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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:42280
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: How is demand from blown film converters trending for natural-colour
rLDPE pellets sourced from production scrap in Germany?
sentences:
- For a tender closing Friday, market participants indicated post-industrial, food-grade
HDPE bales could be workable around €1,030-1,110/t DAP Valencia for prompt-to-March
delivery, depending on lot size and delivery flexibility.
- Demand from German blown-film converters for natural rLDPE pellets sourced from
production scrap was steady to slightly firmer week on week, though buyers continued
to push back on offers above the low-to-mid €1,200s/t FCA level.
- Europe recycled high-density polyethylene (R-HDPE) blow-moulding natural pellet
demand continues to increase on the back of new packaging projects and increased
recycled content use from the packaging sector.
- source_sentence: What is the current premium for clean PI white HDPE bales over
mixed-colour PI HDPE bales on an FCA Germany basis?
sentences:
- Nevertheless, this is not yet considered representative of the bulk of material,
or seen as achievable for high density polyethylene (HDPE) dominated bales, which
continue to see a top end of €300/tonne ex-works.
- UK supply of white rHDPE blow moulding pellets is described as comfortable to
long, as recyclers are running close to normal rates while demand from blow moulders
remains subdued, keeping prompt availability open.
- Clean post-industrial white HDPE bales are currently at a €40-70/t premium to
mixed-colour PI HDPE bales on an FCA Germany basis, with the spread widest where
contamination guarantees are contractually enforced.
- source_sentence: Are you hearing more quality claims (gels, black specks, odour)
on natural transparent flexible rLDPE pellets in the current spot market?
sentences:
- ICIS assessed natural rLDPE pellets produced from post-consumer LDPE film at €1,140-1,230/tonne
delivered FD Northwest Europe in the week to 21 February, up €10/tonne week on
week on tighter prompt availability.
- How does the quality specification of rLDPE Pellet Flexible Natural Translucent
affect its pricing in the spot market?
- Spot market participants reported more frequent quality claims on natural transparent
flexible rLDPE pellets—mainly gels and sporadic black specks—leading to discounts
or load rejections in the €20-50/t range.
- source_sentence: For blown film extrusion, what MFI (190°C/2.16 kg) range is most
commonly traded for recycled LDPE pellet, flexible, natural transparent?
sentences:
- Demand is strongest for natural transparent pellet, which is for material with
an MFI of 1.0 and higher, which gives a good transparency, low gels and low contamination,
and is suitable for stretch film applications.
- For post-consumer bulky rigid HDPE bales, typical buyer specs cap moisture at
10%, paper/labels at 5%, metals at 0.5% and non-HD plastics at 5%, with PVC expected
to be near-zero (often ≤0.2%) to avoid wash-line issues.
- For blown film applications, recycled LDPE flexible natural/transparent pellets
are most commonly traded at around 0.3-0.8 g/10min MFI (190°C/2.16kg), with some
grades offered up to about 1.0 g/10min for easier processing.
- source_sentence: Any new import inspections, permits, or customs delays affecting
inbound post-consumer black HDPE bales into Vietnam this quarter?
sentences:
- Market sources said Vietnam has maintained tighter inspection rates and documentation
checks on inbound post-consumer black HDPE bales this quarter, extending customs
clearance to roughly 10-15 working days in some cases, but without a major new
permit requirement.
- Deals and discussions for spot imports of HDPE have taken place at $960-970/tonne
CFR (cost & freight) Vietnam, compared with the $1,030-1,050/tonne CFR Vietnam
assessment range in the week ended 15 September.
- Container freight indications from Ningbo to Hamburg were heard at roughly $2,000-2,300
per 40ft this week (about $80-100/t), adding to CIF Europe ideas for imported
white rHDPE blow moulding pellets even as netbacks stayed steady.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). 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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **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/huggingface/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': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(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): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): 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("Netizine/icis_commodity_embedding")
# Run inference
queries = [
"Any new import inspections, permits, or customs delays affecting inbound post-consumer black HDPE bales into Vietnam this quarter?",
]
documents = [
'Market sources said Vietnam has maintained tighter inspection rates and documentation checks on inbound post-consumer black HDPE bales this quarter, extending customs clearance to roughly 10-15 working days in some cases, but without a major new permit requirement.',
'Deals and discussions for spot imports of HDPE have taken place at $960-970/tonne CFR (cost & freight) Vietnam, compared with the $1,030-1,050/tonne CFR Vietnam assessment range in the week ended 15 September.',
'Container freight indications from Ningbo to Hamburg were heard at roughly $2,000-2,300 per 40ft this week (about $80-100/t), adding to CIF Europe ideas for imported white rHDPE blow moulding pellets even as netbacks stayed steady.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9667, 0.0020, 0.0885]])
```
<!--
### 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>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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.*
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 42,280 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: 18 tokens</li><li>mean: 30.35 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 57.04 tokens</li><li>max: 255 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 33.34 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Can you give the latest ICIS range for rLDPE pellets, post-consumer, colourless, FCA Rotterdam/Antwerp?</code> | <code>ICIS assessed post-consumer colourless rLDPE pellets at €1,070-1,170/t FCA Rotterdam/Antwerp in the week to 21 February 2026.</code> | <code>ICIS began pricing R-HDPE natural blow-moulding pellets in May 2020.</code> |
| <code>What are traders paying this week for PCR LDPE colourless pellets (post-consumer) delivered to Lombardy, Italy (DDP)?</code> | <code>This week, post-consumer colourless PCR LDPE pellet business was heard around €1,180-1,280/t DDP Lombardy, with better-filtered, low-odour lots at the top of the range.</code> | <code>Colourless (C) polyethylene terephthalate (PET) post-consumer bottle bale prices have increased in Italy in the latest monthly auction, adding upwards pressure to both recycled PET (R-PET) C flake and food-grade pellet (FGP) prices in the country, which reflects a common theme for January being felt across the wider European market.</code> |
| <code>How did the weekly assessment for post-consumer colourless rLDPE pellets in NWE change versus last week?</code> | <code>The ICIS weekly assessment for post-consumer colourless rLDPE pellets FCA Rotterdam/Antwerp fell by €20/t week on week to €1,070-1,170/t as buyers resisted higher offers.</code> | <code>Consumption of flexible R-LDPE pellets has increased in April in NWE, although this remains counterbalanced by high stock levels.</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",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `prompts`: {'anchor': 'task: search result | query: ', 'positive': 'title: none | text: ', 'negative': 'title: none | text: '}
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `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`: 5
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: {'anchor': 'task: search result | query: ', 'positive': 'title: none | text: ', 'negative': 'title: none | text: '}
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0378 | 100 | 0.0336 |
| 0.0757 | 200 | 0.0013 |
| 0.1135 | 300 | 0.0009 |
| 0.1513 | 400 | 0.0015 |
| 0.1892 | 500 | 0.0019 |
| 0.2270 | 600 | 0.0013 |
| 0.2649 | 700 | 0.0034 |
| 0.3027 | 800 | 0.0046 |
| 0.3405 | 900 | 0.0007 |
| 0.3784 | 1000 | 0.0009 |
| 0.4162 | 1100 | 0.0022 |
| 0.4540 | 1200 | 0.0107 |
| 0.4919 | 1300 | 0.0081 |
| 0.5297 | 1400 | 0.0111 |
| 0.5675 | 1500 | 0.0052 |
| 0.6054 | 1600 | 0.0013 |
| 0.6432 | 1700 | 0.0108 |
| 0.6810 | 1800 | 0.0055 |
| 0.7189 | 1900 | 0.0042 |
| 0.7567 | 2000 | 0.0056 |
| 0.7946 | 2100 | 0.0034 |
| 0.8324 | 2200 | 0.0051 |
| 0.8702 | 2300 | 0.0021 |
| 0.9081 | 2400 | 0.0022 |
| 0.9459 | 2500 | 0.0089 |
| 0.9837 | 2600 | 0.0036 |
| 1.0216 | 2700 | 0.0013 |
| 1.0594 | 2800 | 0.0019 |
| 1.0972 | 2900 | 0.0015 |
| 1.1351 | 3000 | 0.0008 |
| 1.1729 | 3100 | 0.001 |
| 1.2107 | 3200 | 0.003 |
| 1.2486 | 3300 | 0.0013 |
| 1.2864 | 3400 | 0.0017 |
| 1.3243 | 3500 | 0.0008 |
| 1.3621 | 3600 | 0.0004 |
| 1.3999 | 3700 | 0.0012 |
| 1.4378 | 3800 | 0.0022 |
| 1.4756 | 3900 | 0.0032 |
| 1.5134 | 4000 | 0.0011 |
| 1.5513 | 4100 | 0.0016 |
| 1.5891 | 4200 | 0.0014 |
| 1.6269 | 4300 | 0.0024 |
| 1.6648 | 4400 | 0.0038 |
| 1.7026 | 4500 | 0.0015 |
| 1.7404 | 4600 | 0.0008 |
| 1.7783 | 4700 | 0.001 |
| 1.8161 | 4800 | 0.0006 |
| 1.8540 | 4900 | 0.0011 |
| 1.8918 | 5000 | 0.001 |
| 1.9296 | 5100 | 0.001 |
| 1.9675 | 5200 | 0.0013 |
| 2.0053 | 5300 | 0.0011 |
| 2.0431 | 5400 | 0.0005 |
| 2.0810 | 5500 | 0.0004 |
| 2.1188 | 5600 | 0.0009 |
| 2.1566 | 5700 | 0.001 |
| 2.1945 | 5800 | 0.0005 |
| 2.2323 | 5900 | 0.0012 |
| 2.2701 | 6000 | 0.0024 |
| 2.3080 | 6100 | 0.0006 |
| 2.3458 | 6200 | 0.0002 |
| 2.3837 | 6300 | 0.0005 |
| 2.4215 | 6400 | 0.0003 |
| 2.4593 | 6500 | 0.0004 |
| 2.4972 | 6600 | 0.0003 |
| 2.5350 | 6700 | 0.0006 |
| 2.5728 | 6800 | 0.0005 |
| 2.6107 | 6900 | 0.0005 |
| 2.6485 | 7000 | 0.0004 |
| 2.6863 | 7100 | 0.0007 |
| 2.7242 | 7200 | 0.0005 |
| 2.7620 | 7300 | 0.0003 |
| 2.7998 | 7400 | 0.0005 |
| 2.8377 | 7500 | 0.0007 |
| 2.8755 | 7600 | 0.0009 |
| 2.9134 | 7700 | 0.0002 |
| 2.9512 | 7800 | 0.0001 |
| 2.9890 | 7900 | 0.0012 |
| 3.0269 | 8000 | 0.0004 |
| 3.0647 | 8100 | 0.0014 |
| 3.1025 | 8200 | 0.0003 |
| 3.1404 | 8300 | 0.0004 |
| 3.1782 | 8400 | 0.0003 |
| 3.2160 | 8500 | 0.0002 |
| 3.2539 | 8600 | 0.0003 |
| 3.2917 | 8700 | 0.0002 |
| 3.3295 | 8800 | 0.0001 |
| 3.3674 | 8900 | 0.0003 |
| 3.4052 | 9000 | 0.0002 |
| 3.4431 | 9100 | 0.0001 |
| 3.4809 | 9200 | 0.0001 |
| 3.5187 | 9300 | 0.0003 |
| 3.5566 | 9400 | 0.0006 |
| 3.5944 | 9500 | 0.0002 |
| 3.6322 | 9600 | 0.0001 |
| 3.6701 | 9700 | 0.0002 |
| 3.7079 | 9800 | 0.0007 |
| 3.7457 | 9900 | 0.0001 |
| 3.7836 | 10000 | 0.0001 |
| 3.8214 | 10100 | 0.0002 |
| 3.8593 | 10200 | 0.0003 |
| 3.8971 | 10300 | 0.0001 |
| 3.9349 | 10400 | 0.0002 |
| 3.9728 | 10500 | 0.0001 |
| 4.0106 | 10600 | 0.0002 |
| 4.0484 | 10700 | 0.0002 |
| 4.0863 | 10800 | 0.0001 |
| 4.1241 | 10900 | 0.0003 |
| 4.1619 | 11000 | 0.0001 |
| 4.1998 | 11100 | 0.0001 |
| 4.2376 | 11200 | 0.0002 |
| 4.2754 | 11300 | 0.0004 |
| 4.3133 | 11400 | 0.0003 |
| 4.3511 | 11500 | 0.0001 |
| 4.3890 | 11600 | 0.0001 |
| 4.4268 | 11700 | 0.0001 |
| 4.4646 | 11800 | 0.0001 |
| 4.5025 | 11900 | 0.0005 |
| 4.5403 | 12000 | 0.0004 |
| 4.5781 | 12100 | 0.0001 |
| 4.6160 | 12200 | 0.0002 |
| 4.6538 | 12300 | 0.0001 |
| 4.6916 | 12400 | 0.0001 |
| 4.7295 | 12500 | 0.0001 |
| 4.7673 | 12600 | 0.0001 |
| 4.8051 | 12700 | 0.0 |
| 4.8430 | 12800 | 0.0001 |
| 4.8808 | 12900 | 0.0007 |
| 4.9187 | 13000 | 0.0001 |
| 4.9565 | 13100 | 0.0008 |
| 4.9943 | 13200 | 0.0001 |
</details>
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.3
- Transformers: 4.57.0.dev0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.12.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
## 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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