This is a sentence-transformers model finetuned from sentence-transformers/all-mpnet-base-v2. 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-mpnet-base-v2
Maximum Sequence Length: 384 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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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("zazabe/categorizer-mpnet-v2-mnrl")
# Run inference
sentences = [
    'Software: SEPA-Batch Profi. Manufacturer: None. Focus: Specialized in handling SEPA and related banking formats for batch processing of payments and direct debits, targeting businesses and organizations needing efficient electronic payment management.. Features: SEPA-Batch Profi enables the creation and management of cashless bookings in SEPA/DTAUS/DTAZV formats, supporting both domestic and EU credit and debit transactions. It helps reduce transaction costs by using widely supported banking formats. The software includes import functions for clients and booking records.. Security: not found.',
    'Business Applications > Finance > Payment Systems',
    'Tools and Utilities > Utilities > Desktop Enhancements',
]
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,  0.4858, -0.0712],
#         [ 0.4858,  1.0000,  0.0091],
#         [-0.0712,  0.0091,  1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: software_val_set
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5725
cosine_accuracy@3	0.8
cosine_accuracy@5	0.8431
cosine_precision@1	0.5725
cosine_precision@3	0.2667
cosine_precision@5	0.1686
cosine_precision@10	0.0922
cosine_recall@1	0.5725
cosine_recall@3	0.8
cosine_recall@5	0.8431
cosine_recall@10	0.9216
cosine_ndcg@10	0.7514
cosine_mrr@10	0.6966
cosine_map@100	0.7008

Training Details

Training Dataset

Unnamed Dataset

Size: 3,011 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 12 tokens
mean: 104.08 tokens
max: 214 tokens
min: 3 tokens
mean: 9.4 tokens
max: 21 tokens

Samples:

anchor	positive
Software: ASUS DisplayWidget. Manufacturer: ASUS. Focus: ASUS DisplayWidget is a utility designed to provide users with an easy way to customize and optimize their display settings for various tasks and preferences. It is primarily targeted at ASUS monitor users.. Features: ASUS DisplayWidget allows users to adjust display settings, such as brightness, contrast, color temperature, and gamma, directly from the desktop. It also offers preset modes for different usage scenarios, like gaming, reading, or watching movies. Additionally, it allows users to save and load custom display profiles.. Security: None.	`Utilities`
`Software: Amazon JDBC Driver. Manufacturer: Amazon. Focus: Designed for Java developers needing to integrate their applications with Amazon's database offerings.. Features: Enables Java applications to connect to Amazon Web Services databases. Supports various AWS database services. Provides optimized performance for AWS environments.. Security: Leverages AWS security features. Supports encryption and secure connections..`	`Databases > Database Management Systems (DBMS) > Other Database Management Systems (DBMS)`
`Software: SIMATIC Event Database. Manufacturer: Siemens. Focus: Designed for industrial automation systems, providing a structured approach to managing and analyzing events within SIMATIC environments.. Features: Centralized event logging and archiving, comprehensive diagnostics, and efficient troubleshooting.. Security: None.`	`Database Management Systems (DBMS)`

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

Evaluation Dataset

Unnamed Dataset

Size: 255 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 255 samples:
anchor positive
type string string
details
min: 29 tokens
mean: 115.56 tokens
max: 217 tokens
min: 8 tokens
mean: 13.33 tokens
max: 21 tokens

Samples:

anchor	positive
`Software: SeaTools. Manufacturer: Seagate Technology LLC. Focus: Hard drive diagnostic tool for Seagate, Samsung, LaCie and Maxtor drives.. Features: Diagnoses hard drives, including identifying the make and model, serial number, firmware revision, drive size, and supported features. Performs several tests and provides detailed drive information.. Security: None.`	`Tools and Utilities > Utilities > Accessibility and Assistive Tools`
Software: Stellar Repair for Access. Manufacturer: Stellar Data Recovery Inc.. Focus: Designed to repair corrupt Microsoft Access databases, restoring them to a usable state. It caters to individuals and businesses that rely on Access databases for data management.. Features: Repairs corrupt Access databases (MDB and ACCDB files). Recovers tables, forms, reports, modules, and other database objects. Supports recovery of deleted records.. Security: Does not modify the original database file during the repair process..	`Tools and Utilities > Utilities > Accessibility and Assistive Tools`
Software: Citrix Monitor Service PowerShell snap-in. Manufacturer: Citrix Systems, Inc.. Focus: Designed for IT administrators and support staff responsible for managing and monitoring Citrix virtual application and desktop deployments.. Features: Provides real-time and historical monitoring of Citrix Virtual Apps and Desktops environments. Allows administrators to troubleshoot issues, identify trends, and optimize performance. Offers customizable dashboards and reporting capabilities.. Security: Leverages Citrix security protocols for data transmission and access control. Role-based access control to restrict access to sensitive monitoring data..	`IT Infrastructure > IT Management > Alerts and Monitoring Tools`

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

eval_strategy: epoch
per_device_train_batch_size: 16
gradient_accumulation_steps: 8
weight_decay: 0.01
num_train_epochs: 10
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
optim: adamw_bnb_8bit

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 10
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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
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: True
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_bnb_8bit
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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: no
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: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Validation Loss	software_val_set_cosine_ndcg@10
1.0	24	0.3942	0.6777
2.0	48	0.3664	0.7310
3.0	72	0.3393	0.7453
4.0	96	0.3662	0.7555
5.0	120	0.3718	0.7492
6.0	144	0.3595	0.7435
7.0	168	0.3490	0.7402
8.0	192	0.3813	0.7462
9.0	216	0.3641	0.7536
10.0	240	0.3595	0.7514

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.3
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.9.1+cu128
Accelerate: 1.12.0
Datasets: 4.4.2
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",
}

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}
}

	anchor	positive
type	string	string
details	min: 12 tokens mean: 104.08 tokens max: 214 tokens	min: 3 tokens mean: 9.4 tokens max: 21 tokens