---
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:CoSENTLoss
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-v2-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)
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **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]
```
## 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
Click to expand
- `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
Click to expand
| Epoch | Step | Training Loss | loss |
|:------:|:-----:|:-------------:|:------:|
| 0.0094 | 100 | 16.0755 | - |
| 0.0188 | 200 | 13.0643 | - |
| 0.0282 | 300 | 9.3474 | - |
| 0.0376 | 400 | 8.2606 | - |
| 0.0469 | 500 | 8.084 | - |
| 0.0563 | 600 | 8.0581 | - |
| 0.0657 | 700 | 8.0175 | - |
| 0.0751 | 800 | 8.0285 | - |
| 0.0845 | 900 | 8.0024 | - |
| 0.0939 | 1000 | 8.0161 | - |
| 0.1033 | 1100 | 7.9941 | - |
| 0.1127 | 1200 | 8.0233 | - |
| 0.1221 | 1300 | 8.0141 | - |
| 0.1314 | 1400 | 7.9644 | - |
| 0.1408 | 1500 | 8.0311 | - |
| 0.1502 | 1600 | 8.0306 | - |
| 0.1596 | 1700 | 7.989 | - |
| 0.1690 | 1800 | 8.0034 | - |
| 0.1784 | 1900 | 8.0107 | - |
| 0.1878 | 2000 | 7.9737 | - |
| 0.1972 | 2100 | 7.9827 | - |
| 0.2066 | 2200 | 8.0389 | - |
| 0.2159 | 2300 | 7.973 | - |
| 0.2253 | 2400 | 7.9669 | - |
| 0.2347 | 2500 | 8.0296 | - |
| 0.2441 | 2600 | 7.9984 | - |
| 0.2535 | 2700 | 7.9772 | - |
| 0.2629 | 2800 | 7.9838 | - |
| 0.2723 | 2900 | 7.9816 | - |
| 0.2817 | 3000 | 8.0021 | - |
| 0.2911 | 3100 | 7.9715 | - |
| 0.3004 | 3200 | 7.9809 | - |
| 0.3098 | 3300 | 7.9849 | - |
| 0.3192 | 3400 | 7.9463 | - |
| 0.3286 | 3500 | 8.0067 | - |
| 0.3380 | 3600 | 7.9431 | - |
| 0.3474 | 3700 | 7.9877 | - |
| 0.3568 | 3800 | 7.9494 | - |
| 0.3662 | 3900 | 7.9466 | - |
| 0.3756 | 4000 | 7.9708 | - |
| 0.3849 | 4100 | 7.9525 | - |
| 0.3943 | 4200 | 7.9322 | - |
| 0.4037 | 4300 | 7.9415 | - |
| 0.4131 | 4400 | 7.9932 | - |
| 0.4225 | 4500 | 7.9481 | - |
| 0.4319 | 4600 | 7.976 | - |
| 0.4413 | 4700 | 7.971 | - |
| 0.4507 | 4800 | 7.9647 | - |
| 0.4601 | 4900 | 7.9217 | - |
| 0.4694 | 5000 | 7.9374 | 7.9518 |
| 0.4788 | 5100 | 7.9026 | - |
| 0.4882 | 5200 | 7.9304 | - |
| 0.4976 | 5300 | 7.9148 | - |
| 0.5070 | 5400 | 7.9538 | - |
| 0.5164 | 5500 | 8.0002 | - |
| 0.5258 | 5600 | 7.9571 | - |
| 0.5352 | 5700 | 7.932 | - |
| 0.5445 | 5800 | 7.9047 | - |
| 0.5539 | 5900 | 7.9353 | - |
| 0.5633 | 6000 | 7.9203 | - |
| 0.5727 | 6100 | 7.8967 | - |
| 0.5821 | 6200 | 7.9414 | - |
| 0.5915 | 6300 | 7.9631 | - |
| 0.6009 | 6400 | 7.9606 | - |
| 0.6103 | 6500 | 7.9377 | - |
| 0.6197 | 6600 | 7.9108 | - |
| 0.6290 | 6700 | 7.9225 | - |
| 0.6384 | 6800 | 7.9154 | - |
| 0.6478 | 6900 | 7.9191 | - |
| 0.6572 | 7000 | 7.8903 | - |
| 0.6666 | 7100 | 7.9213 | - |
| 0.6760 | 7200 | 7.9202 | - |
| 0.6854 | 7300 | 7.8998 | - |
| 0.6948 | 7400 | 7.9153 | - |
| 0.7042 | 7500 | 7.9037 | - |
| 0.7135 | 7600 | 7.9146 | - |
| 0.7229 | 7700 | 7.8972 | - |
| 0.7323 | 7800 | 7.9374 | - |
| 0.7417 | 7900 | 7.8647 | - |
| 0.7511 | 8000 | 7.8915 | - |
| 0.7605 | 8100 | 7.8846 | - |
| 0.7699 | 8200 | 7.8988 | - |
| 0.7793 | 8300 | 7.8702 | - |
| 0.7887 | 8400 | 7.923 | - |
| 0.7980 | 8500 | 7.891 | - |
| 0.8074 | 8600 | 7.8832 | - |
| 0.8168 | 8700 | 7.8726 | - |
| 0.8262 | 8800 | 7.8813 | - |
| 0.8356 | 8900 | 7.8986 | - |
| 0.8450 | 9000 | 7.8743 | - |
| 0.8544 | 9100 | 7.8791 | - |
| 0.8638 | 9200 | 7.8783 | - |
| 0.8732 | 9300 | 7.8528 | - |
| 0.8825 | 9400 | 7.8864 | - |
| 0.8919 | 9500 | 7.8989 | - |
| 0.9013 | 9600 | 7.8617 | - |
| 0.9107 | 9700 | 7.8371 | - |
| 0.9201 | 9800 | 7.8566 | - |
| 0.9295 | 9900 | 7.8776 | - |
| 0.9389 | 10000 | 7.8558 | 7.8492 |
| 0.9483 | 10100 | 7.848 | - |
| 0.9577 | 10200 | 7.8227 | - |
| 0.9670 | 10300 | 7.8311 | - |
| 0.9764 | 10400 | 7.8437 | - |
| 0.9858 | 10500 | 7.8454 | - |
| 0.9952 | 10600 | 7.8362 | - |
| 1.0046 | 10700 | 7.8681 | - |
| 1.0140 | 10800 | 7.8745 | - |
| 1.0234 | 10900 | 7.8339 | - |
| 1.0328 | 11000 | 7.8458 | - |
| 1.0422 | 11100 | 7.8493 | - |
| 1.0515 | 11200 | 7.8317 | - |
| 1.0609 | 11300 | 7.841 | - |
| 1.0703 | 11400 | 7.8292 | - |
| 1.0797 | 11500 | 7.8121 | - |
| 1.0891 | 11600 | 7.8165 | - |
| 1.0985 | 11700 | 7.8259 | - |
| 1.1079 | 11800 | 7.8303 | - |
| 1.1173 | 11900 | 7.809 | - |
| 1.1267 | 12000 | 7.818 | - |
| 1.1360 | 12100 | 7.8071 | - |
| 1.1454 | 12200 | 7.801 | - |
| 1.1548 | 12300 | 7.8123 | - |
| 1.1642 | 12400 | 7.8203 | - |
| 1.1736 | 12500 | 7.8609 | - |
| 1.1830 | 12600 | 7.7782 | - |
| 1.1924 | 12700 | 7.8092 | - |
| 1.2018 | 12800 | 7.815 | - |
| 1.2112 | 12900 | 7.8196 | - |
| 1.2205 | 13000 | 7.8206 | - |
| 1.2299 | 13100 | 7.8022 | - |
| 1.2393 | 13200 | 7.8043 | - |
| 1.2487 | 13300 | 7.7823 | - |
| 1.2581 | 13400 | 7.8061 | - |
| 1.2675 | 13500 | 7.8016 | - |
| 1.2769 | 13600 | 7.8076 | - |
| 1.2863 | 13700 | 7.7996 | - |
| 1.2957 | 13800 | 7.8035 | - |
| 1.3050 | 13900 | 7.8092 | - |
| 1.3144 | 14000 | 7.7902 | - |
| 1.3238 | 14100 | 7.8114 | - |
| 1.3332 | 14200 | 7.8112 | - |
| 1.3426 | 14300 | 7.8036 | - |
| 1.3520 | 14400 | 7.8178 | - |
| 1.3614 | 14500 | 7.8391 | - |
| 1.3708 | 14600 | 7.8151 | - |
| 1.3802 | 14700 | 7.7957 | - |
| 1.3895 | 14800 | 7.7833 | - |
| 1.3989 | 14900 | 7.8049 | - |
| 1.4083 | 15000 | 7.8163 | 7.8078 |
| 1.4177 | 15100 | 7.7864 | - |
| 1.4271 | 15200 | 7.8241 | - |
| 1.4365 | 15300 | 7.7694 | - |
| 1.4459 | 15400 | 7.7784 | - |
| 1.4553 | 15500 | 7.7628 | - |
| 1.4647 | 15600 | 7.8044 | - |
| 1.4740 | 15700 | 7.7871 | - |
| 1.4834 | 15800 | 7.809 | - |
| 1.4928 | 15900 | 7.7955 | - |
| 1.5022 | 16000 | 7.8056 | - |
| 1.5116 | 16100 | 7.774 | - |
| 1.5210 | 16200 | 7.7874 | - |
| 1.5304 | 16300 | 7.7918 | - |
| 1.5398 | 16400 | 7.7787 | - |
| 1.5492 | 16500 | 7.7881 | - |
| 1.5585 | 16600 | 7.7723 | - |
| 1.5679 | 16700 | 7.7809 | - |
| 1.5773 | 16800 | 7.8096 | - |
| 1.5867 | 16900 | 7.7559 | - |
| 1.5961 | 17000 | 7.8063 | - |
| 1.6055 | 17100 | 7.8137 | - |
| 1.6149 | 17200 | 7.761 | - |
| 1.6243 | 17300 | 7.7672 | - |
| 1.6336 | 17400 | 7.7939 | - |
| 1.6430 | 17500 | 7.8052 | - |
| 1.6524 | 17600 | 7.7519 | - |
| 1.6618 | 17700 | 7.7643 | - |
| 1.6712 | 17800 | 7.7823 | - |
| 1.6806 | 17900 | 7.7507 | - |
| 1.6900 | 18000 | 7.777 | - |
| 1.6994 | 18100 | 7.786 | - |
| 1.7088 | 18200 | 7.8097 | - |
| 1.7181 | 18300 | 7.7749 | - |
| 1.7275 | 18400 | 7.7626 | - |
| 1.7369 | 18500 | 7.7783 | - |
| 1.7463 | 18600 | 7.7552 | - |
| 1.7557 | 18700 | 7.7837 | - |
| 1.7651 | 18800 | 7.7583 | - |
| 1.7745 | 18900 | 7.7617 | - |
| 1.7839 | 19000 | 7.7649 | - |
| 1.7933 | 19100 | 7.7767 | - |
| 1.8026 | 19200 | 7.7565 | - |
| 1.8120 | 19300 | 7.7702 | - |
| 1.8214 | 19400 | 7.7552 | - |
| 1.8308 | 19500 | 7.7511 | - |
| 1.8402 | 19600 | 7.7818 | - |
| 1.8496 | 19700 | 7.7704 | - |
| 1.8590 | 19800 | 7.7824 | - |
| 1.8684 | 19900 | 7.751 | - |
| 1.8778 | 20000 | 7.7868 | 7.7942 |
| 1.8871 | 20100 | 7.7981 | - |
| 1.8965 | 20200 | 7.7673 | - |
| 1.9059 | 20300 | 7.7695 | - |
| 1.9153 | 20400 | 7.7587 | - |
| 1.9247 | 20500 | 7.7444 | - |
| 1.9341 | 20600 | 7.7736 | - |
| 1.9435 | 20700 | 7.7655 | - |
| 1.9529 | 20800 | 7.7686 | - |
| 1.9623 | 20900 | 7.7731 | - |
| 1.9716 | 21000 | 7.7527 | - |
| 1.9810 | 21100 | 7.7962 | - |
| 1.9904 | 21200 | 7.7676 | - |
| 1.9998 | 21300 | 7.7641 | - |