_graph: None
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: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs