README.md

---

tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2542
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: How does climate change influence the hydrological cycle and streamflow
    patterns in river basins?
  sentences:
  - 'Answer: B) The protection of the water environment is essential.'
  - 'Answer: B) Changes in climatic forces and land use/land cover (LULC) changes

    characterized by re-vegetation'
  - 'Answer: B) Climate change can lead to increased temperatures and altered precipitation

    patterns, affecting streamflow variability.'
- source_sentence: Downscaling methods are only necessary for correcting temperature
    data in climate change impact studies.
  sentences:
  - 'Answer: B) Climate projections and hydrological modeling uncertainties are both

    important in predicting future urban streamflow and flood risks.'
  - 'False'
  - 'False'
- source_sentence: What are the primary challenges faced in monitoring flow and sediment
    dynamics in mountain river environments?
  sentences:
  - 'False'
  - 'Answer: B) Complex environments, rapid hydrological changes, and limited monitoring

    infrastructure'
  - 'True'
- source_sentence: The total annual water storage in the Shashe catchment is approximately
    44,000 Mm3, with groundwater being the dominant storage type.
  sentences:
  - 'True'
  - 'False'
  - 'True'
- source_sentence: Flood risks in the Yellow River Basin are projected to decrease
    under all climate change scenarios.
  sentences:
  - 'True'
  - 'False'
  - 'False'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---


# 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 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-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
<!-- - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel 

  (1): Pooling({'word_embedding_dimension': 384, '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("HydroEmbed/HydroEmbed-MCQTF-MiniLM-MNRL")

# Run inference

sentences = [

    'Flood risks in the Yellow River Basin are projected to decrease under all climate change scenarios.',

    'False',

    'True',

]

embeddings = model.encode(sentences)

print(embeddings.shape)

# [3, 384]



# Get the similarity scores for the embeddings

similarities = model.similarity(embeddings, embeddings)

print(similarities.shape)

# [3, 3]

```

<!--
### 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>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## 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: 2,542 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 9 tokens</li><li>mean: 22.77 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.98 tokens</li><li>max: 38 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                               | sentence_1                                                                                                          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|
  | <code>How does the concept of virtual water transfer contribute to enhancing water security in water-scarce regions?</code>                              | <code>Answer: A) By allowing for the export of water resources to other regions, reducing local consumption.</code> |
  | <code>Groundwater abstraction from various depths in multiple aquifer layers does not lead to significant changes in hydraulic head distribution.</code> | <code>False</code>                                                                                                  |
  | <code>What is the relationship between human intervention and hydrological processes?</code>                                                             | <code>Answer: B) Almost all processes can be manipulated in some way.</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

- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 20
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin



#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 20
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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}

- `tp_size`: 0
- `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`: None

- `hub_always_push`: False

- `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
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler

- `multi_dataset_batch_sampler`: round_robin



</details>



### Training Logs

| Epoch | Step | Training Loss |

|:-----:|:----:|:-------------:|

| 6.25  | 500  | 1.9917        |

| 12.5  | 1000 | 1.3359        |

| 18.75 | 1500 | 1.2406        |





### Framework Versions

- Python: 3.11.1

- Sentence Transformers: 4.1.0

- Transformers: 4.51.3

- PyTorch: 2.7.0+cu118

- Accelerate: 1.6.0

- Datasets: 3.5.1

- Tokenizers: 0.21.1



## 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}

}

```



<!--

## Glossary



*Clearly define terms in order to be accessible across audiences.*

-->



<!--

## Model Card Authors



*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*

-->



<!--

## Model Card Contact



*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*

-->