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---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:23863
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: SFP+ 10GBase-SR 10 Gigabit Ethernet Optics, 850nm for up to 300m
transmission on MMF
sentences:
- Software
- Data Voice or Multimedia Network Equipment or Platforms and Accessories
- Components for information technology or broadcasting or telecommunications
- source_sentence: Apple Macbook Pro Retina 15.4 inch Intel Core i7 2.5GHz 16GB 512GB
SSD MJLT2TU/A
sentences:
- Consumer electronics
- Office supply
- Computer Equipment and Accessories
- source_sentence: Switch and Route Processing Unit A5(Including 1*2G Memory and 1*1G
CF Card)
sentences:
- Data Voice or Multimedia Network Equipment or Platforms and Accessories
- Components for information technology or broadcasting or telecommunications
- Consumer electronics
- source_sentence: Samsung Gear VR R325
sentences:
- Computer Equipment and Accessories
- Data Voice or Multimedia Network Equipment or Platforms and Accessories
- Communications Devices and Accessories
- source_sentence: SUN.Sun Fire T1000 Server, 6 core, 1.0GHz UltraSPARC T1 processor,
4GB DDR2 memory (4 * 1GB DIMMs), 160 SATA hard disk drive.
sentences:
- Computer Equipment and Accessories
- Communications Devices and Accessories
- Domestic appliances
model-index:
- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: .nan
name: Pearson Manhattan
- type: spearman_manhattan
value: .nan
name: Spearman Manhattan
- type: pearson_euclidean
value: .nan
name: Pearson Euclidean
- type: spearman_euclidean
value: .nan
name: Spearman Euclidean
- type: pearson_dot
value: .nan
name: Pearson Dot
- type: spearman_dot
value: .nan
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
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 f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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("alpcansoydas/product-model-17.10.24-ifhavemorethan100sampleperfamily")
# Run inference
sentences = [
'SUN.Sun Fire T1000 Server, 6 core, 1.0GHz UltraSPARC T1 processor, 4GB DDR2 memory (4 * 1GB DIMMs), 160 SATA hard disk drive.',
'Computer Equipment and Accessories',
'Communications Devices and Accessories',
]
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]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:-------------------|:--------|
| pearson_cosine | nan |
| spearman_cosine | nan |
| pearson_manhattan | nan |
| spearman_manhattan | nan |
| pearson_euclidean | nan |
| spearman_euclidean | nan |
| pearson_dot | nan |
| spearman_dot | nan |
| pearson_max | nan |
| **spearman_max** | **nan** |
<!--
## 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 Dataset
#### Unnamed Dataset
* Size: 23,863 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 16.7 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.97 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------|
| <code>High_Performance_DB_HPE ProLiant DL380 Gen10 8SFF</code> | <code>Computer Equipment and Accessories</code> |
| <code>HP PROLIANT DL160 G7 SERVER</code> | <code>Computer Equipment and Accessories</code> |
| <code>ZTE 24-port GE SFP Physical Line Interface Unit Z</code> | <code>Components for information technology or broadcasting or telecommunications</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 5,114 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 17.25 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.83 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------|:-------------------------------------------------------------------------------------|
| <code>Symantec Security Analytics</code> | <code>Computer Equipment and Accessories</code> |
| <code>RAU2 X 7/A28 HP Kit HIGH</code> | <code>Data Voice or Multimedia Network Equipment or Platforms and Accessories</code> |
| <code>HPE DL360 Gen9 8SFF CTO Server</code> | <code>Computer Equipment and Accessories</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5e-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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | spearman_max |
|:------:|:----:|:-------------:|:---------------:|:------------:|
| 0.0670 | 100 | 2.2597 | 1.9744 | nan |
| 0.1340 | 200 | 1.9663 | 1.8451 | nan |
| 0.2011 | 300 | 1.9035 | 1.8232 | nan |
| 0.2681 | 400 | 1.8447 | 1.7664 | nan |
| 0.3351 | 500 | 1.7951 | 1.7387 | nan |
| 0.4021 | 600 | 1.7409 | 1.7485 | nan |
| 0.4692 | 700 | 1.7049 | 1.7022 | nan |
| 0.5362 | 800 | 1.7058 | 1.6885 | nan |
| 0.6032 | 900 | 1.6933 | 1.6730 | nan |
| 0.6702 | 1000 | 1.7053 | 1.6562 | nan |
| 0.7373 | 1100 | 1.6289 | 1.6613 | nan |
| 0.8043 | 1200 | 1.6046 | 1.6571 | nan |
| 0.8713 | 1300 | 1.6332 | 1.6420 | nan |
| 0.9383 | 1400 | 1.6431 | 1.6107 | nan |
| 1.0054 | 1500 | 1.6104 | 1.6309 | nan |
| 1.0724 | 1600 | 1.5444 | 1.6234 | nan |
| 1.1394 | 1700 | 1.4944 | 1.6043 | nan |
| 1.2064 | 1800 | 1.5099 | 1.6083 | nan |
| 1.2735 | 1900 | 1.4763 | 1.6369 | nan |
| 1.3405 | 2000 | 1.5351 | 1.5959 | nan |
| 1.4075 | 2100 | 1.4537 | 1.6378 | nan |
| 1.4745 | 2200 | 1.5263 | 1.5769 | nan |
| 1.5416 | 2300 | 1.46 | 1.5889 | nan |
| 1.6086 | 2400 | 1.4781 | 1.5744 | nan |
| 1.6756 | 2500 | 1.4932 | 1.5663 | nan |
| 1.7426 | 2600 | 1.4158 | 1.5585 | nan |
| 1.8097 | 2700 | 1.4571 | 1.5580 | nan |
| 1.8767 | 2800 | 1.4078 | 1.5627 | nan |
| 1.9437 | 2900 | 1.4205 | 1.5622 | nan |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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