This is a sentence-transformers model finetuned from microsoft/mpnet-base on the wsc_queries_with_negatives dataset. 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

Model Type: Sentence Transformer
Base model: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- wsc_queries_with_negatives

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': 512, 'do_lower_case': False, 'architecture': 'MPNetModel'})
  (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})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("AL3110/mpnet-wsc-finetuned-triplet")
# Run inference
queries = [
    "What should I do if I get injured while working from my home office?",
]
documents = [
    'You are expected to report the injury via the Incident Reporting Form within 48 hours of the occurrence.',
    "Yes, your manager can approve workflows in your queue by using the 'switch to' option in WorklistPlus.",
    'Participating countries/regions include Bangladesh, China, Guam, Hong Kong, Macau, Pakistan, Philippines, Singapore, Sri Lanka, and Thailand. Australia and New Zealand have a separate shutdown period.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9211, 0.9136, 0.9134]])

Training Details

Training Dataset

wsc_queries_with_negatives

Dataset: wsc_queries_with_negatives at d342836
Size: 35 training samples
Columns: query, answer, and negative_answer
Approximate statistics based on the first 35 samples:
query answer negative_answer
type string string string
details
min: 11 tokens
mean: 16.54 tokens
max: 30 tokens
min: 18 tokens
mean: 37.71 tokens
max: 58 tokens
min: 18 tokens
mean: 38.57 tokens
max: 71 tokens

Samples:

query	answer	negative_answer
`What is Oracle's policy on drugs and alcohol in the workplace?`	`Oracle maintains a strict policy prohibiting the use, possession, sale, or transfer of illegal drugs at any time while conducting company business. Reporting to work under the influence of alcohol or illegal drugs is also prohibited.`	`After discussing with your manager, you can modify your WSC in HCM by navigating to: Me >> Journeys >> Request Workspace Category Journey >> Select Workspace Category Details form >> Complete the details and Submit.`
`If I change my WSC status from Assigned to Remote, do I need to raise another transaction to change it back after a certain period?`	`No, you do not need to initiate another transaction unless you need to modify your WSC again in the future. The change has no end date.`	`This category applies to employees who have access to workspaces within their local office but do not have a permanently designated space.`
`What should I do if my manager asks me to work remotely or flex?`	`First, have a conversation with your manager to finalize the arrangement and start date. Then, log into HCM to submit a transaction to change your workspace category. You can track its status on WorklistPlus.`	`No, managers are not authorized to issue letters directly on behalf of Oracle. They can provide a personal testimony, but it cannot be on Oracle letterhead or imply it is from the company.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Evaluation Dataset

wsc_queries_with_negatives

Dataset: wsc_queries_with_negatives at d342836
Size: 9 evaluation samples
Columns: query, answer, and negative_answer
Approximate statistics based on the first 9 samples:
query answer negative_answer
type string string string
details
min: 10 tokens
mean: 16.33 tokens
max: 26 tokens
min: 19 tokens
mean: 36.11 tokens
max: 71 tokens
min: 19 tokens
mean: 32.78 tokens
max: 54 tokens

Samples:

query	answer	negative_answer
`Can I work from a different country?`	`Working from another country is generally not permitted unless approved under specific circumstances, as Oracle requires employees to live and work in the country where they are paid. You should refer to the ‘Living and Working in Payroll Country Policy’ for details.`	`You will receive a notification from WorklistPlus with the reason for rejection. You should discuss the reason with your manager or the relevant team (HR, RE&F) and then reinitiate the workflow from Me >> Journeys >> Completed.`
`When is the JAPAC Year-End Break for 2024?`	`The Oracle JAPAC Year-End Break is from December 26 to December 31, 2024.`	`The system is currently configured to allow these changes to be initiated only by the employee.`
`What is the definition of the 'Flexible' workspace category?`	`This category applies to employees who have access to workspaces within their local office but do not have a permanently designated space.`	`No, there is no end date. Once your WSC is approved, it remains active until you or your manager initiate another change.`

Loss: TripletLoss with these parameters:
json
1{ 2 "distance_metric": "TripletDistanceMetric.EUCLIDEAN", 3 "triplet_margin": 5 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
learning_rate: 2e-05
num_train_epochs: 5
warmup_ratio: 0.1
fp16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

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: 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: 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: 5
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_fused
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
hub_revision: None
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
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
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
1.0	5	-	5.2933
2.0	10	5.2335	5.3693
3.0	15	-	5.4665
4.0	20	4.8863	5.5259
5.0	25	-	5.5477

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.10
Sentence Transformers: 5.1.0
Transformers: 4.55.4
PyTorch: 2.8.0+cpu
Accelerate: 1.10.0
Datasets: 4.0.0
Tokenizers: 0.21.4

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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

	query	answer	negative_answer
type	string	string	string
details	min: 11 tokens mean: 16.54 tokens max: 30 tokens	min: 18 tokens mean: 37.71 tokens max: 58 tokens	min: 18 tokens mean: 38.57 tokens max: 71 tokens