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checkpoint-7500/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

checkpoint-7500/README.md

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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+language:
+- en
+library_name: sentence-transformers
+license: apache-2.0
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:1830648
+- loss:AnglELoss
+widget:
+- source_sentence: crunchy chips
+  sentences:
+  - big chips spiced gouda
+  - purse
+  - macaroni
+- source_sentence: genuine leather luggage
+  sentences:
+  - janatte luggage
+  - bomb chemise
+  - purse
+- source_sentence: head covers Rashguard
+  sentences:
+  - Double Shaded Blue Clutch
+  - Rashguard
+  - bathing costume
+- source_sentence: hand Made Sweatpants
+  sentences:
+  - acid cleanser
+  - reflective weave sweatpants
+  - rashguard
+- source_sentence: siamy wrap
+  sentences:
+  - siamy
+  - hair revival
+  - backpack
+---
+
+# all-MiniLM-L6-v5-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'siamy wrap',
+    'siamy',
+    'hair revival',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.1
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 128
+- `per_device_eval_batch_size`: 128
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `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
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | loss   |
+|:------:|:----:|:-------------:|:------:|
+| 0.0070 | 100  | 16.865        | -      |
+| 0.0140 | 200  | 16.1556       | -      |
+| 0.0210 | 300  | 14.8008       | -      |
+| 0.0280 | 400  | 12.4025       | -      |
+| 0.0350 | 500  | 9.7465        | -      |
+| 0.0420 | 600  | 8.448         | -      |
+| 0.0489 | 700  | 8.1951        | -      |
+| 0.0559 | 800  | 8.1093        | -      |
+| 0.0629 | 900  | 8.0567        | -      |
+| 0.0699 | 1000 | 8.0401        | -      |
+| 0.0769 | 1100 | 7.9491        | -      |
+| 0.0839 | 1200 | 7.9494        | -      |
+| 0.0909 | 1300 | 7.9386        | -      |
+| 0.0979 | 1400 | 7.9033        | -      |
+| 0.1049 | 1500 | 7.9055        | -      |
+| 0.1119 | 1600 | 7.9203        | -      |
+| 0.1189 | 1700 | 7.8381        | -      |
+| 0.1259 | 1800 | 7.8679        | -      |
+| 0.1328 | 1900 | 7.8686        | -      |
+| 0.1398 | 2000 | 7.8252        | -      |
+| 0.1468 | 2100 | 7.856         | -      |
+| 0.1538 | 2200 | 7.8301        | -      |
+| 0.1608 | 2300 | 7.8595        | -      |
+| 0.1678 | 2400 | 7.8138        | -      |
+| 0.1748 | 2500 | 7.812         | -      |
+| 0.1818 | 2600 | 7.8261        | -      |
+| 0.1888 | 2700 | 7.7988        | -      |
+| 0.1958 | 2800 | 7.7965        | -      |
+| 0.2028 | 2900 | 7.783         | -      |
+| 0.2098 | 3000 | 7.7752        | -      |
+| 0.2168 | 3100 | 7.7715        | -      |
+| 0.2237 | 3200 | 7.7903        | -      |
+| 0.2307 | 3300 | 7.7656        | -      |
+| 0.2377 | 3400 | 7.749         | -      |
+| 0.2447 | 3500 | 7.7662        | -      |
+| 0.2517 | 3600 | 7.7492        | -      |
+| 0.2587 | 3700 | 7.737         | -      |
+| 0.2657 | 3800 | 7.7232        | -      |
+| 0.2727 | 3900 | 7.7616        | -      |
+| 0.2797 | 4000 | 7.7391        | -      |
+| 0.2867 | 4100 | 7.7552        | -      |
+| 0.2937 | 4200 | 7.7273        | -      |
+| 0.3007 | 4300 | 7.7216        | -      |
+| 0.3076 | 4400 | 7.7371        | -      |
+| 0.3146 | 4500 | 7.7426        | -      |
+| 0.3216 | 4600 | 7.7406        | -      |
+| 0.3286 | 4700 | 7.712         | -      |
+| 0.3356 | 4800 | 7.7466        | -      |
+| 0.3426 | 4900 | 7.7058        | -      |
+| 0.3496 | 5000 | 7.7139        | 7.6896 |
+| 0.3566 | 5100 | 7.7457        | -      |
+| 0.3636 | 5200 | 7.7172        | -      |
+| 0.3706 | 5300 | 7.739         | -      |
+| 0.3776 | 5400 | 7.7259        | -      |
+| 0.3846 | 5500 | 7.6977        | -      |
+| 0.3916 | 5600 | 7.7237        | -      |
+| 0.3985 | 5700 | 7.7118        | -      |
+| 0.4055 | 5800 | 7.7099        | -      |
+| 0.4125 | 5900 | 7.7142        | -      |
+| 0.4195 | 6000 | 7.6885        | -      |
+| 0.4265 | 6100 | 7.6799        | -      |
+| 0.4335 | 6200 | 7.7039        | -      |
+| 0.4405 | 6300 | 7.6825        | -      |
+| 0.4475 | 6400 | 7.6846        | -      |
+| 0.4545 | 6500 | 7.7078        | -      |
+| 0.4615 | 6600 | 7.6945        | -      |
+| 0.4685 | 6700 | 7.7017        | -      |
+| 0.4755 | 6800 | 7.6781        | -      |
+| 0.4825 | 6900 | 7.6885        | -      |
+| 0.4894 | 7000 | 7.7426        | -      |
+| 0.4964 | 7100 | 7.6809        | -      |
+| 0.5034 | 7200 | 7.6977        | -      |
+| 0.5104 | 7300 | 7.6964        | -      |
+| 0.5174 | 7400 | 7.6834        | -      |
+| 0.5244 | 7500 | 7.6593        | -      |
+
+
+### Framework Versions
+- Python: 3.8.10
+- Sentence Transformers: 3.1.1
+- Transformers: 4.45.2
+- PyTorch: 2.4.1+cu118
+- Accelerate: 1.0.1
+- Datasets: 3.0.1
+- Tokenizers: 0.20.3
+
+## 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",
+}
+```
+
+#### AnglELoss
+```bibtex
+@misc{li2023angleoptimized,
+    title={AnglE-optimized Text Embeddings},
+    author={Xianming Li and Jing Li},
+    year={2023},
+    eprint={2309.12871},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

checkpoint-7500/config.json

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+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

checkpoint-7500/config_sentence_transformers.json

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+  "__version__": {
+    "sentence_transformers": "3.1.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.4.1+cu118"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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checkpoint-7500/modules.json

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

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checkpoint-7500/sentence_bert_config.json

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

checkpoint-7500/special_tokens_map.json

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checkpoint-7500/tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

checkpoint-7500/trainer_state.json

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