shivamgoel97/finetunesentence

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("shivamgoel97/finetunesentence")
# Run inference
sentences = [
    'What steps should the Fulfillment SPC Leadership take when an Internal Partner forwards an escalation request?',
    'Context: team (a selected group of offline individuals) completes all escalated requests. Escalations from a previous representative will not be reassigned to that representative rather assigned and handled through the Escalation team. The representative\'s supervisor receives real-time feedback to provide the representative immediate coaching and corrective action. Do not assign escalated requests to the Outbound team. The Escalations team owns all escalated requests. If a call to PACT is needed for credit approval, the Outbound team can assist but will not own the request. If more information is needed to complete the request, follow the AIR guidelines.Ways to Receive Escalated RequestsTable: <table border="1" cellpadding="5" cellspacing="0" width="100%"><tbody><tr><th style="text-align: left;" valign="top">If ...</th><th style="text-align: left;" valign="top">Then ...</th></tr><tr><td style="text-align: left;" valign="top">the reason for the escalation is due to a mistake on the original COE-processed application</td><td style="text-align: left;" valign="top"><p>do not reassign to the original coordinator.<br/><br/>Note:\xa0<span style="background-color: transparent;">The Escalation Representative assigned the request makes the necessary changes/corrections and completes the request.</span></p></td></tr><tr><td style="text-align: left;" valign="top">the reason for the escalation is a Missed SLA / Revenue Impacting Order<br/></td><td style="text-align: left;" valign="top"><ul><li>Do not reassign the escalation email request to the coordinator who has the related email if the related WFM is with a coordinator who does not belong to the Escalation team and is in\xa0<b>Wait</b>\xa0status.<br/></li><li>Own the escalated WFM and monitor the progress of the original request until completion.</li><li>Create a case to track completion of the request.</li><li>Be responsible for providing the updates to the Sales partner by sending an email with the status and timelines as possible.</li><li>Complete WFM as (Request Type) Correspondence.</li></ul></td></tr><tr><td style="text-align: left;" valign="top">the related WFM is with another Center/Team<br/></td><td style="text-align: left;" valign="top"><p>reassign any follow-up emails related to that request in order for that team to continue to own it.<br/></p><ul><li>Do not respond to provide an update.\xa0</li><li>Submit a reassignment request if needed.</li></ul></td></tr><tr><td style="text-align: left;" valign="top">Internal Partners forward the escalation request<br/></td><td style="text-align: left;" valign="top"><p>the Fulfillment SPC Leadership does the following:<br/></p><ol><li>Acknowledges the email.</li><li>Provides an update within 1 hour after acknowledgement.</li><li>Tracks completion until closure and provides updates as needed.<ul><li>If the assigned lead is out of the office or unavailable, another leader will cover on their behalf.</li></ul></li></ol></td></tr></tbody></table>',
    'Context: Program provides VZW B2B Data Sales Teams with a selection of key M2M certified devices for use in customer pilots pursuant to a sale. This program is intended to motivate and enable the VZW B2B Data Sales Teams to generate M2M sales. The program is available for Data Sales Teams residing in the 4 VZW B2B Sales Areas and the Enterprise and Government group. It contains a portfolio of key M2M devices that may provide a pilot at no cost to the customer when connected on area-supplied concession lines or test lines.Contact Nishant Shah [email protected] to request these solution-based devices:Table: <table border="1" cellpadding="5" width="100%"><tbody><tr><td style="text-align: left;" valign="top" width="30%"><b>Manufacturer</b></td><td style="text-align: left;" valign="top" width="70%"><b>Model</b></td></tr><tr><td style="text-align: left;" valign="top" width="30%">Cisco</td><td style="text-align: left;" valign="top" width="70%">Demo</td></tr></tbody></table>',
]
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: 7,057 training samples
• Columns: sentence_0, sentence_1, and label
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	int
  details	min: 11 tokens mean: 24.8 tokens max: 49 tokens	min: 91 tokens mean: 230.28 tokens max: 256 tokens	0: ~29.30% 1: ~70.70%

• Samples:

  sentence_0	sentence_1	label
  What steps should be taken if the appropriate ticket is not visible?	Context: appropriate ticket may not be visible. Click View Ticket Details for more information. Type in Ticket Number and click Search. Scroll to the bottom of the screen to view the Comments Log, which contains all the information about the ticket's creation and resolution. Tickets can also be located by picking a date range (Start Date and End Date), selecting Me as the Assigned To value and clicking Search to pull up all tickets created by the Representative doing the searching.Email the escalations mailbox and request the ticket be closed. Note: Cancel, Fraud Cancel and Address Change tickets cannot be undone.Table: Ticket rejection reason Solution Missing or invalid information After utilizing all available resource... 1 What are the steps to assign an Account Manager in My Verizon when the Account Manager does not have an MDN on the account? Context: and password The Account Manager: Is listed as an Authorized User in the Billing System and in My Verizon Can transact account business on My Verizon, My Verizon Express, in-store and by calling CS. Assign Account Manager In-Store or CS Access Only: Account Owner follows these steps: Note: This process can be used when the Account Manager does not have an MDN on the account. The Account Manager is listed as Authorized User in the billing system and has access to transact account business in-store or with CS. Adding an Account Manager is not restricted to a Digital Only Transaction.Table: Step Action 1 Sign in to My Verizon. 1 What should an agent do if a case is received with an executive handle tagged? Context: to send the form). W-9 addresses must always be the corporate address of record for the entity servicing the customer. W-9s are issued to claim exemption from withholding (i.e., 1099) and provide the name and address for information reporting. Payment Questions The EPO Support team is unable to answer any questions about payments and applications. For wireless customers, open a Payment Investigation Ticket through ACSS. For further assistance, search for “Accessing Payment Hub in ACSS” through InfoManager. For wireline customers, open a Payment Investigation Ticket through the Payment Hub. For further assistance, search for “Payment Hub Participants Guide” through VZKnowledge.Table: Step Action 1 0 Loss: CosineSimilarityLoss with these parameters: { "loss_fct": "torch.nn.modules.loss.MSELoss" } Training Hyperparameters Non-Default Hyperparameters multi_dataset_batch_sampler: round_robin All Hyperparameters Click to expand overwrite_output_dir: False do_predict: False eval_strategy: no prediction_loss_only: True per_device_train_batch_size: 8 per_device_eval_batch_size: 8 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: 5e-05 weight_decay: 0.0 adam_beta1: 0.9 adam_beta2: 0.999 adam_epsilon: 1e-08 max_grad_norm: 1 num_train_epochs: 3 max_steps: -1 lr_scheduler_type: linear lr_scheduler_kwargs: {} 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 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 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 Training Logs Epoch Step Training Loss 0.5663 500 0.111 1.1325 1000 0.0838 1.6988 1500 0.0721 2.2650 2000 0.061 2.8313 2500 0.0588 Framework Versions Python: 3.10.12 Sentence Transformers: 3.3.1 Transformers: 4.47.1 PyTorch: 2.5.1+cu121 Accelerate: 1.2.1 Datasets: 3.2.0 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", }