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---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8000
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: As a user, I want to reset my password via email so that I can
regain access.
sentences:
- 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
are saved and reflected in the user's profile.<br>3. Test the validation of profile
fields (e.g., email format).
- 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
are saved and reflected in the user's profile.<br>3. Test the validation of profile
fields (e.g., email format).
- 1. Verify that the password reset email is sent to the user's registered email
address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
password reset link to confirm it allows setting a new password.
- source_sentence: As a user, I want to update my profile information so that my account
details are current.
sentences:
- 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
page displays previous orders correctly.<br>3. Test the ability to filter orders
by date or status.
- 1. Verify that the password reset email is sent to the user's registered email
address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
password reset link to confirm it allows setting a new password.
- 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
are saved and reflected in the user's profile.<br>3. Test the validation of profile
fields (e.g., email format).
- source_sentence: As a customer, I want to receive notifications for order status
updates so that I stay informed.
sentences:
- 1. Check that notifications are sent for order status changes.<br>2. Verify that
notifications include accurate order details.<br>3. Test the notification settings
to ensure users can customize their preferences.
- 1. Verify that the password reset email is sent to the user's registered email
address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
password reset link to confirm it allows setting a new password.
- 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
page displays previous orders correctly.<br>3. Test the ability to filter orders
by date or status.
- source_sentence: As a user, I want to reset my password via email so that I can
regain access.
sentences:
- 1. Ensure the user can access the 'Order History' page.<br>2. Verify that the
page displays previous orders correctly.<br>3. Test the ability to filter orders
by date or status.
- 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
are saved and reflected in the user's profile.<br>3. Test the validation of profile
fields (e.g., email format).
- 1. Verify that the password reset email is sent to the user's registered email
address.<br>2. Ensure the email contains a password reset link.<br>3. Test the
password reset link to confirm it allows setting a new password.
- source_sentence: As a user, I want to update my profile information so that my account
details are current.
sentences:
- 1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes
are saved and reflected in the user's profile.<br>3. Test the validation of profile
fields (e.g., email format).
- 1. Confirm that an admin can access the 'Add New User' form.<br>2. Verify that
the form allows entering user details and submitting.<br>3. Check that the new
user is added to the user list after submission.
- 1. Test the search functionality by entering a product name and verifying that
the results include the correct product.<br>2. Ensure the search returns results
quickly.<br>3. Verify that searching for non-existent products returns no results.
---
# 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 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **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/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': 256, '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 = [
'As a user, I want to update my profile information so that my account details are current.',
"1. Ensure the user can access the 'Update Profile' form.<br>2. Verify that changes are saved and reflected in the user's profile.<br>3. Test the validation of profile fields (e.g., email format).",
'1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.',
]
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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</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
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 8,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 21 tokens</li><li>mean: 22.0 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 51.98 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
| query | answer |
|:----------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>As a user, I want to search for products by name so that I can find specific items.</code> | <code>1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.</code> |
| <code>As an admin, I want to add new users to the system so that I can manage user accounts.</code> | <code>1. Confirm that an admin can access the 'Add New User' form.<br>2. Verify that the form allows entering user details and submitting.<br>3. Check that the new user is added to the user list after submission.</code> |
| <code>As a user, I want to search for products by name so that I can find specific items.</code> | <code>1. Test the search functionality by entering a product name and verifying that the results include the correct product.<br>2. Ensure the search returns results quickly.<br>3. Verify that searching for non-existent products returns no results.</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 Dataset
#### Unnamed Dataset
* Size: 2,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 21 tokens</li><li>mean: 22.0 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 51.54 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>As a user, I want to reset my password via email so that I can regain access.</code> | <code>1. Verify that the password reset email is sent to the user's registered email address.<br>2. Ensure the email contains a password reset link.<br>3. Test the password reset link to confirm it allows setting a new password.</code> |
| <code>As a customer, I want to receive notifications for order status updates so that I stay informed.</code> | <code>1. Check that notifications are sent for order status changes.<br>2. Verify that notifications include accurate order details.<br>3. Test the notification settings to ensure users can customize their preferences.</code> |
| <code>As a user, I want to view my order history so that I can track my previous purchases.</code> | <code>1. Ensure the user can access the 'Order History' page.<br>2. Verify that the page displays previous orders correctly.<br>3. Test the ability to filter orders by date or status.</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`: epoch
- `per_device_eval_batch_size`: 16
- `learning_rate`: 3e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `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`: 3e-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`: 3
- `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`: 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}
- `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`: False
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss |
|:-----:|:----:|:-------------:|:------:|
| 0.025 | 25 | 0.6305 | - |
| 0.05 | 50 | 0.7491 | - |
| 0.075 | 75 | 0.6931 | - |
| 0.1 | 100 | 0.7093 | - |
| 0.125 | 125 | 0.631 | - |
| 0.15 | 150 | 0.6127 | - |
| 0.175 | 175 | 0.5963 | - |
| 0.2 | 200 | 0.6556 | - |
| 0.225 | 225 | 0.6831 | - |
| 0.25 | 250 | 0.6389 | - |
| 0.275 | 275 | 0.6913 | - |
| 0.3 | 300 | 0.6447 | - |
| 0.325 | 325 | 0.6993 | - |
| 0.35 | 350 | 0.6581 | - |
| 0.375 | 375 | 0.6695 | - |
| 0.4 | 400 | 0.7076 | - |
| 0.425 | 425 | 0.6301 | - |
| 0.45 | 450 | 0.6121 | - |
| 0.475 | 475 | 0.6439 | - |
| 0.5 | 500 | 0.6782 | - |
| 0.525 | 525 | 0.7025 | - |
| 0.55 | 550 | 0.7228 | - |
| 0.575 | 575 | 0.6065 | - |
| 0.6 | 600 | 0.6496 | - |
| 0.625 | 625 | 0.6816 | - |
| 0.65 | 650 | 0.6302 | - |
| 0.675 | 675 | 0.692 | - |
| 0.7 | 700 | 0.7533 | - |
| 0.725 | 725 | 0.6567 | - |
| 0.75 | 750 | 0.6472 | - |
| 0.775 | 775 | 0.6461 | - |
| 0.8 | 800 | 0.661 | - |
| 0.825 | 825 | 0.6897 | - |
| 0.85 | 850 | 0.6097 | - |
| 0.875 | 875 | 0.6284 | - |
| 0.9 | 900 | 0.5923 | - |
| 0.925 | 925 | 0.6642 | - |
| 0.95 | 950 | 0.6531 | - |
| 0.975 | 975 | 0.6705 | - |
| 1.0 | 1000 | 0.7137 | 0.6765 |
| 1.025 | 1025 | 0.6601 | - |
| 1.05 | 1050 | 0.6739 | - |
| 1.075 | 1075 | 0.6487 | - |
| 1.1 | 1100 | 0.6864 | - |
| 1.125 | 1125 | 0.7744 | - |
| 1.15 | 1150 | 0.6698 | - |
| 1.175 | 1175 | 0.6421 | - |
| 1.2 | 1200 | 0.633 | - |
| 1.225 | 1225 | 0.678 | - |
| 1.25 | 1250 | 0.6264 | - |
| 1.275 | 1275 | 0.721 | - |
| 1.3 | 1300 | 0.6736 | - |
| 1.325 | 1325 | 0.5332 | - |
| 1.35 | 1350 | 0.6576 | - |
| 1.375 | 1375 | 0.6625 | - |
| 1.4 | 1400 | 0.7248 | - |
| 1.425 | 1425 | 0.6188 | - |
| 1.45 | 1450 | 0.6452 | - |
| 1.475 | 1475 | 0.7024 | - |
| 1.5 | 1500 | 0.7005 | - |
| 1.525 | 1525 | 0.6219 | - |
| 1.55 | 1550 | 0.6525 | - |
| 1.575 | 1575 | 0.6718 | - |
| 1.6 | 1600 | 0.6738 | - |
| 1.625 | 1625 | 0.6558 | - |
| 1.65 | 1650 | 0.6236 | - |
| 1.675 | 1675 | 0.7126 | - |
| 1.7 | 1700 | 0.6822 | - |
| 1.725 | 1725 | 0.6324 | - |
| 1.75 | 1750 | 0.7036 | - |
| 1.775 | 1775 | 0.6765 | - |
| 1.8 | 1800 | 0.654 | - |
| 1.825 | 1825 | 0.6923 | - |
| 1.85 | 1850 | 0.6976 | - |
| 1.875 | 1875 | 0.6904 | - |
| 1.9 | 1900 | 0.6307 | - |
| 1.925 | 1925 | 0.6437 | - |
| 1.95 | 1950 | 0.6333 | - |
| 1.975 | 1975 | 0.6214 | - |
| 2.0 | 2000 | 0.6424 | 0.6765 |
| 2.025 | 2025 | 0.7041 | - |
| 2.05 | 2050 | 0.7112 | - |
| 2.075 | 2075 | 0.6696 | - |
| 2.1 | 2100 | 0.6739 | - |
| 2.125 | 2125 | 0.6315 | - |
| 2.15 | 2150 | 0.7649 | - |
| 2.175 | 2175 | 0.7079 | - |
| 2.2 | 2200 | 0.6549 | - |
| 2.225 | 2225 | 0.6548 | - |
| 2.25 | 2250 | 0.6647 | - |
| 2.275 | 2275 | 0.7568 | - |
| 2.3 | 2300 | 0.6659 | - |
| 2.325 | 2325 | 0.604 | - |
| 2.35 | 2350 | 0.6342 | - |
| 2.375 | 2375 | 0.6891 | - |
| 2.4 | 2400 | 0.6856 | - |
| 2.425 | 2425 | 0.6683 | - |
| 2.45 | 2450 | 0.678 | - |
| 2.475 | 2475 | 0.7102 | - |
| 2.5 | 2500 | 0.6606 | - |
| 2.525 | 2525 | 0.6634 | - |
| 2.55 | 2550 | 0.6443 | - |
| 2.575 | 2575 | 0.6122 | - |
| 2.6 | 2600 | 0.6926 | - |
| 2.625 | 2625 | 0.5957 | - |
| 2.65 | 2650 | 0.6933 | - |
| 2.675 | 2675 | 0.691 | - |
| 2.7 | 2700 | 0.7015 | - |
| 2.725 | 2725 | 0.7057 | - |
| 2.75 | 2750 | 0.6386 | - |
| 2.775 | 2775 | 0.6868 | - |
| 2.8 | 2800 | 0.6992 | - |
| 2.825 | 2825 | 0.6338 | - |
| 2.85 | 2850 | 0.6175 | - |
| 2.875 | 2875 | 0.6125 | - |
| 2.9 | 2900 | 0.6675 | - |
| 2.925 | 2925 | 0.6369 | - |
| 2.95 | 2950 | 0.6468 | - |
| 2.975 | 2975 | 0.6627 | - |
| 3.0 | 3000 | 0.6685 | 0.6765 |
</details>
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.44.1
- PyTorch: 2.3.0
- Accelerate: 0.33.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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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