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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4122
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Environment Minister Greg Hunt the Coalition's emissions reduction
fund, at $13.95 per tonne of carbon, is around 1 per cent of the cost of reducing
carbon under the former Labor government's carbon pricing scheme, which he cost
$1,300 a tonne.
sentences:
- Sirius's heliacal rising, just before the start of the Nile flood, gave Sopdet
a close connection with the flood and the resulting growth of plants.
- The proposal would have set an emissions price of NZ$15 per tonne of CO2-equivalent.
- '"More recently, evaporation over lakes has steadily been increasing, largely
due to increases in water surface temperature," Gronewold said.'
- source_sentence: “In 2013 the level of U.S. farm output was about 2.7 times its
1948 level, and productivity was growing at an average annual rate of 1.52%.
sentences:
- As the concentration of carbon dioxide increases in the atmosphere, the increased
uptake of carbon dioxide into the oceans is causing a measurable decrease in the
pH of the oceans, which is referred to as ocean acidification.
- The IPCC was tasked with reviewing peer-reviewed scientific literature and other
relevant publications to provide information on the state of knowledge about climate
change.
- Private sector productivity growth, measured as real output per hour of all persons,
increased at an average rate of 1.9% during Reagan's eight years, compared to
an average 1.3% during the preceding eight years.
- source_sentence: '''Phil Jones said that for the past 15 years there has been no
"statistically significant" warming.'
sentences:
- From this, he concluded that "The post-1980 global warming trend from surface
thermometers is not credible.
- Fox News has widely been described as a major platform for climate change denial.
- In comparison to the extended record, the sea-ice extent in the polar region by
September 2007 was only half the recorded mass that had been estimated to exist
within the 1950–1970 period.
- source_sentence: '"NASA satellite data from the years 2000 through 2011 show the
Earth''s atmosphere is allowing far more heat to be released into space than alarmist
computer models have predicted, reports a new study in the peer-reviewed science
journal Remote Sensing.'
sentences:
- The Lamont–Doherty Earth Observatory at Columbia University is one of the world's
leading research centers developing fundamental knowledge about the origin, evolution
and future of the natural world.
- Mann said, "Ten years ago, the availability of data became quite sparse by the
time you got back to 1,000 AD, and what we had then was weighted towards tree-ring
data; but now you can go back 1,300 years without using tree-ring data at all
and still get a verifiable conclusion."
- This premature announcement came from a preliminary news release about a study
which had not yet been peer reviewed.
- source_sentence: '...there [is] anecdotal and other evidence suggesting similar
melts from 1938-43 and on other occasions.'
sentences:
- They were formed by the melting of sulfur deposits at temperatures as low as 113 °C
(235 °F).
- For example, in the study of the origin of the earth, one can reasonably model
earth's mass, temperature, and rate of rotation, as a function of time allowing
one to extrapolate forward or backward in time and so predict future or prior
events.
- Consequently, summers are 2.3 °C (4 °F) warmer in the Northern Hemisphere than
in the Southern Hemisphere under similar conditions.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: claims dev
type: claims-dev
metrics:
- type: cosine_accuracy@1
value: 0.24025974025974026
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44155844155844154
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5454545454545454
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6818181818181818
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24025974025974026
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19047619047619044
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15454545454545457
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10714285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.09577922077922078
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.21482683982683978
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.27532467532467536
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.36612554112554113
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2932326612195408
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3742553081838797
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23004915088757852
name: Cosine Map@100
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
<!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
(1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("jmroth/my-awesome-model")
# Run inference
sentences = [
'...there [is] anecdotal and other evidence suggesting similar melts from 1938-43 and on other occasions.',
'They were formed by the melting of sulfur deposits at temperatures as low as 113\xa0°C (235\xa0°F).',
'Consequently, summers are 2.3\xa0°C (4\xa0°F) warmer in the Northern Hemisphere than in the Southern Hemisphere under similar conditions.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.4966, 0.1535],
# [0.4966, 1.0000, 0.3254],
# [0.1535, 0.3254, 1.0000]])
```
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### 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
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `claims-dev`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2403 |
| cosine_accuracy@3 | 0.4416 |
| cosine_accuracy@5 | 0.5455 |
| cosine_accuracy@10 | 0.6818 |
| cosine_precision@1 | 0.2403 |
| cosine_precision@3 | 0.1905 |
| cosine_precision@5 | 0.1545 |
| cosine_precision@10 | 0.1071 |
| cosine_recall@1 | 0.0958 |
| cosine_recall@3 | 0.2148 |
| cosine_recall@5 | 0.2753 |
| cosine_recall@10 | 0.3661 |
| **cosine_ndcg@10** | **0.2932** |
| cosine_mrr@10 | 0.3743 |
| cosine_map@100 | 0.23 |
<!--
## 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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### Recommendations
*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: 4,122 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 26.75 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 38.71 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>At very high concentrations (100 times atmospheric concentration, or greater), carbon dioxide can be toxic to animal life, so raising the concentration to 10,000 ppm (1%) or higher for several hours will eliminate pests such as whiteflies and spider mites in a greenhouse.</code> |
| <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>Plants can grow as much as 50 percent faster in concentrations of 1,000 ppm CO 2 when compared with ambient conditions, though this assumes no change in climate and no limitation on other nutrients.</code> |
| <code>Not only is there no scientific evidence that CO2 is a pollutant, higher CO2 concentrations actually help ecosystems support more plant and animal life.</code> | <code>Higher carbon dioxide concentrations will favourably affect plant growth and demand for water.</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,
"directions": [
"query_to_doc"
],
"partition_mode": "joint",
"hardness_mode": null,
"hardness_strength": 0.0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `warmup_steps`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: jmroth/nlp-biencoder-finetuned
- `hub_strategy`: end
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0.1
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: True
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `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
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: jmroth/nlp-biencoder-finetuned
- `hub_strategy`: end
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `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`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | claims-dev_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:-------------------------:|
| 0.0775 | 10 | 1.4212 | - |
| 0.1550 | 20 | 1.4229 | - |
| 0.2326 | 30 | 1.1129 | - |
| 0.3101 | 40 | 0.9966 | - |
| 0.3876 | 50 | 0.9207 | 0.2829 |
| 0.4651 | 60 | 0.8326 | - |
| 0.5426 | 70 | 0.8989 | - |
| 0.6202 | 80 | 0.9630 | - |
| 0.6977 | 90 | 0.8394 | - |
| 0.7752 | 100 | 0.8764 | 0.2893 |
| 0.8527 | 110 | 0.8208 | - |
| 0.9302 | 120 | 0.7684 | - |
| 1.0078 | 130 | 0.7049 | - |
| 1.0853 | 140 | 0.7378 | - |
| 1.1628 | 150 | 0.6265 | 0.2941 |
| 1.2403 | 160 | 0.6832 | - |
| 1.3178 | 170 | 0.6365 | - |
| 1.3953 | 180 | 0.5991 | - |
| 1.4729 | 190 | 0.5456 | - |
| **1.5504** | **200** | **0.6355** | **0.2943** |
| 1.6279 | 210 | 0.5927 | - |
| 1.7054 | 220 | 0.7117 | - |
| 1.7829 | 230 | 0.5096 | - |
| 1.8605 | 240 | 0.6036 | - |
| 1.9380 | 250 | 0.6768 | 0.2896 |
| 2.0155 | 260 | 0.6589 | - |
| 2.0930 | 270 | 0.5436 | - |
| 2.1705 | 280 | 0.5173 | - |
| 2.2481 | 290 | 0.5544 | - |
| 2.3256 | 300 | 0.5583 | 0.2911 |
| 2.4031 | 310 | 0.5903 | - |
| 2.4806 | 320 | 0.5265 | - |
| 2.5581 | 330 | 0.5107 | - |
| 2.6357 | 340 | 0.6144 | - |
| 2.7132 | 350 | 0.5175 | 0.2932 |
| 2.7907 | 360 | 0.5805 | - |
| 2.8682 | 370 | 0.5299 | - |
| 2.9457 | 380 | 0.5621 | - |
* The bold row denotes the saved checkpoint.
### Training Time
- **Training**: 32.6 minutes
### Framework Versions
- Python: 3.12.13
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- 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{oord2019representationlearningcontrastivepredictive,
title={Representation Learning with Contrastive Predictive Coding},
author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
year={2019},
eprint={1807.03748},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1807.03748},
}
```
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