ToS-Sentence-Transformers / README.md

AryehRotberg

Add new SentenceTransformer model

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:150468
  - loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      Third-party payment providers, such as Stripe, Checkout. Coingate and
      similar they help us to process payments together with our own authorized
      payment processing companiesUnited States, Ireland, BVIStorage and
      infrastructure service providers, such as BigQuery (by Google), Stitch (by
      Talend)they help us to deliver targeted advertising to the Website
      visitors United StatesLive chat and support service providers, such as
      Zendesk we use them to provide live chat technology and provide support to
      our users United StatesSecurity service providers, such as Cloudflare we
      work with them to provide improved security and performance United
      StatesAttorneys, notaries, bailiffs we transfer personal information in
      cases when we seek to defend our rights and legal interests
    sentences:
      - Many third parties are involved in operating the service
      - Only aggregate data is given to third parties
      - >-
        You should revisit the terms periodically, although in case of material
        changes, the service will notify
  - source_sentence: >-
      The Privacy and Cookie Policy states: "Please note, however, that by
      blocking or deleting cookies used on the Service, you may not be able to
      take full advantage of the Service and you may not be able to log on to
      the Service or play the Roblox games."
    sentences:
      - Blocking first party cookies may limit your ability to use the service
      - There is a date of the last update of the agreements
      - Other applicable rules, terms, conditions or guidelines
  - source_sentence: >-
      You may also request a copy of your data by (a) logging into your Swarm
      account or (b) logging into your City Guide account (web only) and
      clicking on “Export My Data” in your privacy settings. You may also delete
      your data and
    sentences:
      - The court of law governing the terms is in location X
      - This Service provides a list of Third Parties involved in its operation.
      - You can request access, correction and/or deletion of your data
  - source_sentence: >-
      Conducting relevant promotional activities, such as providing marketing
      and promotional materials and updates.
    sentences:
      - >-
        Your personal data may be sold or otherwise transferred as part of a
        bankruptcy proceeding or other type of financial transaction
      - >-
        You authorise the service to charge a credit card supplied on
        re-occurring basis
      - Your personal data may be used for marketing purposes
  - source_sentence: >-
      Pexgle will need to share your information, including personal
      information, in order to ensure the adequate performance of our contract
      with you.
    sentences:
      - IP addresses of website visitors are not tracked
      - Extra data may be collected about you through promotions
      - >-
        This service gives your personal data to third parties involved in its
        operation
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: all nli dev
          type: all-nli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9992556571960449
            name: Cosine Accuracy

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 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': 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("AryehRotberg/ToS-Sentence-Transformers")
# Run inference
sentences = [
    'Pexgle will need to share your information, including personal information, in order to ensure the adequate performance of our contract with you.',
    'This service gives your personal data to third parties involved in its operation',
    'Extra data may be collected about you through promotions',
]
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]

Evaluation

Metrics

Triplet

Dataset: all-nli-dev
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9993

Training Details

Training Dataset

Unnamed Dataset

Size: 150,468 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 4 tokens mean: 48.6 tokens max: 256 tokens	min: 6 tokens mean: 14.72 tokens max: 29 tokens	min: 4 tokens mean: 14.26 tokens max: 29 tokens

Samples:

anchor	positive	negative
`For all User Submissions, you hereby grant Guilded a license to translate, modify (for technical purposes, for example, making sure your content is viewable on a mobile device as well as a computer) and reproduce and otherwise act with respect to such User Submissions, in each case to enable us to operate the Services, as described in more detail below.`	`Copyright license limited for the purposes of that same service but transferable and sublicenseable`	`You are prohibited from sending chain letters, junk mail, spam or any unsolicited messages`
`Our data is stored in the EU or USA with robust physical, digital, and procedural safeguards in place to protect your personal data, including the use of SSL encryption, redundant servers and data centers, and sophisticated perimeter security. We continuously audit for security vulnerabilities and make software patching a priority.`	`Information is provided about security practices`	`The service disables software that you are not licensed to use.`
No part of our Platform may be reproduced in any form or incorporated into any information retrieval system, electronic or mechanical, other than for your personal use. While using our Platform, you cannot redistribute your license (“Premium”, “Pro”, “Lite”) to anyone in any way that can make them use the features bound to your account. Unless otherwise specified, the developer tools and components, download areas, communication forums, and product information are for your personal and non-commercial use.	`This service is only available for use individually and non-commercially.`	`Accessibility to this service is guaranteed at 99% or more`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 37,617 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 45.61 tokens max: 256 tokens	min: 6 tokens mean: 14.64 tokens max: 29 tokens	min: 6 tokens mean: 14.26 tokens max: 29 tokens

Samples:

anchor	positive	negative
`non-exclusive, worldwide right and license to use,`	`The service has non-exclusive use of your content`	`You are not being tracked`
`We also reserve the right to suspend or end the Service at any time at our discretion and without notice. For example, we may suspend or terminate your use of the Service and remove Your Content if you’re not complying with these AUP Guidelines, or using the Service in a manner that may cause us legal liability, disrupt the Service, disrupt others’ use of the Service or, in our sole opinion, reason, cause harm.`	`Your account can be deleted or permanently suspended without prior notice and without a reason`	`The service claims to be CCPA compliant for California users`
`ExpressVPN uses mobile identifiers to generate statistics related to the marketing channels and advertising partners through which users learned about and signed up for ExpressVPN mobile apps.`	`You are tracked via web beacons, tracking pixels, browser fingerprinting, and/or device fingerprinting`	`Your personal data is used for advertising`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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
fp16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 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: 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
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
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

Training Logs

Epoch	Step	Training Loss	Validation Loss	all-nli-dev_cosine_accuracy
-1	-1	-	-	0.9527
0.0106	100	1.3092	1.1396	0.9620
0.0213	200	1.0389	0.8936	0.9742
0.0319	300	0.8838	0.7500	0.9793
0.0425	400	0.7582	0.6477	0.9843
0.0532	500	0.6358	0.5727	0.9871
0.0638	600	0.6451	0.5158	0.9889
0.0744	700	0.4932	0.4715	0.9903
0.0851	800	0.4865	0.4355	0.9913
0.0957	900	0.4636	0.4035	0.9927
0.1063	1000	0.4406	0.3846	0.9930
0.1170	1100	0.3824	0.3691	0.9934
0.1276	1200	0.3967	0.3411	0.9944
0.1382	1300	0.3448	0.3264	0.9945
0.1489	1400	0.3372	0.3018	0.9955
0.1595	1500	0.3035	0.2941	0.9959
0.1701	1600	0.319	0.2864	0.9956
0.1808	1700	0.292	0.2743	0.9964
0.1914	1800	0.2647	0.2727	0.9965
0.2020	1900	0.2948	0.2517	0.9968
0.2127	2000	0.2583	0.2456	0.9971
0.2233	2100	0.2685	0.2352	0.9970
0.2339	2200	0.2879	0.2327	0.9969
0.2446	2300	0.2366	0.2271	0.9972
0.2552	2400	0.231	0.2164	0.9972
0.2658	2500	0.2639	0.2124	0.9973
0.2764	2600	0.2543	0.2078	0.9976
0.2871	2700	0.2261	0.2043	0.9972
0.2977	2800	0.2239	0.1976	0.9978
0.3083	2900	0.2271	0.1932	0.9977
0.3190	3000	0.2334	0.1845	0.9979
0.3296	3100	0.2021	0.1867	0.9981
0.3402	3200	0.2237	0.1762	0.9984
0.3509	3300	0.2109	0.1730	0.9983
0.3615	3400	0.2047	0.1663	0.9985
0.3721	3500	0.1904	0.1629	0.9984
0.3828	3600	0.1687	0.1643	0.9984
0.3934	3700	0.2071	0.1584	0.9984
0.4040	3800	0.1609	0.1543	0.9983
0.4147	3900	0.1862	0.1525	0.9984
0.4253	4000	0.1925	0.1504	0.9984
0.4359	4100	0.1714	0.1484	0.9985
0.4466	4200	0.2025	0.1472	0.9985
0.4572	4300	0.1427	0.1422	0.9986
0.4678	4400	0.1458	0.1401	0.9986
0.4785	4500	0.1796	0.1371	0.9985
0.4891	4600	0.1289	0.1317	0.9987
0.4997	4700	0.1427	0.1298	0.9988
0.5104	4800	0.1349	0.1313	0.9988
0.5210	4900	0.149	0.1293	0.9987
0.5316	5000	0.1633	0.1230	0.9988
0.5423	5100	0.1241	0.1240	0.9988
0.5529	5200	0.1532	0.1196	0.9988
0.5635	5300	0.1547	0.1173	0.9988
0.5742	5400	0.1652	0.1167	0.9990
0.5848	5500	0.1505	0.1120	0.9989
0.5954	5600	0.1309	0.1106	0.9990
0.6061	5700	0.1648	0.1089	0.9988
0.6167	5800	0.118	0.1070	0.9988
0.6273	5900	0.1207	0.1062	0.9988
0.6380	6000	0.1104	0.1046	0.9989
0.6486	6100	0.1262	0.1040	0.9989
0.6592	6200	0.1236	0.1008	0.9990
0.6699	6300	0.122	0.1005	0.9990
0.6805	6400	0.1244	0.1005	0.9991
0.6911	6500	0.1176	0.0998	0.9991
0.7018	6600	0.1215	0.0994	0.9991
0.7124	6700	0.1079	0.0983	0.9991
0.7230	6800	0.1099	0.0957	0.9991
0.7337	6900	0.1121	0.0950	0.9992
0.7443	7000	0.1137	0.0942	0.9992
0.7549	7100	0.1082	0.0929	0.9991
0.7656	7200	0.1047	0.0923	0.9991
0.7762	7300	0.1147	0.0904	0.9992
0.7868	7400	0.1336	0.0895	0.9991
0.7974	7500	0.1122	0.0889	0.9992
0.8081	7600	0.1126	0.0884	0.9993
0.8187	7700	0.116	0.0864	0.9992
0.8293	7800	0.0991	0.0857	0.9992
0.8400	7900	0.1091	0.0851	0.9992
0.8506	8000	0.1052	0.0846	0.9993
0.8612	8100	0.1105	0.0839	0.9992
0.8719	8200	0.1101	0.0836	0.9992
0.8825	8300	0.107	0.0832	0.9993
0.8931	8400	0.0867	0.0827	0.9993
0.9038	8500	0.0965	0.0823	0.9992
0.9144	8600	0.1108	0.0817	0.9993
0.9250	8700	0.1219	0.0814	0.9992
0.9357	8800	0.1169	0.0809	0.9992
0.9463	8900	0.0964	0.0805	0.9992
0.9569	9000	0.0939	0.0804	0.9992
0.9676	9100	0.0955	0.0803	0.9993
0.9782	9200	0.1076	0.0800	0.9993
0.9888	9300	0.1049	0.0798	0.9992
0.9995	9400	0.0826	0.0798	0.9993

Framework Versions

Python: 3.9.19
Sentence Transformers: 4.0.2
Transformers: 4.48.1
PyTorch: 2.4.1+cu124
Accelerate: 1.6.0
Datasets: 2.21.0
Tokenizers: 0.21.0

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}
}