metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:182
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: What documents must contractors/vendors provide?
    sentences:
      - >-
        1. ESH representatives will carry out the training when new employees
        need to be trained, or on an annual basis.
      - >-
        1. Safe Operating Procedure (SOP). 

        2. Risk Assessment ( Hazard Identification, Risk Assessment, & Risk
        control / HIRARC) / JSA / Job Safety Analysis. 

        3. Valid licenses (If applicable). 

        4. Certification of Fitness-CF (For all types of cranes). 

        5. Crane Operator Competency License. (If applicable). 

        6. All scaffolding must be erected as per the statutory regulations. 

        7. Lifting Supervisor Competency Certificate. (If applicable). 

        8. Signal Man Competency Certificate. (If applicable. 

        9. Rigger Competency Certificate. (If applicable). 

        10. Lifting plan (If applicable). 

        11. Scaffolder Level 1/2/3 Certificate. (If applicable).
      - >-
        1. To ensure the specific employees are aware of the correct procedures
        associated with chemical handling and waste management.
  - source_sentence: What is the guideline for shirts and blouses?
    sentences:
      - >-
        1. ESH representatives will carry out the training when new employees
        need to be trained, or on an annual basis.
      - 1. Employees in CLEAN ROOM are NOT ALLOWED to use/wear makeup/bangles.
      - |-
        1. 1. Formal or casual shirts with sleeves. 
        2. 2. Collared T-shirts and blouses/sleeveless tops (for ladies). 
        3. 3. Round-neck T-shirts are allowed for non-office personnel. 
        4. 4. Clothing with the company logo is encouraged. 
        5. 5. Sport Team. 
        6. 6. University. 
        7. 7. Fashion brands on clothing are generally acceptable.
  - source_sentence: >-
      What is the lunch schedule for the 1st shift in the normal schedule in
      M-site?
    sentences:
      - 12 days.
      - >-
        1. Categorization of Machine: Identify the location of the machine, its
        function, and all necessary items needed for it to run (e.g.,
        lubricants, saw blades, etc).

        2. Authorization: Ensure that all personnel operating the machine have
        received the appropriate training.

        3. Hazard & Risks associated with
        equipment/machinery/techniques/process: Identify all hazards and risks
        associated, and implement sufficient controls according to the hierarchy
        of controls (e.g., warning labels and symbols).

        4. Pre-work procedure: Ensure that the machine is in proper, running
        condition before starting work.

        5. During work procedure: Follow the correct standard operating
        procedure for carrying out that work activity.

        6. After work procedure: Ensure that the machine remains in a neat and
        tidy condition at all times.

        7. Work Area: Identify the area where the work is being done.

        8. PPE: Ensure that appropriate PPE is available for all personnel
        handling the machine.

        9. Emergency Procedure: Ensure sufficient emergency features are
        available on the machine (e.g., emergency stop button).

        10. After work hour: Ensure the machine system is in shutdown/standby
        mode when the machine is not running.

        11. Housekeeping: Ensure basic housekeeping is done at the work area.

        12. Scheduled waste: Any scheduled waste generated by the process should
        be disposed of according to Carsem waste management procedure.
      - >-
        1. Lunch (Tengah Hari) for the 1st shift is from 12:00 PM to 1:00 PM,
        lasting 60 minutes.
  - source_sentence: What is the meal schedule for M-site?
    sentences:
      - 2 days.
      - >-
        1. 1st Shift: -Dinner (Malam): 8:00PM - 8:40PM, -Supper(Lewat Malam):
        1:00AM - 1:30 AM -Breakfast(Pagi): 8:00AM - 8:30AM -Lunch(Tengah Hari):
        12:50PM - 1:30PM. 

        2. 2nd Shift: -Dinner(Malam): 8:50PM - 9:30PM -Supper(Lewat Malam):
        1:40AM - 2:10AM -Breakfast(Pagi): 8:40AM - 9:10AM -Lunch(Tengah Hari):
        1:40PM - 2:20PM. 

        3. 3rd Shift: -Dinner(Malam): 9:40PM - 10:20PM -Supper(Lewat Malam):
        2:20AM - 2:50AM -Breakfast(Pagi): 9:20AM - 9:50AM -Lunch(Tengah Hari):
        2:30PM - 3:10PM. 

        4. 4th Shift: -Dinner(Malam): 10:30PM - 11:10PM -Supper(Lewat Malam):
        3:00AM - 3:30AM -Breakfast(Pagi): 10:00AM - 10:30AM -Lunch(Tengah Hari):
        3:20PM - 4:00PM.
      - >-
        1. The mechanical safety guidelines include: 

        2. 1. Lock-Out Tag-Out (LOTO): Always practice LOTO procedures when
        performing maintenance or repairs on machines. 

        3. 2. Preventive Maintenance: Conduct regular preventive maintenance on
        all machinery to ensure proper functioning. 

        4. 3. Pinch Points Awareness: Identify all possible pinch points on
        machinery, and ensure they are properly labeled. 

        5. 4. Production Area Organization: Keep the production area neat and
        organized at all times. 

        6. 5. Operator Training: Provide adequate training to operators before
        allowing them to handle machines. 

        7. 6. Machine Guarding: Ensure all safety guards are in place before
        starting machine operations.
  - source_sentence: Can employees wear traditional attire?
    sentences:
      - >-
        1. N03 : Monday to Friday, 8am to 5:30pm.

        2. N04 : Tuesday to Saturday, 8am to 5:30pm.

        3. N05 : Monday to Friday, 8:30am to 6pm.

        4. N06 : Monday to Friday, 9am to 6:30pm.

        5. N07 : Tuesday to Saturday, 8:30am to 6pm.

        6. N08 : Tuesday to Saturday, 9am to 6.30pm.

        7. N6 : Tuesday to Saturday, 8:30pm to 6:15pm.

        8. N9: 5 working days 2 days off, 7:30am to 5:15pm , 10:30am to 8:15pm.

        9. N10: 5 working days 2 days off, 10:30am to 8:15pm , 7:30am to 5:15pm.

        10. AA/BB/CC/A/B/C : 4 working days 2 days off, 6:30am to 6:30pm ,
        6:30pm to 6:30am.

        11. AA1/BB1/CC1/A1/B1/C1 : 4 working days 2 days off, 6:30am to 6:30pm ,
        6:30pm to 6:30am.

        12. GG/HH/II/GG1/HH1/II1 : 4 working days 2 days off, 7:30am to 7:30pm ,
        7:30pm to 7:30am.

        13. P1 : Monday to Thursday (4 working days 2 days off), 6:30am to
        6:30pm , 6:30pm to 6:30am.

        14. P2 : Tuesday to Friday (4 working days 2 days off), 6:30am to 6:30pm
        , 6:30pm to 6:30am. 

        15. U1/U2/U3/UU1/UU2/UU3 : 4 working days 2 days off, 7:30am to 7.30pm. 

        16. V1/V2/V3/VV1/VV2/VV3 : 4 working days 2 days off, 8.30am to 8.30pm. 

        17. W1/W2/W3/WW1/WW2/WW3 : 4 working days 2 days off, 6.30am to 6.30pm. 

        18. H1 : Monday to Thursday (4 working days 2 days off), 6.30am to
        6.30pm. 

        19. H2 : Tuesday to Friday (4 working days 2 days off), 6.30am to
        6.30pm. 

        20. H3 : Wednesday to Saturday (4 working days 2 days off), 6.30am to
        6.30pm. 

        21. H6(applicable in S only) : Monday to Thursday (4 working days 2 days
        off), 7.30am to 7.30pm. 

        22. H6(applicable in M only) : Monday to Thursday (4 working days 2 days
        off), 7.30am to 7.30pm.
      - >-
        1. 1st Shift: -Dinner (Malam): 8:00PM - 8:40PM, -Supper(Lewat Malam):
        1:00AM - 1:30 AM -Breakfast(Pagi): 8:30AM - 9:00AM -Lunch(Tengah Hari):
        1:40PM - 2:20PM. 

        2. 2nd Shift: -Dinner(Malam): 8:50PM - 9:30PM -Supper(Lewat Malam):
        1:40AM - 2:10AM -Breakfast(Pagi): 9:10AM - 9:40AM -Lunch(Tengah Hari):
        2:30PM - 3:10PM. 

        3. 3rd Shift: -Dinner(Malam): 9:40PM - 10:20PM -Supper(Lewat Malam):
        2:20AM - 2:50AM -Breakfast(Pagi): 9:50AM - 10:20AM -Lunch(Tengah Hari):
        3:20PM - 4:00PM.
      - |-
        1. 1. Yes, acceptable traditional attire includes: 
        2. 1. Malaysian Traditional Attire. 
        3. 2.Malay Baju Kurung. 
        4. 3. Baju Melayu for Muslim men. 
        5. 4.Indian Saree. 
        6. 5. Punjabi Suit. 
        7. Chinese Cheongsam are acceptable.
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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("PeYing/model1_v2")
# Run inference
sentences = [
    'Can employees wear traditional attire?',
    '1. 1. Yes, acceptable traditional attire includes: \n2. 1. Malaysian Traditional Attire. \n3. 2.Malay Baju Kurung. \n4. 3. Baju Melayu for Muslim men. \n5. 4.Indian Saree. \n6. 5. Punjabi Suit. \n7. Chinese Cheongsam are acceptable.',
    '1. N03 : Monday to Friday, 8am to 5:30pm.\n2. N04 : Tuesday to Saturday, 8am to 5:30pm.\n3. N05 : Monday to Friday, 8:30am to 6pm.\n4. N06 : Monday to Friday, 9am to 6:30pm.\n5. N07 : Tuesday to Saturday, 8:30am to 6pm.\n6. N08 : Tuesday to Saturday, 9am to 6.30pm.\n7. N6 : Tuesday to Saturday, 8:30pm to 6:15pm.\n8. N9: 5 working days 2 days off, 7:30am to 5:15pm , 10:30am to 8:15pm.\n9. N10: 5 working days 2 days off, 10:30am to 8:15pm , 7:30am to 5:15pm.\n10. AA/BB/CC/A/B/C : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.\n11. AA1/BB1/CC1/A1/B1/C1 : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.\n12. GG/HH/II/GG1/HH1/II1 : 4 working days 2 days off, 7:30am to 7:30pm , 7:30pm to 7:30am.\n13. P1 : Monday to Thursday (4 working days 2 days off), 6:30am to 6:30pm , 6:30pm to 6:30am.\n14. P2 : Tuesday to Friday (4 working days 2 days off), 6:30am to 6:30pm , 6:30pm to 6:30am. \n15. U1/U2/U3/UU1/UU2/UU3 : 4 working days 2 days off, 7:30am to 7.30pm. \n16. V1/V2/V3/VV1/VV2/VV3 : 4 working days 2 days off, 8.30am to 8.30pm. \n17. W1/W2/W3/WW1/WW2/WW3 : 4 working days 2 days off, 6.30am to 6.30pm. \n18. H1 : Monday to Thursday (4 working days 2 days off), 6.30am to 6.30pm. \n19. H2 : Tuesday to Friday (4 working days 2 days off), 6.30am to 6.30pm. \n20. H3 : Wednesday to Saturday (4 working days 2 days off), 6.30am to 6.30pm. \n21. H6(applicable in S only) : Monday to Thursday (4 working days 2 days off), 7.30am to 7.30pm. \n22. H6(applicable in M only) : Monday to Thursday (4 working days 2 days off), 7.30am to 7.30pm.',
]
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: 182 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 182 samples:
sentence_0 sentence_1 label
type string string int
details
min: 7 tokens
mean: 14.43 tokens
max: 36 tokens

min: 5 tokens
mean: 53.8 tokens
max: 256 tokens

1: 100.00%

	sentence_0	sentence_1	label
type	string	string	int
details	min: 7 tokens mean: 14.43 tokens max: 36 tokens	min: 5 tokens mean: 53.8 tokens max: 256 tokens	1: 100.00%

Samples:

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1
per_device_eval_batch_size: 1
num_train_epochs: 1
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: 1
per_device_eval_batch_size: 1
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: 1
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

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.2
PyTorch: 2.5.1+cu124
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",
}