---
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). \n2. Risk Assessment ( Hazard Identification,\
\ Risk Assessment, & Risk control / HIRARC) / JSA / Job Safety Analysis. \n3.\
\ Valid licenses (If applicable). \n4. Certification of Fitness-CF (For all types\
\ of cranes). \n5. Crane Operator Competency License. (If applicable). \n6. All\
\ scaffolding must be erected as per the statutory regulations. \n7. Lifting Supervisor\
\ Competency Certificate. (If applicable). \n8. Signal Man Competency Certificate.\
\ (If applicable. \n9. Rigger Competency Certificate. (If applicable). \n10. Lifting\
\ plan (If applicable). \n11. 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. \n2. 2. Collared T-shirts and blouses/sleeveless\
\ tops (for ladies). \n3. 3. Round-neck T-shirts are allowed for non-office personnel.\
\ \n4. 4. Clothing with the company logo is encouraged. \n5. 5. Sport Team. \n\
6. 6. University. \n7. 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.\
\ \n2. 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.\
\ \n3. 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.\
\ \n4. 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: \n2. 1. Lock-Out Tag-Out (LOTO):\
\ Always practice LOTO procedures when performing maintenance or repairs on machines.\
\ \n3. 2. Preventive Maintenance: Conduct regular preventive maintenance on all\
\ machinery to ensure proper functioning. \n4. 3. Pinch Points Awareness: Identify\
\ all possible pinch points on machinery, and ensure they are properly labeled.\
\ \n5. 4. Production Area Organization: Keep the production area neat and organized\
\ at all times. \n6. 5. Operator Training: Provide adequate training to operators\
\ before allowing them to handle machines. \n7. 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.\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.\n\
8. 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."
- "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.\
\ \n2. 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.\
\ \n3. 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: \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."
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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("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
| |
* Samples:
| sentence_0 | sentence_1 | label |
|:----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| List out all the work schedule for Carsem. | 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/VV... | 1 |
| What is the maximum allowed working hours in a week? | 1. Employees are not allowed to work more than 60 hours in a week inclusive of overtime and 1 rest day per week. Company will monitor overtime and rest day utilization and take appropriate action to address instances deemed excessive. | 1 |
| Why the company is not allowed working hours in a week more than 60 hours? | 1. Continuous overtime causes worker strain that may lead to reduced productivity, increased turnover and increased injury and illnesses. | 1 |
* Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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