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neel2306
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gte-cp-base

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10932
loss:MultipleNegativesRankingLoss
text-embeddings-inference
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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:10932
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Medicinal And Botanical Chemicals, Drugs, And Other Products 
sentences:
- Alkyl benzene for surfactants
- Botanical extracts for supplements
- Industrial chemicals
- source_sentence: Ball And Roller Bearings
sentences:
- Bearing races
- Dishwashing liquid
- Bearing walls
- source_sentence: Scientific Time Keeping Device
sentences:
- Digital wristwatches
- Quartz crystals
- Natural rubber for tires
- source_sentence: Miscellaneous Electrical Industrial Apparatus
sentences:
- Consumer electronics
- Stainless steel hollow sections
- Industrial circuit breakers
- source_sentence: Mineral Fuels, Lubricants Etc.
sentences:
- Coal
- Logistics costs for machinery distribution
- 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/gte-cp-base")
# Run inference
sentences = [
'Mineral Fuels, Lubricants Etc.',
'Crude oil',
'Coal',
]
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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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10,932 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.91 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.05 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.08 tokens</li><li>max: 14 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------|:----------------------------------------|:-----------------------------------|
| <code>Clay Floor And Wall Tile, Glazed And Unglazed (Including Quarry Tile And Ceramic Mosaic Tile)</code> | <code>Ceramic mosaic tiles</code> | <code>Natural stone tiles</code> |
| <code>Electrical Relay/Conductor</code> | <code>Relay switches</code> | <code>Electrical insulators</code> |
| <code>Plasterer (Kelowna, British Columbia 5 13) (Union Rate)</code> | <code>Labor costs for plasterers</code> | <code>Painting supplies</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: 2,733 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 10.09 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.06 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.95 tokens</li><li>max: 14 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:---------------------------------------------|:------------------------------|
| <code>Asphalt Paving Mixture and Block Manufacturing</code> | <code>Recycled asphalt pavement (RAP)</code> | <code>Asphalt shingles</code> |
| <code>Air Conditioning Plant</code> | <code>Refrigerant gases</code> | <code>Heating elements</code> |
| <code>Oak Lumber</code> | <code>Oak plywood</code> | <code>Pine lumber</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
- `learning_rate`: 6e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `optim`: adamw_hf
- `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`: 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`: 6e-05
- `weight_decay`: 0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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_hf
- `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 |
|:------:|:----:|:-------------:|:------:|
| 0.0731 | 50 | 1.9026 | 1.5169 |
| 0.1462 | 100 | 1.5479 | 1.0813 |
| 0.2193 | 150 | 1.0239 | 0.7291 |
| 0.2924 | 200 | 0.6914 | 0.6372 |
| 0.3655 | 250 | 0.653 | 0.5887 |
| 0.4386 | 300 | 0.5469 | 0.5605 |
| 0.5117 | 350 | 0.5312 | 0.5408 |
| 0.5848 | 400 | 0.4996 | 0.5100 |
| 0.6579 | 450 | 0.4445 | 0.4830 |
| 0.7310 | 500 | 0.5092 | 0.4734 |
| 0.8041 | 550 | 0.532 | 0.4476 |
| 0.8772 | 600 | 0.4147 | 0.4714 |
| 0.9503 | 650 | 0.477 | 0.4400 |
| 1.0234 | 700 | 0.4243 | 0.4466 |
| 1.0965 | 750 | 0.485 | 0.4172 |
| 1.1696 | 800 | 0.3717 | 0.4271 |
| 1.2427 | 850 | 0.3716 | 0.4369 |
| 1.3158 | 900 | 0.3742 | 0.4104 |
| 1.3889 | 950 | 0.3157 | 0.4436 |
| 1.4620 | 1000 | 0.3035 | 0.4444 |
| 1.5351 | 1050 | 0.2797 | 0.4558 |
| 1.6082 | 1100 | 0.2639 | 0.4248 |
| 1.6813 | 1150 | 0.2286 | 0.4308 |
| 1.7544 | 1200 | 0.2753 | 0.4098 |
| 1.8275 | 1250 | 0.1904 | 0.4415 |
| 1.9006 | 1300 | 0.2175 | 0.4503 |
| 1.9737 | 1350 | 0.1806 | 0.4245 |
| 2.0468 | 1400 | 0.1826 | 0.4418 |
| 2.1199 | 1450 | 0.1952 | 0.4138 |
| 2.1930 | 1500 | 0.1612 | 0.4061 |
| 2.2661 | 1550 | 0.1604 | 0.3910 |
| 2.3392 | 1600 | 0.1199 | 0.3852 |
| 2.4123 | 1650 | 0.1439 | 0.4082 |
| 2.4854 | 1700 | 0.1402 | 0.4352 |
| 2.5585 | 1750 | 0.1116 | 0.4338 |
| 2.6316 | 1800 | 0.1113 | 0.4189 |
| 2.7047 | 1850 | 0.1159 | 0.4013 |
| 2.7778 | 1900 | 0.1241 | 0.3853 |
| 2.8509 | 1950 | 0.0977 | 0.3919 |
| 2.9240 | 2000 | 0.0953 | 0.4022 |
| 2.9971 | 2050 | 0.1159 | 0.4073 |
| 3.0702 | 2100 | 0.0923 | 0.3903 |
| 3.1433 | 2150 | 0.0958 | 0.3833 |
| 3.2164 | 2200 | 0.0787 | 0.3875 |
| 3.2895 | 2250 | 0.083 | 0.3807 |
| 3.3626 | 2300 | 0.0714 | 0.3806 |
| 3.4357 | 2350 | 0.0748 | 0.3997 |
| 3.5088 | 2400 | 0.0779 | 0.4027 |
| 3.5819 | 2450 | 0.0709 | 0.3921 |
| 3.6550 | 2500 | 0.0482 | 0.3905 |
| 3.7281 | 2550 | 0.0784 | 0.3760 |
| 3.8012 | 2600 | 0.0694 | 0.3809 |
| 3.8743 | 2650 | 0.0725 | 0.3957 |
| 3.9474 | 2700 | 0.0718 | 0.3897 |
| 4.0205 | 2750 | 0.05 | 0.3894 |
| 4.0936 | 2800 | 0.0597 | 0.4014 |
| 4.1667 | 2850 | 0.0445 | 0.3929 |
| 4.2398 | 2900 | 0.039 | 0.3856 |
| 4.3129 | 2950 | 0.0405 | 0.3723 |
| 4.3860 | 3000 | 0.0456 | 0.3764 |
| 4.4591 | 3050 | 0.0493 | 0.3876 |
| 4.5322 | 3100 | 0.036 | 0.3866 |
| 4.6053 | 3150 | 0.0517 | 0.3791 |
| 4.6784 | 3200 | 0.0383 | 0.3724 |
| 4.7515 | 3250 | 0.0453 | 0.3886 |
| 4.8246 | 3300 | 0.0469 | 0.3897 |
| 4.8977 | 3350 | 0.0385 | 0.3940 |
| 4.9708 | 3400 | 0.0427 | 0.3877 |
| 5.0439 | 3450 | 0.0212 | 0.3914 |
| 5.1170 | 3500 | 0.0452 | 0.3899 |
| 5.1901 | 3550 | 0.0252 | 0.3925 |
| 5.2632 | 3600 | 0.0228 | 0.3895 |
| 5.3363 | 3650 | 0.0219 | 0.3792 |
| 5.4094 | 3700 | 0.0275 | 0.3882 |
| 5.4825 | 3750 | 0.0246 | 0.3892 |
| 5.5556 | 3800 | 0.0226 | 0.3895 |
| 5.6287 | 3850 | 0.0219 | 0.3912 |
| 5.7018 | 3900 | 0.027 | 0.3800 |
| 5.7749 | 3950 | 0.0268 | 0.3667 |
| 5.8480 | 4000 | 0.0313 | 0.3687 |
| 5.9211 | 4050 | 0.0233 | 0.3675 |
| 5.9942 | 4100 | 0.0201 | 0.3649 |
| 6.0673 | 4150 | 0.0207 | 0.3727 |
| 6.1404 | 4200 | 0.0175 | 0.3802 |
| 6.2135 | 4250 | 0.0117 | 0.3760 |
| 6.2865 | 4300 | 0.0124 | 0.3731 |
| 6.3596 | 4350 | 0.0164 | 0.3713 |
| 6.4327 | 4400 | 0.0149 | 0.3782 |
| 6.5058 | 4450 | 0.0127 | 0.3747 |
| 6.5789 | 4500 | 0.013 | 0.3746 |
| 6.6520 | 4550 | 0.0078 | 0.3756 |
| 6.7251 | 4600 | 0.0171 | 0.3741 |
| 6.7982 | 4650 | 0.0211 | 0.3680 |
| 6.8713 | 4700 | 0.0186 | 0.3686 |
| 6.9444 | 4750 | 0.0213 | 0.3688 |
| 7.0175 | 4800 | 0.0107 | 0.3647 |
| 7.0906 | 4850 | 0.011 | 0.3677 |
| 7.1637 | 4900 | 0.0098 | 0.3671 |
| 7.2368 | 4950 | 0.0091 | 0.3708 |
| 7.3099 | 5000 | 0.0074 | 0.3673 |
| 7.3830 | 5050 | 0.0101 | 0.3672 |
| 7.4561 | 5100 | 0.0115 | 0.3676 |
| 7.5292 | 5150 | 0.0054 | 0.3656 |
| 7.6023 | 5200 | 0.0076 | 0.3657 |
| 7.6754 | 5250 | 0.0054 | 0.3639 |
| 7.7485 | 5300 | 0.0115 | 0.3600 |
| 7.8216 | 5350 | 0.0105 | 0.3657 |
| 7.8947 | 5400 | 0.0175 | 0.3649 |
| 7.9678 | 5450 | 0.0091 | 0.3634 |
| 8.0409 | 5500 | 0.0043 | 0.3646 |
| 8.1140 | 5550 | 0.0078 | 0.3650 |
| 8.1871 | 5600 | 0.004 | 0.3683 |
| 8.2602 | 5650 | 0.0045 | 0.3669 |
| 8.3333 | 5700 | 0.005 | 0.3661 |
| 8.4064 | 5750 | 0.0074 | 0.3652 |
| 8.4795 | 5800 | 0.0042 | 0.3662 |
| 8.5526 | 5850 | 0.0039 | 0.3696 |
| 8.6257 | 5900 | 0.004 | 0.3724 |
| 8.6988 | 5950 | 0.008 | 0.3714 |
| 8.7719 | 6000 | 0.0057 | 0.3711 |
| 8.8450 | 6050 | 0.0045 | 0.3702 |
| 8.9181 | 6100 | 0.0122 | 0.3715 |
| 8.9912 | 6150 | 0.0064 | 0.3703 |
| 9.0643 | 6200 | 0.0039 | 0.3689 |
| 9.1374 | 6250 | 0.0034 | 0.3680 |
| 9.2105 | 6300 | 0.0022 | 0.3680 |
| 9.2836 | 6350 | 0.0021 | 0.3684 |
| 9.3567 | 6400 | 0.0025 | 0.3685 |
| 9.4298 | 6450 | 0.0041 | 0.3679 |
| 9.5029 | 6500 | 0.0018 | 0.3679 |
| 9.5760 | 6550 | 0.0039 | 0.3686 |
| 9.6491 | 6600 | 0.0021 | 0.3691 |
| 9.7222 | 6650 | 0.0056 | 0.3689 |
| 9.7953 | 6700 | 0.0025 | 0.3691 |
| 9.8684 | 6750 | 0.0063 | 0.3692 |
| 9.9415 | 6800 | 0.0074 | 0.3692 |
</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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