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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:333
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: brain scan
sentences:
- 'Full WordPress Site Health Audit. Check plugins — installed, active, and update
status command Verify SEO configuration command Test page speed with Lighthouse
command Security check — WordFence scan and login protection check Test REST API
connectivity and credentials command Check Google Search Console for crawl errors
and ranking prompt. Tags: wordpress, audit, seo, security, performance, plugins,
health'
- 'Bulk Article Creation for a Site. Research topics and identify content gaps command
Generate content outlines and briefs command Write content using ZimmWriter or
scripts prompt Generate featured images for all articles command Publish articles
and set featured images command. Tags: content, articles, bulk, wordpress, seo,
publishing, zimmwriter'
- 'Run EMPIRE-BRAIN Scan and Intelligence Cycle. Run full empire brain scan command
Generate intelligence briefing command Check brain stats and performance metrics
command Run evolution cycle command Verify Sentinel service monitoring status
command. Tags: empire, brain, scan, intelligence, monitoring, evolution, briefing'
- source_sentence: check vps
sentences:
- 'Cross-Site Internal Linking Strategy. Identify cluster and linking opportunities
check Find matching content between cluster sites command Generate and inject
cross-links command Verify and monitor link health command. Tags: seo, internal-links,
cross-site, clusters, link-whisper, revenue'
- 'Full WordPress Site Health Audit. Check plugins — installed, active, and update
status command Verify SEO configuration command Test page speed with Lighthouse
command Security check — WordFence scan and login protection check Test REST API
connectivity and credentials command Check Google Search Console for crawl errors
and ranking prompt. Tags: wordpress, audit, seo, security, performance, plugins,
health'
- 'Full WordPress Site Health Audit. Check plugins — installed, active, and update
status command Verify SEO configuration command Test page speed with Lighthouse
command Security check — WordFence scan and login protection check Test REST API
connectivity and credentials command Check Google Search Console for crawl errors
and ranking prompt. Tags: wordpress, audit, seo, security, performance, plugins,
health'
- source_sentence: performance optim
sentences:
- 'Run EMPIRE-BRAIN Scan and Intelligence Cycle. Run full empire brain scan command
Generate intelligence briefing command Check brain stats and performance metrics
command Run evolution cycle command Verify Sentinel service monitoring status
command. Tags: empire, brain, scan, intelligence, monitoring, evolution, briefing'
- 'Site Speed & Core Web Vitals Optimization. Run Lighthouse audit command Configure
LiteSpeed Cache (all sites use it) command Image optimization check Font and render-blocking
resource optimization check Verify improvements and monitor command. Tags: seo,
speed, performance, core-web-vitals, litespeed, images, caching'
- 'WordPress Plugin Deployment to Sites. Check if plugin is available on WorldPressIT
check Install via WordPress REST API or WP-CLI command Configure plugin settings
prompt Verify no conflicts with existing plugins check Deploy fleet-wide if applicable
prompt. Tags: wordpress, plugin, deploy, fleet'
- source_sentence: make pinterest pin
sentences:
- 'Generate Article Featured Images. Run article_images_pipeline.py with correct
arguments command Verify the featured image was set check. Tags: content, images,
wordpress, featured-image'
- 'Social Media Post Generation & Scheduling. Generate platform-specific images
command Write platform-specific copy prompt Schedule or post via automation command.
Tags: content, social, pinterest, instagram, facebook, twitter, scheduling'
- 'Bootstrap a New Empire Project. Create project directory and initialize git command
Create PROJECT_DNA.md command Create CLAUDE.md with project-specific config prompt
Set up Python environment command Create initial git commit and push to GitHub
command Register project in EMPIRE-BRAIN command. Tags: empire, project, bootstrap,
setup, new'
- source_sentence: batch articles
sentences:
- 'Bulk Article Creation for a Site. Research topics and identify content gaps command
Generate content outlines and briefs command Write content using ZimmWriter or
scripts prompt Generate featured images for all articles command Publish articles
and set featured images command. Tags: content, articles, bulk, wordpress, seo,
publishing, zimmwriter'
- 'Site Speed & Core Web Vitals Optimization. Run Lighthouse audit command Configure
LiteSpeed Cache (all sites use it) command Image optimization check Font and render-blocking
resource optimization check Verify improvements and monitor command. Tags: seo,
speed, performance, core-web-vitals, litespeed, images, caching'
- 'SSL Certificate & Domain Management. Check domain/SSL status via Hostinger command
Manage DNS records command SSL certificate management manual Domain security audit
command. Tags: ssl, domain, certificate, dns, hostinger, renewal'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: empire eval
type: empire-eval
metrics:
- type: pearson_cosine
value: 0.5531234570199464
name: Pearson Cosine
- type: spearman_cosine
value: 0.5320495924169611
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 384, '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:
```bash
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("WealthFromAI/empire-embed")
# Run inference
sentences = [
'batch articles',
'Bulk Article Creation for a Site. Research topics and identify content gaps command Generate content outlines and briefs command Write content using ZimmWriter or scripts prompt Generate featured images for all articles command Publish articles and set featured images command. Tags: content, articles, bulk, wordpress, seo, publishing, zimmwriter',
'Site Speed & Core Web Vitals Optimization. Run Lighthouse audit command Configure LiteSpeed Cache (all sites use it) command Image optimization check Font and render-blocking resource optimization check Verify improvements and monitor command. Tags: seo, speed, performance, core-web-vitals, litespeed, images, caching',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7041, 0.2289],
# [0.7041, 1.0000, 0.3787],
# [0.2289, 0.3787, 1.0000]])
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `empire-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.5531 |
| **spearman_cosine** | **0.532** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 333 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 333 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.24 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 62.94 tokens</li><li>max: 141 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.57</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:-------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>bootstrap project</code> | <code>Bootstrap a New Empire Project. Create project directory and initialize git command Create PROJECT_DNA.md command Create CLAUDE.md with project-specific config prompt Set up Python environment command Create initial git commit and push to GitHub command Register project in EMPIRE-BRAIN command. Tags: empire, project, bootstrap, setup, new</code> | <code>1.0</code> |
| <code>update container</code> | <code>WordPress Site SEO Setup & Configuration. Verify RankMath SEO plugin is installed and activated check Configure RankMath general settings prompt Set up Schema markup patterns per post type prompt Configure robots.txt and sitemap command Set up internal linking structure prompt Configure affiliate link handling prompt. Tags: wordpress, seo, rankmath, schema, setup</code> | <code>0.0</code> |
| <code>restart service</code> | <code>Full WordPress Site Health Audit. Check plugins — installed, active, and update status command Verify SEO configuration command Test page speed with Lighthouse command Security check — WordFence scan and login protection check Test REST API connectivity and credentials command Check Google Search Console for crawl errors and ranking prompt. Tags: wordpress, audit, seo, security, performance, plugins, health</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 5
- `eval_strategy`: steps
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 16
- `num_train_epochs`: 5
- `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`: 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`: 16
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | empire-eval_spearman_cosine |
|:-----:|:----:|:---------------------------:|
| 1.0 | 21 | 0.5320 |
### Framework Versions
- Python: 3.12.10
- Sentence Transformers: 5.3.0
- Transformers: 5.4.0
- PyTorch: 2.11.0+cpu
- Accelerate: 1.13.0
- Datasets: 4.8.4
- 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",
}
```
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