| ---
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| tags:
|
| - sentence-transformers
|
| - sentence-similarity
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| - feature-extraction
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| - dense
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| - generated_from_trainer
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| - dataset_size:3763
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| - loss:MultipleNegativesRankingLoss
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| base_model: sentence-transformers/all-mpnet-base-v2
|
| widget:
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| - source_sentence: request increase server capacity dear customer support ask upgrade
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| server capacity optimize database improve scalability performance saas project
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| management platform current setup limit increase user volume lead long response
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| time reduced productivity ensure smooth user experience remain competitive extension
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| infrastructure necessary ask urgent review request timely solution please inform
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| u detail need process start
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| sentences:
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| - customer service update please write request update integration enhance compatibility
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| across multiple product within scalable saas project management platform aim improve
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| user experience increase efficiency
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| - system failure data synchronization problem detect incident impact several product
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| result system crash data synchronization error root cause suspect server overload
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| lead integration failure effort resolve include restarting service clear cache
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| review log problem persist scalability challenge likely underlying issue
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| - ' customer support inquire updating security protocol software integration within
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| hospital system objective improve data protection compliance ensure confidentiality
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| integrity patient information would like know solution available process implement
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| update could please provide detail matter thank assistance look forward prompt
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| response best regard thank support'
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| - source_sentence: medical data security problem dear customer service would like
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| contact medical data loss problem ubuntu server loss problem may occur due weak
|
| password policy outdated cisco io try solve problem change password update joomla
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| plugins however need additional support secure security system could please give
|
| u instruction follow investigation recommendation avoid future loss problem would
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| appreciate help provide matter thank understand support sincerely name
|
| sentences:
|
| - need technical assistance digital campaign halt due technical problem might relate
|
| old software malware already reboot system verify update perform antivirus scan
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| yet issue remain unresolved
|
| - inquiry clickup feature financial firm hello customer support write inquire clickup
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| feature optimize investment analytics financial firm representative financial
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| firm interest learn clickup help u streamline investment analysis process enhance
|
| performance specifically would like know follow feature 1 customizable dashboard
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| clickup provide customizable dashboard allow u track key performance indicator
|
| kpis metric relevant investment analytics 2 automate workflow clickup automate
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| workflow task related investment analysis data collection processing report 3
|
| integration tool clickup integrate tool platform use investment analysis data
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| provider risk management system portfolio management software 4 collaboration
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| communication clickup facilitate collaboration communication among team member
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| stakeholder involved investment analysis portfolio manager analyst risk manager
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| 5 data visualization clickup provide data visualization capability enable u easily
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| interpret understand complex investment data analytics would appreciate information
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| feature benefit financial firm additionally request demo trial clickup see firsthand
|
| feature apply specific need thank time assistance look forward hear back soon
|
| - multiple equipment failure dear customer support n ni encounter concurrent failure
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| across various office gadget include soundbar ring light surface pro smart doorbell
|
| hdmi cable thinkpad usb drive desktop computer air purifier vr headset issue significantly
|
| hamper workflow suspect recent power surge network outage might cause despite
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| restart device inspect cable thoroughly problem remain unresolved unaltered
|
| - source_sentence: digital tool operational digital strategy tool use marketing agency
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| experience malfunction restart device update application resolve problem
|
| sentences:
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| - immediate attention need zoom screen share issue dear customer support write report
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| high priority technical issue zoom version 5 11 0 screen share feature not work
|
| video conference affect team productivity require urgent resolution please address
|
| matter early convenience thank name company
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| - problem website integration not work website social medium integration cease function
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| might due api connection problem restart server verified configuration setting
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| issue remain unresolved
|
| - ' data analytics tool occasionally fail process investment data expect might due
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| recent software update increase data volume restart system check basic configuration
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| issue still persist assistance need resolve problem'
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| - source_sentence: request additional server administration hello customer support
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| hope message find well currently partnership continuous solution require additional
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| support server management expand operation require improve supervision server
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| system maintain efficiency security please let u know available option associate
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| cost change current agreement look forward continue productive partnership thank
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| attention request
|
| sentences:
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| - issue access notion today employee report difficulty access notion microsoft dynamic
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| 365 try resetting password clear cache instal late software update without success
|
| - warranty request dear customer service write inquire option available extended
|
| warranty dell xps 13 9310 ultrabook recently purchase model high performance specification
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| want make sure remain protect could please tell detail plan cost runtimes thank
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| support matter sincerely name tel num acc num
|
| - integration problem report dear support team experience integration issue impact
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| system functionality specifically several tool fail synchronize data essential
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| daily operation believe problem may stem api authentication difficulty face similar
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| issue despite attempt restart service verify credential issue still persist kindly
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| request address matter promptly provide u solution please inform u require additional
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| information u thank assistance
|
| - source_sentence: difference invoice new tariff change marketing agency complain
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| billing difference multiple subscription due new service change overlap fee change
|
| price level could behind try contact customer service check account statement
|
| hop problem solve please help u solve problem
|
| sentences:
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| - payment problem identify recently encounter issue subscription payment decline
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| problem might due insufficient fund card expiration verify card detail ensure
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| sufficient balance issue still persist
|
| - problem digital strategy digital strategy tool provide marketing agency malfunction
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| unexpectedly may software compatibility configuration problem attempt resolve
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| include restart system update software verify connection kindly assist resolve
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| issue promptly reduce disruption
|
| - ' deploy advance security measure secure medical information across interconnect
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| hospital device system'
|
| pipeline_tag: sentence-similarity
|
| library_name: sentence-transformers
|
| ---
|
|
|
| # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
|
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-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
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| - **Model Type:** Sentence Transformer
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| - **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision e8c3b32edf5434bc2275fc9bab85f82640a19130 -->
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| - **Maximum Sequence Length:** 384 tokens
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| - **Output Dimensionality:** 768 dimensions
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| - **Similarity Function:** Cosine Similarity
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| <!-- - **Training Dataset:** Unknown -->
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| <!-- - **Language:** Unknown -->
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| <!-- - **License:** Unknown -->
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|
|
| ### Model Sources
|
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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|
|
| ### Full Model Architecture
|
|
|
| ```
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| SentenceTransformer(
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| (0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
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| (1): Pooling({'word_embedding_dimension': 768, '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})
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| (2): Normalize()
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| )
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| ```
|
|
|
| ## Usage
|
|
|
| ### Direct Usage (Sentence Transformers)
|
|
|
| First install the Sentence Transformers library:
|
|
|
| ```bash
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| pip install -U sentence-transformers
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| ```
|
|
|
| Then you can load this model and run inference.
|
| ```python
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| from sentence_transformers import SentenceTransformer
|
|
|
| # Download from the 🤗 Hub
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| model = SentenceTransformer("sentence_transformers_model_id")
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| # Run inference
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| sentences = [
|
| 'difference invoice new tariff change marketing agency complain billing difference multiple subscription due new service change overlap fee change price level could behind try contact customer service check account statement hop problem solve please help u solve problem',
|
| 'payment problem identify recently encounter issue subscription payment decline problem might due insufficient fund card expiration verify card detail ensure sufficient balance issue still persist',
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| 'problem digital strategy digital strategy tool provide marketing agency malfunction unexpectedly may software compatibility configuration problem attempt resolve include restart system update software verify connection kindly assist resolve issue promptly reduce disruption',
|
| ]
|
| embeddings = model.encode(sentences)
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| print(embeddings.shape)
|
| # [3, 768]
|
|
|
| # Get the similarity scores for the embeddings
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| similarities = model.similarity(embeddings, embeddings)
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| print(similarities)
|
| # tensor([[1.0000, 0.7374, 0.5017],
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| # [0.7374, 1.0000, 0.3963],
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| # [0.5017, 0.3963, 1.0000]])
|
| ```
|
|
|
| <!--
|
| ### Direct Usage (Transformers)
|
|
|
| <details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Downstream Usage (Sentence Transformers)
|
|
|
| You can finetune this model on your own dataset.
|
|
|
| <details><summary>Click to expand</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Out-of-Scope Use
|
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| -->
|
|
|
| <!--
|
| ## 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.*
|
| -->
|
|
|
| <!--
|
| ### Recommendations
|
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| -->
|
|
|
| ## Training Details
|
|
|
| ### Training Dataset
|
|
|
| #### Unnamed Dataset
|
|
|
| * Size: 3,763 training samples
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| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
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| * Approximate statistics based on the first 1000 samples:
|
| | | sentence_0 | sentence_1 | label |
|
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| | type | string | string | float |
|
| | details | <ul><li>min: 5 tokens</li><li>mean: 52.91 tokens</li><li>max: 267 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 53.77 tokens</li><li>max: 267 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
|
| * Samples:
|
| | sentence_0 | sentence_1 | label |
|
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
|
| | <code>intern network issue saas application experience temporary connectivity issue saas application network instability misconfigurations might cause please restart tp link switch netgear router</code> | <code>problem application crash peak usage application crash unexpectedly peak usage hour may due database overload resource constraint although attempt make optimize sql query reduce load step not successful would greatly appreciate assistance resolve issue prevent future crash ensure good user experience</code> | <code>1.0</code> |
|
| | <code>request update discord drupal hello customer support contact request update discord drupal integration digital marketing effort heavily depend platform think improve integration greatly enhance track performance give fast paced environment digital marketing essential stay forefront optimize tool increase reach engagement could please examine provide solution fit requirement additional step need undertake detail need please let know thank time assistance look forward response</code> | <code>update require immediately please integration must update good compatibility</code> | <code>1.0</code> |
|
| | <code> dear customer support contact address problem integration multiple application unexpectedly stop work believe issue might due recent api modification excessive server load despite effort reset service review logs test application independently problem remain unresolved kindly request examine situation offer prompt solution please inform information end necessary address issue thank attention cooperation sincerely name</code> | <code>integration problem jira clickup dear support team would like report integration issue jira clickup synchronization error suddenly appear night suspect might relate api change try restart server review log issue persists would appreciate could look matter offer solution soon possible please let know need additional information resolve issue thank time assistance look forward prompt response</code> | <code>1.0</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",
|
| "gather_across_devices": false
|
| }
|
| ```
|
|
|
| ### Training Hyperparameters
|
| #### Non-Default Hyperparameters
|
|
|
| - `num_train_epochs`: 5
|
| - `fp16`: True
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| - `multi_dataset_batch_sampler`: round_robin
|
|
|
| #### All Hyperparameters
|
| <details><summary>Click to expand</summary>
|
|
|
| - `overwrite_output_dir`: False
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| - `do_predict`: False
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| - `eval_strategy`: no
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| - `prediction_loss_only`: True
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| - `per_device_train_batch_size`: 8
|
| - `per_device_eval_batch_size`: 8
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| - `per_gpu_train_batch_size`: None
|
| - `per_gpu_eval_batch_size`: None
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| - `gradient_accumulation_steps`: 1
|
| - `eval_accumulation_steps`: None
|
| - `torch_empty_cache_steps`: None
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| - `learning_rate`: 5e-05
|
| - `weight_decay`: 0.0
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| - `adam_beta1`: 0.9
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| - `adam_beta2`: 0.999
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| - `adam_epsilon`: 1e-08
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| - `max_grad_norm`: 1
|
| - `num_train_epochs`: 5
|
| - `max_steps`: -1
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| - `lr_scheduler_type`: linear
|
| - `lr_scheduler_kwargs`: {}
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| - `warmup_ratio`: 0.0
|
| - `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
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| - `no_cuda`: False
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| - `use_cpu`: False
|
| - `use_mps_device`: False
|
| - `seed`: 42
|
| - `data_seed`: None
|
| - `jit_mode_eval`: False
|
| - `use_ipex`: False
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| - `bf16`: False
|
| - `fp16`: True
|
| - `fp16_opt_level`: O1
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| - `half_precision_backend`: auto
|
| - `bf16_full_eval`: False
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| - `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`: batch_sampler
|
| - `multi_dataset_batch_sampler`: round_robin
|
| - `router_mapping`: {}
|
| - `learning_rate_mapping`: {}
|
|
|
| </details>
|
|
|
| ### Training Logs
|
| | Epoch | Step | Training Loss |
|
| |:------:|:----:|:-------------:|
|
| | 1.0616 | 500 | 1.8284 |
|
| | 2.1231 | 1000 | 1.5296 |
|
| | 3.1847 | 1500 | 1.3349 |
|
| | 4.2463 | 2000 | 1.1681 |
|
|
|
|
|
| ### Framework Versions
|
| - Python: 3.12.7
|
| - Sentence Transformers: 5.2.3
|
| - Transformers: 4.49.0
|
| - PyTorch: 2.6.0+cu124
|
| - Accelerate: 1.13.0
|
| - Datasets: 3.3.2
|
| - Tokenizers: 0.21.0
|
|
|
| ## 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}
|
| }
|
| ```
|
|
|
| <!--
|
| ## Glossary
|
|
|
| *Clearly define terms in order to be accessible across audiences.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Authors
|
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Contact
|
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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| --> |
