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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5600
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: What is the main factor of signal interference in MCFs?
sentences:
- The main factor of signal interference in MCFs is crosstalk, which is the leakage
of a fraction of the signal power from a given core to its neighboring core.
- An integrity group temporal key (IGTK) is a random value used to protect group
addressed medium access control (MAC) management protocol data units (MMPDUs)
from a broadcast/multicast source station (STA).
- Wireless sensing through the combined use of radio wave and AI technologies aims
to identify objects and recognize actions with high precision.
- source_sentence: What types of drones can be used to construct multi-tier drone-cell
networks?
sentences:
- The coupling coefficient represents the tightness of coupling between transmit
and receive coils in wireless charging systems.
- A cheap, slow photodiode placed next to the rear face of the laser package is
commonly used as the monitor detector in laser drive circuits.
- Multi-tier drone-cell networks can be constructed by utilizing several drone types,
similar to terrestrial HetNets with macro-, small-, femtocells, and relays.
- source_sentence: Which technology was explored for high capacity last mile and pre-aggregation
backhaul in small cell networks?
sentences:
- According to Pearl's Ladder of Causation, counterfactual questions can only be
answered if information from all other levels (associational and interventional)
is available. Counterfactuals subsume interventional and associational questions,
and therefore sit at the top of the hierarchy.
- Shannon's classical source coding theorem provides the minimum distortion achievable
in encoding a Gaussian stationary input signal.
- The passage mentions that 60 GHz and 70-80 GHz millimeter wave communication technologies
were explored for high capacity last mile and pre-aggregation backhaul in small
cell networks.
- source_sentence: What is the main output of the design procedure for a passive lossless
Huygens metasurface?
sentences:
- Entanglement distillation is the process of purifying imperfect entangled states
to obtain maximally entangled states.
- The main output of the design procedure is the transmitted fields as well as the
surface impedance and admittance.
- The component of IoT responsible for sensing and collecting data is the sensors.
- source_sentence: What is the formula for the relative entropy between two probability
density functions?
sentences:
- The consequence of the fact that the total power radiated varies as the square
of the frequency of the oscillation is that shorter wavelength (higher frequency)
light is scattered much more strongly than longer wavelength (lower frequency)
light.
- Hybrid infrastructures are comprised of various proximate and distant computing
nodes, either mobile or immobile.
- The relative entropy between two probability density functions f and g is equal
to the negative integral of f(x) multiplied by the logarithm of the ratio of f(x)
and g(x), with respect to x.
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_recall@1
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: telecom ir eval
type: telecom-ir-eval
metrics:
- type: cosine_accuracy@1
value: 0.9733333333333334
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.995
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.995
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.995
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9733333333333334
name: Cosine Precision@1
- type: cosine_recall@1
value: 0.9733333333333334
name: Cosine Recall@1
- type: cosine_ndcg@10
value: 0.985912396714286
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9827777777777778
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9831452173557438
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the csv dataset. 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- csv
<!-- - **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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'What is the formula for the relative entropy between two probability density functions?',
'The relative entropy between two probability density functions f and g is equal to the negative integral of f(x) multiplied by the logarithm of the ratio of f(x) and g(x), with respect to x.',
'The consequence of the fact that the total power radiated varies as the square of the frequency of the oscillation is that shorter wavelength (higher frequency) light is scattered much more strongly than longer wavelength (lower frequency) light.',
]
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]
```
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `telecom-ir-eval`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy@1 | 0.9733 |
| cosine_accuracy@3 | 0.995 |
| cosine_accuracy@5 | 0.995 |
| cosine_accuracy@10 | 0.995 |
| cosine_precision@1 | 0.9733 |
| cosine_recall@1 | 0.9733 |
| **cosine_ndcg@10** | **0.9859** |
| cosine_mrr@10 | 0.9828 |
| cosine_map@100 | 0.9831 |
<!--
## 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
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## Training Details
### Training Dataset
#### csv
* Dataset: csv
* Size: 5,600 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: 4 tokens</li><li>mean: 18.48 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.0 tokens</li><li>max: 85 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>How can the unique decodability of a code be tested using the Sardinas and Patterson test?</code> | <code>The Sardinas and Patterson test for unique decodability involves checking if no codewords are prefixes of any other codewords.</code> |
| <code>What is the purpose of encapsulation in the OSI (Open System Interconnection) model?</code> | <code>Encapsulation is used to add control information and transform data units into protocol data units.</code> |
| <code>What advantages do measurements from user equipment (UE) have over drive tests in disaster small cell networks?</code> | <code>Measurements from user equipment (UE) have the advantages of reduced labor intensity, measurements obtained from additional locations, such as inside buildings, and better adaptation to specific characteristics and requirements in disaster scenarios.</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
#### csv
* Dataset: csv
* Size: 1,400 evaluation 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: 4 tokens</li><li>mean: 18.92 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.0 tokens</li><li>max: 96 tokens</li></ul> |
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the three major steps in SLAM-based techniques for THz localization?</code> | <code>SLAM-based techniques for THz localization involve imaging the environment, estimating ranges to the user, and fusing the images with the estimated ranges.</code> |
| <code>What is the service time distribution in the M/M(X)/1 model?</code> | <code>In the M/M(X)/1 model, the service time distribution is exponential with parameter µ.</code> |
| <code>What is the main advantage of the ensemble patch method in generating adversarial patches?</code> | <code>The main advantage of the ensemble patch method is that it achieves a higher attack success rate compared to single patches.</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`: 128
- `per_device_eval_batch_size`: 128
- `weight_decay`: 0.01
- `num_train_epochs`: 5
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 0.01
- `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`: cosine_with_restarts
- `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`: 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`: True
- `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`: 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
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | telecom-ir-eval_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:---------------:|:------------------------------:|
| 1.1364 | 50 | 0.2567 | 0.0419 | 0.9844 |
| **2.2727** | **100** | **0.0502** | **0.0397** | **0.9859** |
| 3.4091 | 150 | 0.0277 | 0.0399 | 0.9846 |
| 4.5455 | 200 | 0.0231 | 0.0406 | 0.9840 |
| 5.0 | 220 | - | - | 0.9859 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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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