metadata

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 model finetuned from 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
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 tokens
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': 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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Training Details

Training Dataset

Unnamed Dataset

Size: 8,000 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 21 tokens
mean: 22.0 tokens
max: 24 tokens

min: 46 tokens
mean: 51.98 tokens
max: 56 tokens

	query	answer
type	string	string
details	min: 21 tokens mean: 22.0 tokens max: 24 tokens	min: 46 tokens mean: 51.98 tokens max: 56 tokens

Samples:

query	answer
`As a user, I want to search for products by name so that I can find specific items.`	`1. Test the search functionality by entering a product name and verifying that the results include the correct product. 2. Ensure the search returns results quickly. 3. Verify that searching for non-existent products returns no results.`
`As an admin, I want to add new users to the system so that I can manage user accounts.`	`1. Confirm that an admin can access the 'Add New User' form. 2. Verify that the form allows entering user details and submitting. 3. Check that the new user is added to the user list after submission.`
`As a user, I want to search for products by name so that I can find specific items.`	`1. Test the search functionality by entering a product name and verifying that the results include the correct product. 2. Ensure the search returns results quickly. 3. Verify that searching for non-existent products returns no results.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 2,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 21 tokens
mean: 22.0 tokens
max: 24 tokens

min: 46 tokens
mean: 51.54 tokens
max: 56 tokens

	query	answer
type	string	string
details	min: 21 tokens mean: 22.0 tokens max: 24 tokens	min: 46 tokens mean: 51.54 tokens max: 56 tokens

Samples:

query	answer
`As a user, I want to reset my password via email so that I can regain access.`	`1. Verify that the password reset email is sent to the user's registered email address. 2. Ensure the email contains a password reset link. 3. Test the password reset link to confirm it allows setting a new password.`
`As a customer, I want to receive notifications for order status updates so that I stay informed.`	`1. Check that notifications are sent for order status changes. 2. Verify that notifications include accurate order details. 3. Test the notification settings to ensure users can customize their preferences.`
`As a user, I want to view my order history so that I can track my previous purchases.`	`1. Ensure the user can access the 'Order History' page. 2. Verify that the page displays previous orders correctly. 3. Test the ability to filter orders by date or status.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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

Click to expand

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

Training Logs

Click to expand

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

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

@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

@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}
}