Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+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) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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]
+```
+
+<!--
+### 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: 182 training samples
+* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
+* Approximate statistics based on the first 182 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                        | label                        |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
+  | type    | string                                                                            | string                                                                            | int                          |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 14.43 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 53.8 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> |
+* Samples:
+  | sentence_0                                                                              | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | label          |
+  |:----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>List out all the work schedule for Carsem.</code>                                 | <code>1. N03 : Monday to Friday, 8am to 5:30pm.<br>2. N04 : Tuesday to Saturday, 8am to 5:30pm.<br>3. N05 : Monday to Friday, 8:30am to 6pm.<br>4. N06 : Monday to Friday, 9am to 6:30pm.<br>5. N07 : Tuesday to Saturday, 8:30am to 6pm.<br>6. N08 : Tuesday to Saturday, 9am to 6.30pm.<br>7. N6 : Tuesday to Saturday, 8:30pm to 6:15pm.<br>8. N9: 5 working days 2 days off, 7:30am to 5:15pm , 10:30am to 8:15pm.<br>9. N10: 5 working days 2 days off, 10:30am to 8:15pm , 7:30am to 5:15pm.<br>10. AA/BB/CC/A/B/C : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.<br>11. AA1/BB1/CC1/A1/B1/C1 : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.<br>12. GG/HH/II/GG1/HH1/II1 : 4 working days 2 days off, 7:30am to 7:30pm , 7:30pm to 7:30am.<br>13. P1 : Monday to Thursday (4 working days 2 days off), 6:30am to 6:30pm , 6:30pm to 6:30am.<br>14. P2 : Tuesday to Friday (4 working days 2 days off), 6:30am to 6:30pm , 6:30pm to 6:30am. <br>15. U1/U2/U3/UU1/UU2/UU3 : 4 working days 2 days off, 7:30am to 7.30pm. <br>16. V1/V2/V3/VV1/VV...</code> | <code>1</code> |
+  | <code>What is the maximum allowed working hours in a week?</code>                       | <code>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.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>1</code> |
+  | <code>Why the company is not allowed working hours in a week more than 60 hours?</code> | <code>1. Continuous overtime causes worker strain that may lead to reduced productivity, increased turnover and increased injury and illnesses.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | <code>1</code> |
+* Loss: [<code>CosineSimilarityLoss</code>](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
+<details><summary>Click to expand</summary>
+
+- `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
+
+</details>
+
+### 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
+```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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
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+  "num_hidden_layers": 6,
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+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.2",
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+  "use_cache": true,
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+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.2",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dc16c2bc95a6b8ac3761bcb6337cc131064093a68a3826c0cd5461a89e3fa0d8
+size 90864192

modules.json

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+[
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+  {
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "do_lower_case": true,
+  "extra_special_tokens": {},
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+  "never_split": null,
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+  "pad_token": "[PAD]",
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+  "padding_side": "right",
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}