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checkpoint-10000/1_Pooling/config.json

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+{
+  "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
+}

checkpoint-10000/README.md

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+---
+base_model: sentence-transformers/all-mpnet-base-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:1363306
+- loss:AnglELoss
+widget:
+- source_sentence: labneh
+  sentences:
+  - iftar
+  - bathing suit
+  - coffee cup
+- source_sentence: Velvet flock Veil
+  sentences:
+  - mermaid purse
+  - veil
+  - mobile bag
+- source_sentence: Red lipstick
+  sentences:
+  - chemise dress
+  - tote
+  - rouge
+- source_sentence: Unisex Travel bag
+  sentences:
+  - spf
+  - basic vega ring
+  - travel backpack
+- source_sentence: jeremy hush book
+  sentences:
+  - chinese jumper
+  - perfume
+  - home automation device
+---
+
+# all-mpnet-base-v3-pair_score
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
+- **Maximum Sequence Length:** 384 tokens
+- **Output Dimensionality:** 768 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (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})
+  (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 = [
+    'jeremy hush book',
+    'chinese jumper',
+    'perfume',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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   |
+|:------:|:-----:|:-------------:|:------:|
+| 0.0094 | 100   | 16.2337       | -      |
+| 0.0188 | 200   | 13.5901       | -      |
+| 0.0282 | 300   | 9.8565        | -      |
+| 0.0376 | 400   | 8.3332        | -      |
+| 0.0469 | 500   | 8.1261        | -      |
+| 0.0563 | 600   | 8.0697        | -      |
+| 0.0657 | 700   | 8.0298        | -      |
+| 0.0751 | 800   | 8.033         | -      |
+| 0.0845 | 900   | 7.9858        | -      |
+| 0.0939 | 1000  | 8.012         | -      |
+| 0.1033 | 1100  | 7.9745        | -      |
+| 0.1127 | 1200  | 8.0091        | -      |
+| 0.1221 | 1300  | 8.0221        | -      |
+| 0.1314 | 1400  | 7.9583        | -      |
+| 0.1408 | 1500  | 8.0031        | -      |
+| 0.1502 | 1600  | 7.9985        | -      |
+| 0.1596 | 1700  | 7.9647        | -      |
+| 0.1690 | 1800  | 7.9857        | -      |
+| 0.1784 | 1900  | 7.9806        | -      |
+| 0.1878 | 2000  | 7.9761        | -      |
+| 0.1972 | 2100  | 7.9696        | -      |
+| 0.2066 | 2200  | 8.0014        | -      |
+| 0.2159 | 2300  | 7.9546        | -      |
+| 0.2253 | 2400  | 7.9874        | -      |
+| 0.2347 | 2500  | 7.9846        | -      |
+| 0.2441 | 2600  | 7.9664        | -      |
+| 0.2535 | 2700  | 7.9725        | -      |
+| 0.2629 | 2800  | 7.9419        | -      |
+| 0.2723 | 2900  | 7.9786        | -      |
+| 0.2817 | 3000  | 7.9479        | -      |
+| 0.2911 | 3100  | 7.9526        | -      |
+| 0.3004 | 3200  | 7.9613        | -      |
+| 0.3098 | 3300  | 7.9994        | -      |
+| 0.3192 | 3400  | 7.9464        | -      |
+| 0.3286 | 3500  | 7.9429        | -      |
+| 0.3380 | 3600  | 7.9539        | -      |
+| 0.3474 | 3700  | 7.9699        | -      |
+| 0.3568 | 3800  | 7.9144        | -      |
+| 0.3662 | 3900  | 7.9424        | -      |
+| 0.3756 | 4000  | 7.9361        | -      |
+| 0.3849 | 4100  | 7.9144        | -      |
+| 0.3943 | 4200  | 7.907         | -      |
+| 0.4037 | 4300  | 7.9049        | -      |
+| 0.4131 | 4400  | 7.939         | -      |
+| 0.4225 | 4500  | 7.9067        | -      |
+| 0.4319 | 4600  | 7.9149        | -      |
+| 0.4413 | 4700  | 7.9705        | -      |
+| 0.4507 | 4800  | 7.8992        | -      |
+| 0.4601 | 4900  | 7.9077        | -      |
+| 0.4694 | 5000  | 7.8992        | 7.9167 |
+| 0.4788 | 5100  | 7.914         | -      |
+| 0.4882 | 5200  | 7.8913        | -      |
+| 0.4976 | 5300  | 7.8999        | -      |
+| 0.5070 | 5400  | 7.8818        | -      |
+| 0.5164 | 5500  | 7.9383        | -      |
+| 0.5258 | 5600  | 7.9094        | -      |
+| 0.5352 | 5700  | 7.8986        | -      |
+| 0.5445 | 5800  | 7.9015        | -      |
+| 0.5539 | 5900  | 7.9059        | -      |
+| 0.5633 | 6000  | 7.8524        | -      |
+| 0.5727 | 6100  | 7.8788        | -      |
+| 0.5821 | 6200  | 7.8712        | -      |
+| 0.5915 | 6300  | 7.8967        | -      |
+| 0.6009 | 6400  | 7.8677        | -      |
+| 0.6103 | 6500  | 7.9132        | -      |
+| 0.6197 | 6600  | 7.853         | -      |
+| 0.6290 | 6700  | 7.8968        | -      |
+| 0.6384 | 6800  | 7.8656        | -      |
+| 0.6478 | 6900  | 7.8801        | -      |
+| 0.6572 | 7000  | 7.8378        | -      |
+| 0.6666 | 7100  | 7.8554        | -      |
+| 0.6760 | 7200  | 7.8305        | -      |
+| 0.6854 | 7300  | 7.8613        | -      |
+| 0.6948 | 7400  | 7.8554        | -      |
+| 0.7042 | 7500  | 7.8653        | -      |
+| 0.7135 | 7600  | 7.8387        | -      |
+| 0.7229 | 7700  | 7.8513        | -      |
+| 0.7323 | 7800  | 7.8496        | -      |
+| 0.7417 | 7900  | 7.8276        | -      |
+| 0.7511 | 8000  | 7.8353        | -      |
+| 0.7605 | 8100  | 7.8103        | -      |
+| 0.7699 | 8200  | 7.8622        | -      |
+| 0.7793 | 8300  | 7.832         | -      |
+| 0.7887 | 8400  | 7.8349        | -      |
+| 0.7980 | 8500  | 7.855         | -      |
+| 0.8074 | 8600  | 7.8316        | -      |
+| 0.8168 | 8700  | 7.8066        | -      |
+| 0.8262 | 8800  | 7.8166        | -      |
+| 0.8356 | 8900  | 7.8588        | -      |
+| 0.8450 | 9000  | 7.8042        | -      |
+| 0.8544 | 9100  | 7.8431        | -      |
+| 0.8638 | 9200  | 7.7947        | -      |
+| 0.8732 | 9300  | 7.8175        | -      |
+| 0.8825 | 9400  | 7.8299        | -      |
+| 0.8919 | 9500  | 7.8455        | -      |
+| 0.9013 | 9600  | 7.8072        | -      |
+| 0.9107 | 9700  | 7.8052        | -      |
+| 0.9201 | 9800  | 7.7953        | -      |
+| 0.9295 | 9900  | 7.8497        | -      |
+| 0.9389 | 10000 | 7.801         | 7.7835 |
+
+
+### 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-10000/config.json

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+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "vocab_size": 30527
+}

checkpoint-10000/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
+}

checkpoint-10000/model.safetensors

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+size 437967672

checkpoint-10000/modules.json

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

checkpoint-10000/optimizer.pt

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checkpoint-10000/rng_state.pth

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checkpoint-10000/scheduler.pt

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checkpoint-10000/sentence_bert_config.json

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+{
+  "max_seq_length": 384,
+  "do_lower_case": false
+}

checkpoint-10000/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-10000/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
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checkpoint-10000/trainer_state.json

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