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neel2306
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RE-cp-costgen

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1439
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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RE-cp-costgen / README.md
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 ---
base_model: thenlper/gte-base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1439
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Motors and Generators (manufacturing)
sentences:
- Generator components
- Hydraulic pumps
- Positive displacement pumps for oil transport
- source_sentence: Heat Exchangers and Boilers Manufacturing
sentences:
- Insulation materials for boilers
- Water heaters
- Lubricants for roller bearings
- source_sentence: Industrial Molds And Mold Boxes
sentences:
- Logistics costs for machinery distribution
- Mold release agents
- Mold design and engineering services
- source_sentence: Industrial Patterns
sentences:
- Group I base oils
- Pattern making services
- Design patterns in software
- source_sentence: Lubricating And Similar Oils Not From Petroleum Refineries
sentences:
- Crude oil extraction costs
- Synthetic lubricants
- Crude oil
---
# SentenceTransformer based on thenlper/gte-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). 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:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) <!-- at revision 5e95d41db6721e7cbd5006e99c7508f0083223d6 -->
- **Maximum Sequence Length:** 512 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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("neel2306/RE-cp-costgen")
# Run inference
sentences = [
'Lubricating And Similar Oils Not From Petroleum Refineries',
'Synthetic lubricants',
'Crude oil',
]
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><summary>Click to expand</summary>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,439 training samples
* Columns: <code>anchor</code>, <code>positives</code>, and <code>negatives</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positives | negatives |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.72 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.96 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 11 tokens</li></ul> |
* Samples:
| anchor | positives | negatives |
|:------------------------------------------------------------------------------|:-----------------------------------------------------|:------------------------------------------------------|
| <code>Other Metal Valve and Pipe Fitting Manufacturing</code> | <code>Pipe fittings</code> | <code>Rubber gaskets</code> |
| <code>Fluid Power Pump and Motor Manufacturing: Miscellaneous Receipts</code> | <code>Pneumatic motors</code> | <code>Gear pumps</code> |
| <code>Maintenance and Repair for Commercial Machinery</code> | <code>Labor costs for maintenance technicians</code> | <code>Office supplies for administrative tasks</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: 480 evaluation samples
* Columns: <code>anchor</code>, <code>positives</code>, and <code>negatives</code>
* Approximate statistics based on the first 480 samples:
| | anchor | positives | negatives |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 10.4 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.09 tokens</li><li>max: 14 tokens</li></ul> |
* Samples:
| anchor | positives | negatives |
|:-----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|:-------------------------------------------|
| <code>Other Metal Ore Mining</code> | <code>Aluminum ore processing</code> | <code>Metal alloy production</code> |
| <code>Bituminous Coal And Lignite Surface Mining: Processed Bituminous Coal And Lignite From Surface Operations</code> | <code>Processed Bituminous Coal</code> | <code>Anthracite Coal</code> |
| <code>Roofing Contractors</code> | <code>Labor costs for roofing installation</code> | <code>Foundation construction costs</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`: 4
- `per_device_eval_batch_size`: 4
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### 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`: 4
- `per_device_eval_batch_size`: 4
- `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`: 15
- `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`: False
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss |
|:-------:|:----:|:-------------:|:------:|
| 0.1389 | 50 | 0.955 | 0.8155 |
| 0.2778 | 100 | 0.8643 | 0.6782 |
| 0.4167 | 150 | 0.6977 | 0.5452 |
| 0.5556 | 200 | 0.5738 | 0.4514 |
| 0.6944 | 250 | 0.3365 | 0.5229 |
| 0.8333 | 300 | 0.3888 | 0.4742 |
| 0.9722 | 350 | 0.4754 | 0.3900 |
| 1.1111 | 400 | 0.4109 | 0.4337 |
| 1.25 | 450 | 0.3081 | 0.3950 |
| 1.3889 | 500 | 0.3282 | 0.3345 |
| 1.5278 | 550 | 0.2371 | 0.3538 |
| 1.6667 | 600 | 0.1282 | 0.4055 |
| 1.8056 | 650 | 0.1091 | 0.5044 |
| 1.9444 | 700 | 0.2137 | 0.4423 |
| 2.0833 | 750 | 0.1169 | 0.4840 |
| 2.2222 | 800 | 0.1076 | 0.4867 |
| 2.3611 | 850 | 0.1669 | 0.4859 |
| 2.5 | 900 | 0.074 | 0.4873 |
| 2.6389 | 950 | 0.0519 | 0.4409 |
| 2.7778 | 1000 | 0.0257 | 0.4604 |
| 2.9167 | 1050 | 0.0749 | 0.4678 |
| 3.0556 | 1100 | 0.0393 | 0.4564 |
| 3.1944 | 1150 | 0.0454 | 0.4301 |
| 3.3333 | 1200 | 0.062 | 0.4882 |
| 3.4722 | 1250 | 0.0645 | 0.4434 |
| 3.6111 | 1300 | 0.0115 | 0.4296 |
| 3.75 | 1350 | 0.0172 | 0.4398 |
| 3.8889 | 1400 | 0.0429 | 0.4396 |
| 4.0278 | 1450 | 0.0115 | 0.4482 |
| 4.1667 | 1500 | 0.0141 | 0.4597 |
| 4.3056 | 1550 | 0.0032 | 0.4776 |
| 4.4444 | 1600 | 0.0288 | 0.4693 |
| 4.5833 | 1650 | 0.006 | 0.4990 |
| 4.7222 | 1700 | 0.0222 | 0.4693 |
| 4.8611 | 1750 | 0.0016 | 0.4755 |
| 5.0 | 1800 | 0.0016 | 0.4367 |
| 5.1389 | 1850 | 0.0084 | 0.3789 |
| 5.2778 | 1900 | 0.0013 | 0.3689 |
| 5.4167 | 1950 | 0.0554 | 0.3591 |
| 5.5556 | 2000 | 0.0022 | 0.3691 |
| 5.6944 | 2050 | 0.0019 | 0.3776 |
| 5.8333 | 2100 | 0.0008 | 0.3802 |
| 5.9722 | 2150 | 0.0006 | 0.3799 |
| 6.1111 | 2200 | 0.0007 | 0.3688 |
| 6.25 | 2250 | 0.0003 | 0.3635 |
| 6.3889 | 2300 | 0.0125 | 0.3526 |
| 6.5278 | 2350 | 0.0034 | 0.3338 |
| 6.6667 | 2400 | 0.0003 | 0.3482 |
| 6.8056 | 2450 | 0.0149 | 0.3730 |
| 6.9444 | 2500 | 0.0004 | 0.3932 |
| 7.0833 | 2550 | 0.0003 | 0.3977 |
| 7.2222 | 2600 | 0.0007 | 0.3915 |
| 7.3611 | 2650 | 0.0112 | 0.3923 |
| 7.5 | 2700 | 0.0006 | 0.3938 |
| 7.6389 | 2750 | 0.0002 | 0.3986 |
| 7.7778 | 2800 | 0.0005 | 0.3946 |
| 7.9167 | 2850 | 0.0003 | 0.3944 |
| 8.0556 | 2900 | 0.0002 | 0.3996 |
| 8.1944 | 2950 | 0.0001 | 0.4032 |
| 8.3333 | 3000 | 0.0001 | 0.4018 |
| 8.4722 | 3050 | 0.0119 | 0.3811 |
| 8.6111 | 3100 | 0.0001 | 0.3826 |
| 8.75 | 3150 | 0.0001 | 0.3844 |
| 8.8889 | 3200 | 0.0002 | 0.3893 |
| 9.0278 | 3250 | 0.0001 | 0.3942 |
| 9.1667 | 3300 | 0.0001 | 0.3963 |
| 9.3056 | 3350 | 0.0001 | 0.3965 |
| 9.4444 | 3400 | 0.0144 | 0.3766 |
| 9.5833 | 3450 | 0.0002 | 0.3792 |
| 9.7222 | 3500 | 0.0001 | 0.3830 |
| 9.8611 | 3550 | 0.0001 | 0.3870 |
| 10.0 | 3600 | 0.0002 | 0.3909 |
| 10.1389 | 3650 | 0.0001 | 0.3939 |
| 10.2778 | 3700 | 0.0001 | 0.3943 |
| 10.4167 | 3750 | 0.0103 | 0.3896 |
| 10.5556 | 3800 | 0.0001 | 0.3906 |
| 10.6944 | 3850 | 0.0001 | 0.3929 |
| 10.8333 | 3900 | 0.0001 | 0.3957 |
| 10.9722 | 3950 | 0.0001 | 0.3969 |
| 11.1111 | 4000 | 0.0001 | 0.4016 |
| 11.25 | 4050 | 0.0001 | 0.4012 |
| 11.3889 | 4100 | 0.0049 | 0.4058 |
| 11.5278 | 4150 | 0.0002 | 0.4117 |
| 11.6667 | 4200 | 0.0001 | 0.4121 |
| 11.8056 | 4250 | 0.0001 | 0.4131 |
| 11.9444 | 4300 | 0.0001 | 0.4140 |
| 12.0833 | 4350 | 0.0001 | 0.4145 |
| 12.2222 | 4400 | 0.0001 | 0.4145 |
| 12.3611 | 4450 | 0.0085 | 0.4135 |
| 12.5 | 4500 | 0.0001 | 0.4112 |
| 12.6389 | 4550 | 0.0001 | 0.4119 |
| 12.7778 | 4600 | 0.0001 | 0.4127 |
| 12.9167 | 4650 | 0.0001 | 0.4140 |
| 13.0556 | 4700 | 0.0001 | 0.4174 |
| 13.1944 | 4750 | 0.0001 | 0.4182 |
| 13.3333 | 4800 | 0.0001 | 0.4187 |
| 13.4722 | 4850 | 0.0051 | 0.4184 |
| 13.6111 | 4900 | 0.0001 | 0.4183 |
| 13.75 | 4950 | 0.0001 | 0.4190 |
| 13.8889 | 5000 | 0.0001 | 0.4195 |
| 14.0278 | 5050 | 0.0001 | 0.4199 |
| 14.1667 | 5100 | 0.0002 | 0.4177 |
| 14.3056 | 5150 | 0.0001 | 0.4177 |
| 14.4444 | 5200 | 0.0066 | 0.4153 |
| 14.5833 | 5250 | 0.0001 | 0.4155 |
| 14.7222 | 5300 | 0.0001 | 0.4155 |
| 14.8611 | 5350 | 0.0001 | 0.4155 |
| 15.0 | 5400 | 0.0001 | 0.4156 |
</details>
### Framework Versions
- Python: 3.12.6
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cpu
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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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