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--- |
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language: |
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- th |
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license: apache-2.0 |
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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:2452 |
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- loss:MultipleNegativesRankingLoss |
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- thai |
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- semantic-search |
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- food |
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- ingredients |
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- retrieval |
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base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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widget: |
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- source_sentence: น้ำ, ปีกไก่, ไข่เป็ด, รากผักชี, อบเชย, โป๊ยกั๊ก, น้ำตาลมะพร้าว, |
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น้ำปลา, เกลือ, มะนาว, พริกขี้หนู, พริกจินดา, ผักชี |
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sentences: |
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- พะโล้ไข่เป็ดต้มแซ่บ |
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- ต้มยำซี่โครงหมู สูตรใส่คนอร์ |
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- ชุดอาหารเช้า |
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- source_sentence: ข้าวสวย, หมึกกล้วย, หมูสับ, พริกแกง, น้ำปลา, น้ำตาลทราย, น้ำมันพืช, |
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ใบโหระพา, ใบมะกรูด, พริกชี้ฟ้าแดง, ผักชี, พริกแกงเขียวหวาน, กะทิ, น้ำปลา, น้ำตาลทราย |
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sentences: |
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- ไส้กรอกพันเบคอนซอสน้ำตก (เบทาโกร) |
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- ผักกาดขาวผัดไข่ |
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- หมึกยัดไส้ข้าวผัดเขียวหวาน |
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- source_sentence: กะปิ, กุ้งแห้ง, พริกขี้หนูสวน, กระเทียมไทย, น้ำปลา, น้ำตาลปี๊บ, |
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น้ำมะนาว, น้ำเปล่า, ใบตอง, มะเขือพวง |
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sentences: |
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- น้ำพริกกะปิกุ้งแห้ง |
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- หมูสวรรค์ |
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- แกงส้มผักรวมปลากระพง |
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- source_sentence: กล้วยไข่สุก, น้ำตาลทรายขาว, เกลือ, มะพร้าวอ่อน, ใบเตย |
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sentences: |
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- กล้วยไข่บวดชีมะพร้าวอ่อน |
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- เส้นใหญ่ผัดซีอิ๊วไก่ |
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- หุง(นึ่ง)ข้าวเหนียว ด้วยไมโครเวฟ |
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- source_sentence: ปลาทูน่า, พริกขี้หนู, ไข่ไก่, น้ำปลา, เล็กน้อย, น้ำมันพืช |
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sentences: |
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- เจี๋ยนปลาทับทิม สูตร 2 น้ำมะขามเปียก |
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- ข้าวแต๋น |
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- ไข่เจียวทูน่าพริกสับ |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: Thai Food Ingredients → Dish Prediction |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: thai food eval |
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type: thai-food-eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6052631578947368 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8421052631578947 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9605263157894737 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9736842105263158 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6052631578947368 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28070175438596484 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.19210526315789467 |
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name: Cosine Precision@5 |
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- type: cosine_recall@1 |
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value: 0.6052631578947368 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8421052631578947 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9605263157894737 |
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name: Cosine Recall@5 |
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- type: cosine_ndcg@10 |
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value: 0.7948700328021918 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.735964912280702 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7371832358674465 |
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name: Cosine Map@100 |
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--- |
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# Thai Food Ingredients → Dish Prediction |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 84fccfe766bcfd679e39efefe4ebf45af190ad2d --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** th |
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- **License:** apache-2.0 |
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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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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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("thai_food_prediction1") |
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# Run inference |
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sentences = [ |
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'ปลาทูน่า, พริกขี้หนู, ไข่ไก่, น้ำปลา, เล็กน้อย, น้ำมันพืช', |
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'ไข่เจียวทูน่าพริกสับ', |
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'ข้าวแต๋น', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Dataset: `thai-food-eval` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy@1 | 0.6053 | |
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| cosine_accuracy@3 | 0.8421 | |
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| cosine_accuracy@5 | 0.9605 | |
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| cosine_accuracy@10 | 0.9737 | |
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| cosine_precision@1 | 0.6053 | |
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| cosine_precision@3 | 0.2807 | |
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| cosine_precision@5 | 0.1921 | |
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| cosine_recall@1 | 0.6053 | |
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| cosine_recall@3 | 0.8421 | |
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| cosine_recall@5 | 0.9605 | |
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| **cosine_ndcg@10** | **0.7949** | |
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| cosine_mrr@10 | 0.736 | |
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| cosine_map@100 | 0.7372 | |
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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: 2,452 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 29.15 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.91 tokens</li><li>max: 22 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------------------------------------------------------------------------------------|:-------------------------| |
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| <code>ปลาหมึก, ซีอิ๊วดำ, ผงขมิ้น, น้ำปูนใส, กระเทียมสับ, รากผักชี, พริกแดง, น้ำตาลปี๊บ, เกลือ, น้ำปลา, น้ำมะนาว</code> | <code>ปลาหมึกย่าง</code> | |
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| <code>ไปตกหมึกมา อยากทำอะไรกินง่ายๆ ได้รสชาติของปลาหมึกแท้ๆ </code> | <code>ปลาหมึกย่าง</code> | |
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| <code>อยากกินปลาหมึกๆ ซีฟุ้ด อร่อยๆ</code> | <code>ปลาหมึกย่าง</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Evaluation Dataset |
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#### Unnamed Dataset |
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* Size: 76 evaluation samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 76 samples: |
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| | anchor | positive | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 47.42 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.24 tokens</li><li>max: 20 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------| |
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| <code>น้ำมันพืช, กระเทียม, น้ำตาลทราย, น้ำปลา, ซีอิ๊วขาว, ซอสปรุงรส, ซีอิ๊วดำเค็ม, น้ำส้มสายชู, พริกไทย, เส้นหมี่แห้ง, ลูกชิ้น, ถั่วงอก</code> | <code>หมี่คลุก</code> | |
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| <code>น้ำมัน, กระเทียม, หมูหมัก, เส้นใหญ่, ซีอิ้วดำ, คะน้า, กระหล่ำปลี, แครอท, ไข่เป็ด, ไข่ไก่, ผงปรุงรส, น้ำตาลทราย, ซอสหอยนางรม, ซอสปรุงรส, พริกไทย</code> | <code>ผัดซีอิ้วเส้นใหญ่</code> | |
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| <code>สะโพกหมู, น้ำตาลทราย, น้ำตาลปี๊บ, ซีอิ๊วขาว, เกลือ, น้ำเปล่า, ลูกผักชี, ยี่หร่า, กระเทียมไทย, สับละเอียด, น้ำมันพืช</code> | <code>หมูสวรรค์</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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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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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 24 |
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- `per_device_eval_batch_size`: 24 |
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- `learning_rate`: 5e-06 |
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- `num_train_epochs`: 8 |
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- `warmup_ratio`: 0.1 |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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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`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 24 |
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- `per_device_eval_batch_size`: 24 |
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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-06 |
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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`: 1.0 |
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- `num_train_epochs`: 8 |
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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`: 0.1 |
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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 |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `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 |
|
|
- `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`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | thai-food-eval_cosine_ndcg@10 | |
|
|
|:-------:|:-------:|:-------------:|:---------------:|:-----------------------------:| |
|
|
| 0.0971 | 10 | 3.2623 | - | - | |
|
|
| 0.1942 | 20 | 2.7506 | - | - | |
|
|
| 0.2913 | 30 | 2.45 | - | - | |
|
|
| 0.3883 | 40 | 2.1205 | - | - | |
|
|
| 0.4854 | 50 | 2.0216 | - | - | |
|
|
| 0.5825 | 60 | 1.7865 | - | - | |
|
|
| 0.6796 | 70 | 1.7075 | - | - | |
|
|
| 0.7767 | 80 | 1.4338 | - | - | |
|
|
| 0.8738 | 90 | 1.5122 | - | - | |
|
|
| 0.9709 | 100 | 1.4975 | - | - | |
|
|
| 1.0 | 103 | - | 1.0337 | 0.6411 | |
|
|
| 1.0680 | 110 | 1.2398 | - | - | |
|
|
| 1.1650 | 120 | 1.1619 | - | - | |
|
|
| 1.2621 | 130 | 1.1641 | - | - | |
|
|
| 1.3592 | 140 | 1.084 | - | - | |
|
|
| 1.4563 | 150 | 0.992 | - | - | |
|
|
| 1.5534 | 160 | 0.9877 | - | - | |
|
|
| 1.6505 | 170 | 1.0527 | - | - | |
|
|
| 1.7476 | 180 | 1.0431 | - | - | |
|
|
| 1.8447 | 190 | 1.0235 | - | - | |
|
|
| 1.9417 | 200 | 1.0231 | - | - | |
|
|
| 2.0 | 206 | - | 0.6985 | 0.7429 | |
|
|
| 2.0388 | 210 | 0.8387 | - | - | |
|
|
| 2.1359 | 220 | 0.6738 | - | - | |
|
|
| 2.2330 | 230 | 0.7837 | - | - | |
|
|
| 2.3301 | 240 | 0.8629 | - | - | |
|
|
| 2.4272 | 250 | 0.6708 | - | - | |
|
|
| 2.5243 | 260 | 0.8917 | - | - | |
|
|
| 2.6214 | 270 | 0.7686 | - | - | |
|
|
| 2.7184 | 280 | 0.7352 | - | - | |
|
|
| 2.8155 | 290 | 0.6844 | - | - | |
|
|
| 2.9126 | 300 | 0.7821 | - | - | |
|
|
| 3.0 | 309 | - | 0.6023 | 0.7508 | |
|
|
| 3.0097 | 310 | 0.6968 | - | - | |
|
|
| 3.1068 | 320 | 0.6536 | - | - | |
|
|
| 3.2039 | 330 | 0.6157 | - | - | |
|
|
| 3.3010 | 340 | 0.6562 | - | - | |
|
|
| 3.3981 | 350 | 0.563 | - | - | |
|
|
| 3.4951 | 360 | 0.6401 | - | - | |
|
|
| 3.5922 | 370 | 0.6167 | - | - | |
|
|
| 3.6893 | 380 | 0.5221 | - | - | |
|
|
| 3.7864 | 390 | 0.5609 | - | - | |
|
|
| 3.8835 | 400 | 0.6595 | - | - | |
|
|
| 3.9806 | 410 | 0.5761 | - | - | |
|
|
| 4.0 | 412 | - | 0.5541 | 0.7728 | |
|
|
| 4.0777 | 420 | 0.4465 | - | - | |
|
|
| 4.1748 | 430 | 0.4011 | - | - | |
|
|
| 4.2718 | 440 | 0.4988 | - | - | |
|
|
| 4.3689 | 450 | 0.5891 | - | - | |
|
|
| 4.4660 | 460 | 0.6107 | - | - | |
|
|
| 4.5631 | 470 | 0.5573 | - | - | |
|
|
| 4.6602 | 480 | 0.5007 | - | - | |
|
|
| 4.7573 | 490 | 0.4907 | - | - | |
|
|
| 4.8544 | 500 | 0.4756 | - | - | |
|
|
| 4.9515 | 510 | 0.5233 | - | - | |
|
|
| 5.0 | 515 | - | 0.4831 | 0.7920 | |
|
|
| 5.0485 | 520 | 0.4877 | - | - | |
|
|
| 5.1456 | 530 | 0.5158 | - | - | |
|
|
| 5.2427 | 540 | 0.4769 | - | - | |
|
|
| 5.3398 | 550 | 0.4461 | - | - | |
|
|
| 5.4369 | 560 | 0.4684 | - | - | |
|
|
| 5.5340 | 570 | 0.347 | - | - | |
|
|
| 5.6311 | 580 | 0.4203 | - | - | |
|
|
| 5.7282 | 590 | 0.4448 | - | - | |
|
|
| 5.8252 | 600 | 0.3725 | - | - | |
|
|
| 5.9223 | 610 | 0.4598 | - | - | |
|
|
| **6.0** | **618** | **-** | **0.4754** | **0.7938** | |
|
|
| 6.0194 | 620 | 0.5246 | - | - | |
|
|
| 6.1165 | 630 | 0.3768 | - | - | |
|
|
| 6.2136 | 640 | 0.3567 | - | - | |
|
|
| 6.3107 | 650 | 0.4294 | - | - | |
|
|
| 6.4078 | 660 | 0.356 | - | - | |
|
|
| 6.5049 | 670 | 0.4915 | - | - | |
|
|
| 6.6019 | 680 | 0.3908 | - | - | |
|
|
| 6.6990 | 690 | 0.3245 | - | - | |
|
|
| 6.7961 | 700 | 0.4382 | - | - | |
|
|
| 6.8932 | 710 | 0.4935 | - | - | |
|
|
| 6.9903 | 720 | 0.4248 | - | - | |
|
|
| 7.0 | 721 | - | 0.4928 | 0.7886 | |
|
|
| 7.0874 | 730 | 0.2804 | - | - | |
|
|
| 7.1845 | 740 | 0.3395 | - | - | |
|
|
| 7.2816 | 750 | 0.3559 | - | - | |
|
|
| 7.3786 | 760 | 0.4312 | - | - | |
|
|
| 7.4757 | 770 | 0.3929 | - | - | |
|
|
| 7.5728 | 780 | 0.3772 | - | - | |
|
|
| 7.6699 | 790 | 0.3205 | - | - | |
|
|
| 7.7670 | 800 | 0.3914 | - | - | |
|
|
| 7.8641 | 810 | 0.4501 | - | - | |
|
|
| 7.9612 | 820 | 0.4708 | - | - | |
|
|
| 8.0 | 824 | - | 0.4766 | 0.7949 | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.13 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.52.4 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.8.1 |
|
|
- Datasets: 2.14.4 |
|
|
- Tokenizers: 0.21.2 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
|
|
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