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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:849
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L12-v2
widget:
- source_sentence: Graphic designer who specializes in creating visual content for
brands, including logos, marketing materials, and user interfaces. Focuses on
aesthetics, user experience, and brand identity.
sentences:
- 'user_1: I''m looking to refresh my company''s brand image but don''t know where
to start.
user_2: You should consult a brand manager.'
- 'user_1: I need help designing a logo for my new business.
user_2: Have you thought about hiring a graphic designer?
user_1: Yes, I want something that really represents my brand.'
- 'user_1: My car''s making a weird noise, and I don''t know what to do.
user_2: You should take it to a mechanic.'
- source_sentence: Nutritionist who specializes in dietary planning and nutritional
counseling. Helps clients achieve their health goals through personalized meal
plans and education.
sentences:
- 'user_1: I''m trying to lose weight but I don''t know what to eat.
user_2: Have you considered talking to a nutritionist?'
- 'user_1: Our database is running slow, and I don''t know why.
user_2: Have you checked the indexing?'
- 'user_1: I need help fixing my car''s engine; it''s making a weird noise.
user_2: Have you checked the oil level?'
- source_sentence: 'user_2: Sure, what problem are you working on?'
sentences:
- Gardening expert specializing in vegetable gardening techniques and plant care.
- Event planner focusing on corporate events and wedding coordination.
- Math tutor specializing in teaching and clarifying mathematical concepts and problem-solving.
- source_sentence: 'user_2: Have you thought about getting some storage bins?'
sentences:
- Web developer focused on software engineering and application design.
- Professional organizer specializing in home organization and decluttering strategies.
- Pet behavior specialist who provides advice on dog breeds and training for small
living spaces.
- source_sentence: 'user_1: Maybe the national parks, I want to see some nature.'
sentences:
- Mental health counselor specializing in stress management and coping strategies.
- Data analyst focusing on market trends and business intelligence.
- Travel consultant specializing in road trip planning and national park itineraries.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) on the semantic_triplets_round1 and inverse_semantic_triplets datasets. 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-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision c004d8e3e901237d8fa7e9fff12774962e391ce5 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- semantic_triplets_round1
- inverse_semantic_triplets
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/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': 128, 'do_lower_case': False}) with Transformer model: 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("sentence_transformers_model_id")
# Run inference
sentences = [
'user_1: Maybe the national parks, I want to see some nature.',
'Travel consultant specializing in road trip planning and national park itineraries.',
'Data analyst focusing on market trends and business intelligence.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Training Details
### Training Datasets
#### semantic_triplets_round1
* Dataset: semantic_triplets_round1
* Size: 422 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 422 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 17.44 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 14.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 12.49 tokens</li><li>max: 20 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|
| <code>user_1: Can anyone recommend a good app for tracking my expenses?</code> | <code>Personal finance advisor specializing in budgeting tools and expense tracking applications.</code> | <code>Fitness instructor focusing on workout plans and nutrition.</code> |
| <code>user_1: Can anyone recommend a good workout routine for beginners?</code> | <code>Fitness trainer who specializes in creating beginner workout plans and exercise coaching.</code> | <code>Financial advisor focused on investment strategies and retirement planning.</code> |
| <code>user_2: What kind of vegetables are you thinking of planting?</code> | <code>Gardening expert who provides guidance on vegetable gardening techniques and plant care.</code> | <code>Investment advisor specializing in stock market strategies and financial planning.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### inverse_semantic_triplets
* Dataset: inverse_semantic_triplets
* Size: 427 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 427 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 18 tokens</li><li>mean: 28.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 40.04 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 27.66 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------|
| <code>UX researcher specializing in user experience design and user testing. Conducts research to understand user needs and improve product usability.</code> | <code>user_1: I'm looking for ways to improve the usability of our app.<br>user_2: Have you considered conducting user interviews?</code> | <code>user_1: I need to plan a trip to Europe next summer.<br>user_2: What countries are you thinking about visiting?</code> |
| <code>Software developer specializing in web applications, proficient in various programming languages and frameworks. I design, develop, and maintain software solutions, focusing on user experience and functionality.</code> | <code>user_1: I'm trying to build a web application, but I'm stuck on how to integrate the backend with the frontend.<br>user_2: What technologies are you using for both?<br>user_1: I’m using Node.js for the backend and React for the frontend.</code> | <code>user_1: I'm looking for a good recipe for chocolate chip cookies.<br>user_2: I can share my favorite one!</code> |
| <code>Marketing strategist who focuses on developing comprehensive marketing plans to drive brand engagement and sales growth. Specializes in digital marketing and content strategy.</code> | <code>user_1: I'm launching a new product and need a marketing strategy.<br>user_2: Have you set any goals for your campaign?</code> | <code>user_1: I'm looking for a new pair of running shoes.<br>user_2: What brand do you prefer?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Datasets
#### semantic_triplets_round1
* Dataset: semantic_triplets_round1
* Size: 47 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 47 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 17.87 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 14.32 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 12.49 tokens</li><li>max: 16 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|
| <code>user_1: What's the best way to train my puppy to stop barking?</code> | <code>Dog training specialist focused on behavioral issues and obedience training.</code> | <code>Financial advisor who specializes in investment strategies and wealth management.</code> |
| <code>user_2: What vegetables do you want to grow?</code> | <code>Gardening expert specializing in vegetable gardening and sustainable practices.</code> | <code>Real estate agent focusing on home buying and selling.</code> |
| <code>user_1: Anyone have tips on how to improve my running time for a 5k?</code> | <code>Running coach specializing in training plans and performance improvement.</code> | <code>Financial advisor focusing on investment strategies and retirement planning.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### inverse_semantic_triplets
* Dataset: inverse_semantic_triplets
* Size: 48 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 48 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 28.42 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 39.71 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 28.4 tokens</li><li>max: 52 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>Graphic designer who specializes in creating visual content for brands, including logos, marketing materials, and user interfaces. Focuses on aesthetics, user experience, and brand identity.</code> | <code>user_1: I need help designing a logo for my new business.<br>user_2: Have you thought about hiring a graphic designer?<br>user_1: Yes, I want something that really represents my brand.</code> | <code>user_1: My car's making a weird noise, and I don't know what to do.<br>user_2: You should take it to a mechanic.</code> |
| <code>Physical therapist specializing in rehabilitation for sports injuries, pain management, and improving mobility through tailored exercise programs.</code> | <code>user_1: I twisted my ankle playing basketball, and it's really swollen.<br>user_2: Have you seen a doctor about it?</code> | <code>user_1: I'm thinking of redecorating my living room.<br>user_2: What style are you going for?</code> |
| <code>An accountant who specializes in financial record-keeping, tax preparation, and business consulting. Provides services to help clients manage their finances effectively and ensure compliance with tax regulations.</code> | <code>user_1: I need help with my taxes this year.<br>user_2: Are you looking for someone to prepare them for you?</code> | <code>user_1: I'm thinking about getting a puppy.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `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
- `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
- `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
- `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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Framework Versions
- Python: 3.12.9
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}
}
```
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