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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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- dense |
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- generated_from_trainer |
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- dataset_size:1310129 |
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- loss:MultipleNegativesRankingLoss |
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base_model: unsloth/Qwen3-Embedding-0.6B |
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widget: |
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- source_sentence: 닥터브로너스 [페이셜&바디워시] 닥터브로너스 퓨어 캐스틸 솝 475ml 12종 택1 |
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sentences: |
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- 露得清卸妆油 |
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- Versace Man Eau Fraiche |
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- ピュアキャスティールソープ |
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- source_sentence: 베르사체 베르사체 맨오프레쉬 30ml 단품/기획 택1 |
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sentences: |
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- ピーリングジェル |
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- Versace Bright |
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- Man Eau Fraiche single |
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- source_sentence: 랑방 랑방 루머 2 로즈 50ml |
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sentences: |
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- 캐스틸 솝 475ml |
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- 랑방 루머 |
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- 防晒霜 |
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- source_sentence: 케어존 케어존 데일리&패밀리 선크림 80ml (SPF50+/PA+++) |
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sentences: |
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- 伊丽莎白雅顿 100毫升 |
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- Rumeur Rose perfume |
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- 패밀리 선크림 |
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- source_sentence: 랑방 랑방 메리미 EDP 50ml |
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sentences: |
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- マリーミー EDP |
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- 浪凡 EDP |
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- ケアゾーン デイリー日焼け止め |
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datasets: |
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- dkqjrm/olive-product |
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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 unsloth/Qwen3-Embedding-0.6B |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/Qwen3-Embedding-0.6B](https://huggingface.co/unsloth/Qwen3-Embedding-0.6B) on the [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product) dataset. It maps sentences & paragraphs to a 1024-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:** [unsloth/Qwen3-Embedding-0.6B](https://huggingface.co/unsloth/Qwen3-Embedding-0.6B) <!-- at revision f2fddb42505bde9feaf19f0967b01dce52e764c6 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product) |
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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/huggingface/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': 512, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'}) |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, '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("dkqjrm/lora_model") |
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# Run inference |
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sentences = [ |
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'랑방 랑방 메리미 EDP 50ml', |
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'浪凡 EDP', |
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'マリーミー EDP', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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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) |
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# tensor([[1.0000, 0.6852, 0.6339], |
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# [0.6852, 1.0000, 0.3744], |
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# [0.6339, 0.3744, 1.0000]]) |
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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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#### olive-product |
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* Dataset: [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product) at [8d1f081](https://huggingface.co/datasets/dkqjrm/olive-product/tree/8d1f0813721299cb95f6d5cc09b2ef9317e8d06c) |
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* Size: 1,310,129 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: 16 tokens</li><li>mean: 25.67 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.19 tokens</li><li>max: 38 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------|:-----------------------------------| |
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| <code>베르사체 베르사체 브라이트 크리스탈 50ml 택1</code> | <code>베르사체 브라이트 크리스탈 50ml 1</code> | |
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| <code>베르사체 베르사체 브라이트 크리스탈 50ml 택1</code> | <code>베르사체 브라이트</code> | |
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| <code>베르사체 베르사체 브라이트 크리스탈 50ml 택1</code> | <code>베르사체 크리스탈</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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"gather_across_devices": false |
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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`: 32 |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 3e-05 |
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- `num_train_epochs`: 2 |
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- `lr_scheduler_type`: constant_with_warmup |
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- `warmup_ratio`: 0.03 |
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- `fp16`: True |
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- `push_to_hub`: True |
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- `hub_model_id`: dkqjrm/qwen3-embedding-olive-lora |
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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`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 8 |
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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`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 3e-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`: 1.0 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: constant_with_warmup |
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- `lr_scheduler_kwargs`: None |
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- `warmup_ratio`: 0.03 |
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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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- `bf16`: False |
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- `fp16`: True |
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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`: False |
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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 |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `project`: huggingface |
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- `trackio_space_id`: trackio |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: True |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: dkqjrm/qwen3-embedding-olive-lora |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: no |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: True |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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<details><summary>Click to expand</summary> |
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| Epoch | Step | Training Loss | |
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|:------:|:-----:|:-------------:| |
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| 0.0049 | 50 | 1.5027 | |
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| 0.0098 | 100 | 0.8366 | |
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| 0.0147 | 150 | 0.6713 | |
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| 0.0195 | 200 | 0.5863 | |
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| 0.0244 | 250 | 0.53 | |
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| 0.0293 | 300 | 0.4562 | |
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| 0.0342 | 350 | 0.4061 | |
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| 0.0391 | 400 | 0.3899 | |
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| 0.0440 | 450 | 0.3417 | |
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| 0.0488 | 500 | 0.3367 | |
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| 0.0537 | 550 | 0.2948 | |
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| 0.0586 | 600 | 0.281 | |
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| 0.0635 | 650 | 0.2808 | |
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| 0.0684 | 700 | 0.2414 | |
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| 0.0733 | 750 | 0.2448 | |
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| 0.0782 | 800 | 0.2307 | |
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| 0.0830 | 850 | 0.2174 | |
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| 0.0879 | 900 | 0.2129 | |
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| 0.0928 | 950 | 0.2139 | |
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| 0.0977 | 1000 | 0.198 | |
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| 0.1026 | 1050 | 0.1797 | |
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| 0.1075 | 1100 | 0.1923 | |
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| 0.1124 | 1150 | 0.1887 | |
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| 0.1172 | 1200 | 0.1789 | |
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| 0.1221 | 1250 | 0.1833 | |
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| 0.1270 | 1300 | 0.168 | |
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| 0.1319 | 1350 | 0.1683 | |
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| 0.1368 | 1400 | 0.1536 | |
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| 0.1417 | 1450 | 0.1632 | |
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| 0.1465 | 1500 | 0.155 | |
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| 0.1514 | 1550 | 0.1533 | |
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| 0.1563 | 1600 | 0.1442 | |
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| 0.1612 | 1650 | 0.1407 | |
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| 0.1661 | 1700 | 0.1396 | |
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| 0.1710 | 1750 | 0.1388 | |
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| 0.1759 | 1800 | 0.1375 | |
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| 0.1807 | 1850 | 0.1356 | |
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| 0.1856 | 1900 | 0.1335 | |
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| 0.1905 | 1950 | 0.1296 | |
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| 0.1954 | 2000 | 0.1281 | |
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| 0.2003 | 2050 | 0.1379 | |
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| 0.2052 | 2100 | 0.1213 | |
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| 0.2101 | 2150 | 0.1209 | |
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| 0.2149 | 2200 | 0.1142 | |
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| 0.2198 | 2250 | 0.1305 | |
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| 0.2247 | 2300 | 0.115 | |
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| 0.2296 | 2350 | 0.1125 | |
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| 0.2345 | 2400 | 0.1159 | |
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| 0.2394 | 2450 | 0.1131 | |
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| 0.2442 | 2500 | 0.1133 | |
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| 0.2491 | 2550 | 0.1126 | |
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| 0.2540 | 2600 | 0.109 | |
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| 0.2589 | 2650 | 0.1135 | |
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| 0.2638 | 2700 | 0.0986 | |
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| 0.2687 | 2750 | 0.1127 | |
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| 0.2736 | 2800 | 0.114 | |
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| 0.2784 | 2850 | 0.1079 | |
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| 0.2833 | 2900 | 0.1106 | |
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| 0.2882 | 2950 | 0.1112 | |
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| 0.2931 | 3000 | 0.1006 | |
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| 0.2980 | 3050 | 0.1051 | |
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| 0.3029 | 3100 | 0.1105 | |
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| 0.3078 | 3150 | 0.1046 | |
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| 0.3126 | 3200 | 0.1011 | |
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| 0.3175 | 3250 | 0.0962 | |
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| 0.3224 | 3300 | 0.1002 | |
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| 0.3273 | 3350 | 0.1066 | |
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| 0.3322 | 3400 | 0.0907 | |
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| 0.3371 | 3450 | 0.0894 | |
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| 0.3419 | 3500 | 0.1002 | |
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| 0.3468 | 3550 | 0.0894 | |
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| 0.3517 | 3600 | 0.0897 | |
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| 0.3566 | 3650 | 0.0995 | |
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| 0.3615 | 3700 | 0.0949 | |
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| 0.3664 | 3750 | 0.0914 | |
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| 0.3713 | 3800 | 0.0929 | |
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| 0.3761 | 3850 | 0.0841 | |
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| 0.3810 | 3900 | 0.0847 | |
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| 0.3859 | 3950 | 0.0964 | |
|
|
| 0.3908 | 4000 | 0.0937 | |
|
|
| 0.3957 | 4050 | 0.0874 | |
|
|
| 0.4006 | 4100 | 0.0911 | |
|
|
| 0.4055 | 4150 | 0.093 | |
|
|
| 0.4103 | 4200 | 0.0867 | |
|
|
| 0.4152 | 4250 | 0.0841 | |
|
|
| 0.4201 | 4300 | 0.083 | |
|
|
| 0.4250 | 4350 | 0.0908 | |
|
|
| 0.4299 | 4400 | 0.0829 | |
|
|
| 0.4348 | 4450 | 0.0871 | |
|
|
| 0.4396 | 4500 | 0.0799 | |
|
|
| 0.4445 | 4550 | 0.0777 | |
|
|
| 0.4494 | 4600 | 0.0873 | |
|
|
| 0.4543 | 4650 | 0.0805 | |
|
|
| 0.4592 | 4700 | 0.0851 | |
|
|
| 0.4641 | 4750 | 0.0855 | |
|
|
| 0.4690 | 4800 | 0.0763 | |
|
|
| 0.4738 | 4850 | 0.082 | |
|
|
| 0.4787 | 4900 | 0.0699 | |
|
|
| 0.4836 | 4950 | 0.0802 | |
|
|
| 0.4885 | 5000 | 0.0807 | |
|
|
| 0.4934 | 5050 | 0.0746 | |
|
|
| 0.4983 | 5100 | 0.0705 | |
|
|
| 0.5032 | 5150 | 0.0707 | |
|
|
| 0.5080 | 5200 | 0.0827 | |
|
|
| 0.5129 | 5250 | 0.0808 | |
|
|
| 0.5178 | 5300 | 0.0835 | |
|
|
| 0.5227 | 5350 | 0.0782 | |
|
|
| 0.5276 | 5400 | 0.0698 | |
|
|
| 0.5325 | 5450 | 0.0755 | |
|
|
| 0.5373 | 5500 | 0.0743 | |
|
|
| 0.5422 | 5550 | 0.0744 | |
|
|
| 0.5471 | 5600 | 0.0724 | |
|
|
| 0.5520 | 5650 | 0.0781 | |
|
|
| 0.5569 | 5700 | 0.0712 | |
|
|
| 0.5618 | 5750 | 0.0738 | |
|
|
| 0.5667 | 5800 | 0.0692 | |
|
|
| 0.5715 | 5850 | 0.0747 | |
|
|
| 0.5764 | 5900 | 0.0686 | |
|
|
| 0.5813 | 5950 | 0.0761 | |
|
|
| 0.5862 | 6000 | 0.0696 | |
|
|
| 0.5911 | 6050 | 0.0681 | |
|
|
| 0.5960 | 6100 | 0.0714 | |
|
|
| 0.6008 | 6150 | 0.0682 | |
|
|
| 0.6057 | 6200 | 0.0746 | |
|
|
| 0.6106 | 6250 | 0.0638 | |
|
|
| 0.6155 | 6300 | 0.0672 | |
|
|
| 0.6204 | 6350 | 0.0727 | |
|
|
| 0.6253 | 6400 | 0.0711 | |
|
|
| 0.6302 | 6450 | 0.0716 | |
|
|
| 0.6350 | 6500 | 0.0609 | |
|
|
| 0.6399 | 6550 | 0.066 | |
|
|
| 0.6448 | 6600 | 0.0709 | |
|
|
| 0.6497 | 6650 | 0.0687 | |
|
|
| 0.6546 | 6700 | 0.0629 | |
|
|
| 0.6595 | 6750 | 0.0693 | |
|
|
| 0.6644 | 6800 | 0.0678 | |
|
|
| 0.6692 | 6850 | 0.0612 | |
|
|
| 0.6741 | 6900 | 0.0653 | |
|
|
| 0.6790 | 6950 | 0.0642 | |
|
|
| 0.6839 | 7000 | 0.068 | |
|
|
| 0.6888 | 7050 | 0.0626 | |
|
|
| 0.6937 | 7100 | 0.0623 | |
|
|
| 0.6985 | 7150 | 0.0622 | |
|
|
| 0.7034 | 7200 | 0.0661 | |
|
|
| 0.7083 | 7250 | 0.0597 | |
|
|
| 0.7132 | 7300 | 0.0584 | |
|
|
| 0.7181 | 7350 | 0.0595 | |
|
|
| 0.7230 | 7400 | 0.0647 | |
|
|
| 0.7279 | 7450 | 0.0664 | |
|
|
| 0.7327 | 7500 | 0.0682 | |
|
|
| 0.7376 | 7550 | 0.0621 | |
|
|
| 0.7425 | 7600 | 0.0603 | |
|
|
| 0.7474 | 7650 | 0.0617 | |
|
|
| 0.7523 | 7700 | 0.0554 | |
|
|
| 0.7572 | 7750 | 0.056 | |
|
|
| 0.7621 | 7800 | 0.0594 | |
|
|
| 0.7669 | 7850 | 0.0594 | |
|
|
| 0.7718 | 7900 | 0.0618 | |
|
|
| 0.7767 | 7950 | 0.0638 | |
|
|
| 0.7816 | 8000 | 0.0556 | |
|
|
| 0.7865 | 8050 | 0.0608 | |
|
|
| 0.7914 | 8100 | 0.0624 | |
|
|
| 0.7962 | 8150 | 0.0621 | |
|
|
| 0.8011 | 8200 | 0.0653 | |
|
|
| 0.8060 | 8250 | 0.0648 | |
|
|
| 0.8109 | 8300 | 0.0533 | |
|
|
| 0.8158 | 8350 | 0.0584 | |
|
|
| 0.8207 | 8400 | 0.0552 | |
|
|
| 0.8256 | 8450 | 0.066 | |
|
|
| 0.8304 | 8500 | 0.0616 | |
|
|
| 0.8353 | 8550 | 0.0648 | |
|
|
| 0.8402 | 8600 | 0.0618 | |
|
|
| 0.8451 | 8650 | 0.0587 | |
|
|
| 0.8500 | 8700 | 0.0616 | |
|
|
| 0.8549 | 8750 | 0.0544 | |
|
|
| 0.8598 | 8800 | 0.0637 | |
|
|
| 0.8646 | 8850 | 0.0621 | |
|
|
| 0.8695 | 8900 | 0.0574 | |
|
|
| 0.8744 | 8950 | 0.0587 | |
|
|
| 0.8793 | 9000 | 0.0606 | |
|
|
| 0.8842 | 9050 | 0.0595 | |
|
|
| 0.8891 | 9100 | 0.0627 | |
|
|
| 0.8939 | 9150 | 0.0564 | |
|
|
| 0.8988 | 9200 | 0.0542 | |
|
|
| 0.9037 | 9250 | 0.0538 | |
|
|
| 0.9086 | 9300 | 0.055 | |
|
|
| 0.9135 | 9350 | 0.0562 | |
|
|
| 0.9184 | 9400 | 0.0547 | |
|
|
| 0.9233 | 9450 | 0.0514 | |
|
|
| 0.9281 | 9500 | 0.0574 | |
|
|
| 0.9330 | 9550 | 0.0503 | |
|
|
| 0.9379 | 9600 | 0.0647 | |
|
|
| 0.9428 | 9650 | 0.0554 | |
|
|
| 0.9477 | 9700 | 0.0532 | |
|
|
| 0.9526 | 9750 | 0.056 | |
|
|
| 0.9575 | 9800 | 0.0554 | |
|
|
| 0.9623 | 9850 | 0.0535 | |
|
|
| 0.9672 | 9900 | 0.0553 | |
|
|
| 0.9721 | 9950 | 0.0581 | |
|
|
| 0.9770 | 10000 | 0.05 | |
|
|
| 0.9819 | 10050 | 0.0571 | |
|
|
| 0.9868 | 10100 | 0.0534 | |
|
|
| 0.9916 | 10150 | 0.0462 | |
|
|
| 0.9965 | 10200 | 0.0508 | |
|
|
| 1.0014 | 10250 | 0.0506 | |
|
|
| 1.0063 | 10300 | 0.0548 | |
|
|
| 1.0111 | 10350 | 0.0476 | |
|
|
| 1.0160 | 10400 | 0.0504 | |
|
|
| 1.0209 | 10450 | 0.0433 | |
|
|
| 1.0258 | 10500 | 0.0499 | |
|
|
| 1.0307 | 10550 | 0.0453 | |
|
|
| 1.0356 | 10600 | 0.0494 | |
|
|
| 1.0404 | 10650 | 0.0456 | |
|
|
| 1.0453 | 10700 | 0.0499 | |
|
|
| 1.0502 | 10750 | 0.049 | |
|
|
| 1.0551 | 10800 | 0.0464 | |
|
|
| 1.0600 | 10850 | 0.0483 | |
|
|
| 1.0649 | 10900 | 0.0487 | |
|
|
| 1.0698 | 10950 | 0.0461 | |
|
|
| 1.0746 | 11000 | 0.0433 | |
|
|
| 1.0795 | 11050 | 0.0474 | |
|
|
| 1.0844 | 11100 | 0.0485 | |
|
|
| 1.0893 | 11150 | 0.0462 | |
|
|
| 1.0942 | 11200 | 0.0396 | |
|
|
| 1.0991 | 11250 | 0.0479 | |
|
|
| 1.1040 | 11300 | 0.0471 | |
|
|
| 1.1088 | 11350 | 0.0473 | |
|
|
| 1.1137 | 11400 | 0.0482 | |
|
|
| 1.1186 | 11450 | 0.0412 | |
|
|
| 1.1235 | 11500 | 0.0455 | |
|
|
| 1.1284 | 11550 | 0.0448 | |
|
|
| 1.1333 | 11600 | 0.0531 | |
|
|
| 1.1381 | 11650 | 0.0466 | |
|
|
| 1.1430 | 11700 | 0.0527 | |
|
|
| 1.1479 | 11750 | 0.0465 | |
|
|
| 1.1528 | 11800 | 0.0536 | |
|
|
| 1.1577 | 11850 | 0.0474 | |
|
|
| 1.1626 | 11900 | 0.0515 | |
|
|
| 1.1675 | 11950 | 0.0429 | |
|
|
| 1.1723 | 12000 | 0.0464 | |
|
|
| 1.1772 | 12050 | 0.0463 | |
|
|
| 1.1821 | 12100 | 0.0491 | |
|
|
| 1.1870 | 12150 | 0.0433 | |
|
|
| 1.1919 | 12200 | 0.0466 | |
|
|
| 1.1968 | 12250 | 0.0522 | |
|
|
| 1.2017 | 12300 | 0.0463 | |
|
|
| 1.2065 | 12350 | 0.0528 | |
|
|
| 1.2114 | 12400 | 0.0451 | |
|
|
| 1.2163 | 12450 | 0.0449 | |
|
|
| 1.2212 | 12500 | 0.0475 | |
|
|
| 1.2261 | 12550 | 0.0468 | |
|
|
| 1.2310 | 12600 | 0.0456 | |
|
|
| 1.2358 | 12650 | 0.0411 | |
|
|
| 1.2407 | 12700 | 0.0439 | |
|
|
| 1.2456 | 12750 | 0.0434 | |
|
|
| 1.2505 | 12800 | 0.0475 | |
|
|
| 1.2554 | 12850 | 0.0468 | |
|
|
| 1.2603 | 12900 | 0.046 | |
|
|
| 1.2652 | 12950 | 0.0467 | |
|
|
| 1.2700 | 13000 | 0.0429 | |
|
|
| 1.2749 | 13050 | 0.0437 | |
|
|
| 1.2798 | 13100 | 0.048 | |
|
|
| 1.2847 | 13150 | 0.0429 | |
|
|
| 1.2896 | 13200 | 0.0507 | |
|
|
| 1.2945 | 13250 | 0.0426 | |
|
|
| 1.2994 | 13300 | 0.0408 | |
|
|
| 1.3042 | 13350 | 0.0468 | |
|
|
| 1.3091 | 13400 | 0.0389 | |
|
|
| 1.3140 | 13450 | 0.0458 | |
|
|
| 1.3189 | 13500 | 0.044 | |
|
|
| 1.3238 | 13550 | 0.0417 | |
|
|
| 1.3287 | 13600 | 0.0437 | |
|
|
| 1.3335 | 13650 | 0.0427 | |
|
|
| 1.3384 | 13700 | 0.0444 | |
|
|
| 1.3433 | 13750 | 0.0496 | |
|
|
| 1.3482 | 13800 | 0.0443 | |
|
|
| 1.3531 | 13850 | 0.0421 | |
|
|
| 1.3580 | 13900 | 0.0431 | |
|
|
| 1.3629 | 13950 | 0.0474 | |
|
|
| 1.3677 | 14000 | 0.0423 | |
|
|
| 1.3726 | 14050 | 0.0437 | |
|
|
| 1.3775 | 14100 | 0.038 | |
|
|
| 1.3824 | 14150 | 0.0457 | |
|
|
| 1.3873 | 14200 | 0.0459 | |
|
|
| 1.3922 | 14250 | 0.0421 | |
|
|
| 1.3970 | 14300 | 0.0482 | |
|
|
| 1.4019 | 14350 | 0.0496 | |
|
|
| 1.4068 | 14400 | 0.0436 | |
|
|
| 1.4117 | 14450 | 0.0437 | |
|
|
| 1.4166 | 14500 | 0.0463 | |
|
|
| 1.4215 | 14550 | 0.04 | |
|
|
| 1.4264 | 14600 | 0.046 | |
|
|
| 1.4312 | 14650 | 0.0451 | |
|
|
| 1.4361 | 14700 | 0.044 | |
|
|
| 1.4410 | 14750 | 0.0436 | |
|
|
| 1.4459 | 14800 | 0.0411 | |
|
|
| 1.4508 | 14850 | 0.0453 | |
|
|
| 1.4557 | 14900 | 0.0402 | |
|
|
| 1.4606 | 14950 | 0.0437 | |
|
|
| 1.4654 | 15000 | 0.0451 | |
|
|
| 1.4703 | 15050 | 0.0454 | |
|
|
| 1.4752 | 15100 | 0.0433 | |
|
|
| 1.4801 | 15150 | 0.0399 | |
|
|
| 1.4850 | 15200 | 0.0389 | |
|
|
| 1.4899 | 15250 | 0.0451 | |
|
|
| 1.4947 | 15300 | 0.0417 | |
|
|
| 1.4996 | 15350 | 0.0411 | |
|
|
| 1.5045 | 15400 | 0.0415 | |
|
|
| 1.5094 | 15450 | 0.044 | |
|
|
| 1.5143 | 15500 | 0.045 | |
|
|
| 1.5192 | 15550 | 0.0414 | |
|
|
| 1.5241 | 15600 | 0.0439 | |
|
|
| 1.5289 | 15650 | 0.0381 | |
|
|
| 1.5338 | 15700 | 0.0425 | |
|
|
| 1.5387 | 15750 | 0.0439 | |
|
|
| 1.5436 | 15800 | 0.0405 | |
|
|
| 1.5485 | 15850 | 0.0407 | |
|
|
| 1.5534 | 15900 | 0.04 | |
|
|
| 1.5583 | 15950 | 0.0404 | |
|
|
| 1.5631 | 16000 | 0.0392 | |
|
|
| 1.5680 | 16050 | 0.0432 | |
|
|
| 1.5729 | 16100 | 0.0374 | |
|
|
| 1.5778 | 16150 | 0.044 | |
|
|
| 1.5827 | 16200 | 0.0429 | |
|
|
| 1.5876 | 16250 | 0.0394 | |
|
|
| 1.5924 | 16300 | 0.0446 | |
|
|
| 1.5973 | 16350 | 0.0389 | |
|
|
| 1.6022 | 16400 | 0.0429 | |
|
|
| 1.6071 | 16450 | 0.0442 | |
|
|
| 1.6120 | 16500 | 0.0394 | |
|
|
| 1.6169 | 16550 | 0.0403 | |
|
|
| 1.6218 | 16600 | 0.0414 | |
|
|
| 1.6266 | 16650 | 0.0386 | |
|
|
| 1.6315 | 16700 | 0.0401 | |
|
|
| 1.6364 | 16750 | 0.0415 | |
|
|
| 1.6413 | 16800 | 0.0427 | |
|
|
| 1.6462 | 16850 | 0.0412 | |
|
|
| 1.6511 | 16900 | 0.0404 | |
|
|
| 1.6560 | 16950 | 0.0402 | |
|
|
| 1.6608 | 17000 | 0.0394 | |
|
|
| 1.6657 | 17050 | 0.0429 | |
|
|
| 1.6706 | 17100 | 0.0452 | |
|
|
| 1.6755 | 17150 | 0.0438 | |
|
|
| 1.6804 | 17200 | 0.0433 | |
|
|
| 1.6853 | 17250 | 0.0393 | |
|
|
| 1.6901 | 17300 | 0.0405 | |
|
|
| 1.6950 | 17350 | 0.044 | |
|
|
| 1.6999 | 17400 | 0.042 | |
|
|
| 1.7048 | 17450 | 0.0401 | |
|
|
| 1.7097 | 17500 | 0.0417 | |
|
|
| 1.7146 | 17550 | 0.0351 | |
|
|
| 1.7195 | 17600 | 0.0367 | |
|
|
| 1.7243 | 17650 | 0.0436 | |
|
|
| 1.7292 | 17700 | 0.0392 | |
|
|
| 1.7341 | 17750 | 0.04 | |
|
|
| 1.7390 | 17800 | 0.0415 | |
|
|
| 1.7439 | 17850 | 0.0418 | |
|
|
| 1.7488 | 17900 | 0.0366 | |
|
|
| 1.7537 | 17950 | 0.0433 | |
|
|
| 1.7585 | 18000 | 0.0391 | |
|
|
| 1.7634 | 18050 | 0.0377 | |
|
|
| 1.7683 | 18100 | 0.0398 | |
|
|
| 1.7732 | 18150 | 0.0396 | |
|
|
| 1.7781 | 18200 | 0.0404 | |
|
|
| 1.7830 | 18250 | 0.0405 | |
|
|
| 1.7878 | 18300 | 0.0381 | |
|
|
| 1.7927 | 18350 | 0.04 | |
|
|
| 1.7976 | 18400 | 0.0404 | |
|
|
| 1.8025 | 18450 | 0.0348 | |
|
|
| 1.8074 | 18500 | 0.0397 | |
|
|
| 1.8123 | 18550 | 0.042 | |
|
|
| 1.8172 | 18600 | 0.0454 | |
|
|
| 1.8220 | 18650 | 0.0384 | |
|
|
| 1.8269 | 18700 | 0.0387 | |
|
|
| 1.8318 | 18750 | 0.042 | |
|
|
| 1.8367 | 18800 | 0.0413 | |
|
|
| 1.8416 | 18850 | 0.0403 | |
|
|
| 1.8465 | 18900 | 0.0417 | |
|
|
| 1.8514 | 18950 | 0.0386 | |
|
|
| 1.8562 | 19000 | 0.0417 | |
|
|
| 1.8611 | 19050 | 0.0396 | |
|
|
| 1.8660 | 19100 | 0.039 | |
|
|
| 1.8709 | 19150 | 0.0403 | |
|
|
| 1.8758 | 19200 | 0.0402 | |
|
|
| 1.8807 | 19250 | 0.044 | |
|
|
| 1.8855 | 19300 | 0.0413 | |
|
|
| 1.8904 | 19350 | 0.0379 | |
|
|
| 1.8953 | 19400 | 0.042 | |
|
|
| 1.9002 | 19450 | 0.0389 | |
|
|
| 1.9051 | 19500 | 0.0399 | |
|
|
| 1.9100 | 19550 | 0.0405 | |
|
|
| 1.9149 | 19600 | 0.0414 | |
|
|
| 1.9197 | 19650 | 0.0406 | |
|
|
| 1.9246 | 19700 | 0.037 | |
|
|
| 1.9295 | 19750 | 0.0406 | |
|
|
| 1.9344 | 19800 | 0.0433 | |
|
|
| 1.9393 | 19850 | 0.0357 | |
|
|
| 1.9442 | 19900 | 0.038 | |
|
|
| 1.9490 | 19950 | 0.0444 | |
|
|
| 1.9539 | 20000 | 0.0406 | |
|
|
| 1.9588 | 20050 | 0.0343 | |
|
|
| 1.9637 | 20100 | 0.0414 | |
|
|
| 1.9686 | 20150 | 0.0359 | |
|
|
| 1.9735 | 20200 | 0.0421 | |
|
|
| 1.9784 | 20250 | 0.0352 | |
|
|
| 1.9832 | 20300 | 0.0406 | |
|
|
| 1.9881 | 20350 | 0.0403 | |
|
|
| 1.9930 | 20400 | 0.0396 | |
|
|
| 1.9979 | 20450 | 0.0378 | |
|
|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.12 |
|
|
- Sentence Transformers: 5.2.1 |
|
|
- Transformers: 4.57.6 |
|
|
- PyTorch: 2.10.0+cu128 |
|
|
- Accelerate: 1.12.0 |
|
|
- Datasets: 4.3.0 |
|
|
- Tokenizers: 0.22.2 |
|
|
|
|
|
## 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}, |
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primaryClass={cs.CL} |
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} |
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``` |
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