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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:182 |
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- loss:CosineSimilarityLoss |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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widget: |
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- source_sentence: What documents must contractors/vendors provide? |
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sentences: |
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- 1. ESH representatives will carry out the training when new employees need to |
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be trained, or on an annual basis. |
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- "1. Safe Operating Procedure (SOP). \n2. Risk Assessment ( Hazard Identification,\ |
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\ Risk Assessment, & Risk control / HIRARC) / JSA / Job Safety Analysis. \n3.\ |
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\ Valid licenses (If applicable). \n4. Certification of Fitness-CF (For all types\ |
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\ of cranes). \n5. Crane Operator Competency License. (If applicable). \n6. All\ |
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\ scaffolding must be erected as per the statutory regulations. \n7. Lifting Supervisor\ |
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\ Competency Certificate. (If applicable). \n8. Signal Man Competency Certificate.\ |
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\ (If applicable. \n9. Rigger Competency Certificate. (If applicable). \n10. Lifting\ |
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\ plan (If applicable). \n11. Scaffolder Level 1/2/3 Certificate. (If applicable)." |
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- 1. To ensure the specific employees are aware of the correct procedures associated |
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with chemical handling and waste management. |
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- source_sentence: What is the guideline for shirts and blouses? |
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sentences: |
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- 1. ESH representatives will carry out the training when new employees need to |
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be trained, or on an annual basis. |
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- 1. Employees in CLEAN ROOM are NOT ALLOWED to use/wear makeup/bangles. |
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- "1. 1. Formal or casual shirts with sleeves. \n2. 2. Collared T-shirts and blouses/sleeveless\ |
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\ tops (for ladies). \n3. 3. Round-neck T-shirts are allowed for non-office personnel.\ |
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\ \n4. 4. Clothing with the company logo is encouraged. \n5. 5. Sport Team. \n\ |
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6. 6. University. \n7. 7. Fashion brands on clothing are generally acceptable." |
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- source_sentence: What is the lunch schedule for the 1st shift in the normal schedule |
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in M-site? |
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sentences: |
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- 12 days. |
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- '1. Categorization of Machine: Identify the location of the machine, its function, |
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and all necessary items needed for it to run (e.g., lubricants, saw blades, etc). |
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2. Authorization: Ensure that all personnel operating the machine have received |
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the appropriate training. |
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3. Hazard & Risks associated with equipment/machinery/techniques/process: Identify |
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all hazards and risks associated, and implement sufficient controls according |
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to the hierarchy of controls (e.g., warning labels and symbols). |
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4. Pre-work procedure: Ensure that the machine is in proper, running condition |
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before starting work. |
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5. During work procedure: Follow the correct standard operating procedure for |
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carrying out that work activity. |
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6. After work procedure: Ensure that the machine remains in a neat and tidy condition |
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at all times. |
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7. Work Area: Identify the area where the work is being done. |
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8. PPE: Ensure that appropriate PPE is available for all personnel handling the |
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machine. |
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9. Emergency Procedure: Ensure sufficient emergency features are available on |
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the machine (e.g., emergency stop button). |
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10. After work hour: Ensure the machine system is in shutdown/standby mode when |
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the machine is not running. |
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11. Housekeeping: Ensure basic housekeeping is done at the work area. |
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12. Scheduled waste: Any scheduled waste generated by the process should be disposed |
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of according to Carsem waste management procedure.' |
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- 1. Lunch (Tengah Hari) for the 1st shift is from 12:00 PM to 1:00 PM, lasting |
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60 minutes. |
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- source_sentence: What is the meal schedule for M-site? |
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sentences: |
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- 2 days. |
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- "1. 1st Shift: -Dinner (Malam): 8:00PM - 8:40PM, -Supper(Lewat Malam): 1:00AM\ |
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\ - 1:30 AM -Breakfast(Pagi): 8:00AM - 8:30AM -Lunch(Tengah Hari): 12:50PM - 1:30PM.\ |
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\ \n2. 2nd Shift: -Dinner(Malam): 8:50PM - 9:30PM -Supper(Lewat Malam): 1:40AM\ |
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\ - 2:10AM -Breakfast(Pagi): 8:40AM - 9:10AM -Lunch(Tengah Hari): 1:40PM - 2:20PM.\ |
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\ \n3. 3rd Shift: -Dinner(Malam): 9:40PM - 10:20PM -Supper(Lewat Malam): 2:20AM\ |
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\ - 2:50AM -Breakfast(Pagi): 9:20AM - 9:50AM -Lunch(Tengah Hari): 2:30PM - 3:10PM.\ |
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\ \n4. 4th Shift: -Dinner(Malam): 10:30PM - 11:10PM -Supper(Lewat Malam): 3:00AM\ |
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\ - 3:30AM -Breakfast(Pagi): 10:00AM - 10:30AM -Lunch(Tengah Hari): 3:20PM - 4:00PM." |
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- "1. The mechanical safety guidelines include: \n2. 1. Lock-Out Tag-Out (LOTO):\ |
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\ Always practice LOTO procedures when performing maintenance or repairs on machines.\ |
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\ \n3. 2. Preventive Maintenance: Conduct regular preventive maintenance on all\ |
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\ machinery to ensure proper functioning. \n4. 3. Pinch Points Awareness: Identify\ |
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\ all possible pinch points on machinery, and ensure they are properly labeled.\ |
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\ \n5. 4. Production Area Organization: Keep the production area neat and organized\ |
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\ at all times. \n6. 5. Operator Training: Provide adequate training to operators\ |
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\ before allowing them to handle machines. \n7. 6. Machine Guarding: Ensure all\ |
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\ safety guards are in place before starting machine operations." |
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- source_sentence: Can employees wear traditional attire? |
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sentences: |
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- "1. N03 : Monday to Friday, 8am to 5:30pm.\n2. N04 : Tuesday to Saturday, 8am\ |
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\ to 5:30pm.\n3. N05 : Monday to Friday, 8:30am to 6pm.\n4. N06 : Monday to Friday,\ |
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\ 9am to 6:30pm.\n5. N07 : Tuesday to Saturday, 8:30am to 6pm.\n6. N08 : Tuesday\ |
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\ to Saturday, 9am to 6.30pm.\n7. N6 : Tuesday to Saturday, 8:30pm to 6:15pm.\n\ |
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8. N9: 5 working days 2 days off, 7:30am to 5:15pm , 10:30am to 8:15pm.\n9. N10:\ |
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\ 5 working days 2 days off, 10:30am to 8:15pm , 7:30am to 5:15pm.\n10. AA/BB/CC/A/B/C\ |
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\ : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.\n11. AA1/BB1/CC1/A1/B1/C1\ |
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\ : 4 working days 2 days off, 6:30am to 6:30pm , 6:30pm to 6:30am.\n12. GG/HH/II/GG1/HH1/II1\ |
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\ : 4 working days 2 days off, 7:30am to 7:30pm , 7:30pm to 7:30am.\n13. P1 :\ |
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\ Monday to Thursday (4 working days 2 days off), 6:30am to 6:30pm , 6:30pm to\ |
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\ 6:30am.\n14. P2 : Tuesday to Friday (4 working days 2 days off), 6:30am to 6:30pm\ |
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\ , 6:30pm to 6:30am. \n15. U1/U2/U3/UU1/UU2/UU3 : 4 working days 2 days off,\ |
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\ 7:30am to 7.30pm. \n16. V1/V2/V3/VV1/VV2/VV3 : 4 working days 2 days off, 8.30am\ |
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\ to 8.30pm. \n17. W1/W2/W3/WW1/WW2/WW3 : 4 working days 2 days off, 6.30am to\ |
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\ 6.30pm. \n18. H1 : Monday to Thursday (4 working days 2 days off), 6.30am to\ |
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\ 6.30pm. \n19. H2 : Tuesday to Friday (4 working days 2 days off), 6.30am to\ |
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\ 6.30pm. \n20. H3 : Wednesday to Saturday (4 working days 2 days off), 6.30am\ |
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\ to 6.30pm. \n21. H6(applicable in S only) : Monday to Thursday (4 working days\ |
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\ 2 days off), 7.30am to 7.30pm. \n22. H6(applicable in M only) : Monday to Thursday\ |
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\ (4 working days 2 days off), 7.30am to 7.30pm." |
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- "1. 1st Shift: -Dinner (Malam): 8:00PM - 8:40PM, -Supper(Lewat Malam): 1:00AM\ |
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\ - 1:30 AM -Breakfast(Pagi): 8:30AM - 9:00AM -Lunch(Tengah Hari): 1:40PM - 2:20PM.\ |
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\ \n2. 2nd Shift: -Dinner(Malam): 8:50PM - 9:30PM -Supper(Lewat Malam): 1:40AM\ |
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\ - 2:10AM -Breakfast(Pagi): 9:10AM - 9:40AM -Lunch(Tengah Hari): 2:30PM - 3:10PM.\ |
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\ \n3. 3rd Shift: -Dinner(Malam): 9:40PM - 10:20PM -Supper(Lewat Malam): 2:20AM\ |
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\ - 2:50AM -Breakfast(Pagi): 9:50AM - 10:20AM -Lunch(Tengah Hari): 3:20PM - 4:00PM." |
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- "1. 1. Yes, acceptable traditional attire includes: \n2. 1. Malaysian Traditional\ |
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\ Attire. \n3. 2.Malay Baju Kurung. \n4. 3. Baju Melayu for Muslim men. \n5. 4.Indian\ |
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\ Saree. \n6. 5. Punjabi Suit. \n7. Chinese Cheongsam are acceptable." |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("PeYing/model1_v2") |
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# Run inference |
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sentences = [ |
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'Can employees wear traditional attire?', |
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'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.', |
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'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.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 182 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 182 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------| |
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| type | string | string | int | |
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| 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> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
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```json |
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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 1 |
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- `per_device_eval_batch_size`: 1 |
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- `num_train_epochs`: 1 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 1 |
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- `per_device_eval_batch_size`: 1 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `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", |
|
|
} |
|
|
``` |
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