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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6552
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: What problem can reconfigurable intelligent surfaces mitigate in
light fidelity systems?
sentences:
- The document mentions that blind channel estimation requires a large number of
data symbols to improve accuracy, which may not be feasible in practice.
- Empirical evidence suggests that the power decay can even be exponential with
distance.
- Reconfigurable intelligent surface-enabled environments can enhance light fidelity
coverage by mitigating the dead-zone problem for users at the edge of the cell,
improving link quality.
- source_sentence: What is the advantage of conformal arrays in UAV (Unmanned Aerial
Vehicle) communication systems?
sentences:
- Overfitting occurs when a model fits the training data too well and fails to generalize
to unseen data, while underfitting occurs when a model does not fit the training
data well enough to capture the underlying patterns.
- A point-to-multipoint service is a service type in which data is sent to all service
subscribers or a pre-defined subset of all subscribers within an area defined
by the Service Requester.
- Conformal arrays offer good aerodynamic performance, enable full-space beam scanning,
and provide more DoFs for geometry design.
- source_sentence: What is a Virtual Home Environment?
sentences:
- Compressive spectrum sensing utilizes the sparsity property of signals to enable
sub-Nyquist sampling.
- A Virtual Home Environment is a concept that allows for the portability of personal
service environments across network boundaries and between terminals.
- In the Client Server model, a Client application waits passively on contact while
a Server starts the communication actively.
- source_sentence: What is multi-agent RL (Reinforcement learning) concerned with?
sentences:
- Data centers account for about 1% of global electricity demand, as stated in the
document.
- Fog Computing and Communication in the Frugal 5G network architecture brings intelligence
to the edge and enables more efficient communication with reduced resource usage.
- Multi-agent RL is concerned with learning in presence of multiple agents and encompasses
unique problem formulation that draws from game theoretical concepts.
- source_sentence: What is the trade-off between privacy and convergence performance
when using artificial noise obscuring in federated learning?
sentences:
- The 'decrypt_error' alert indicates a handshake cryptographic operation failed,
including being unable to verify a signature, decrypt a key exchange, or validate
a finished message.
- The trade-off between privacy and convergence performance when using artificial
noise obscuring in federated learning is that increasing the noise variance improves
privacy but degrades convergence.
- The design rules for sub-carrier allocations to users in cellular systems are
to allocate the sub-carriers as spread out as possible and hop the sub-carriers
every OFDM symbol time.
datasets:
- dinho1597/Telecom-QA-MultipleChoice
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: 0.9679633867276888
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9916094584286804
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9916094584286804
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.992372234935164
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9679633867276888
name: Cosine Precision@1
- type: cosine_recall@1
value: 0.9679633867276888
name: Cosine Recall@1
- type: cosine_ndcg@10
value: 0.9823240649953693
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9788647342995168
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9791402442094453
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 [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) 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:**
- [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice)
<!-- - **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 trade-off between privacy and convergence performance when using artificial noise obscuring in federated learning?',
'The trade-off between privacy and convergence performance when using artificial noise obscuring in federated learning is that increasing the noise variance improves privacy but degrades convergence.',
"The 'decrypt_error' alert indicates a handshake cryptographic operation failed, including being unable to verify a signature, decrypt a key exchange, or validate a finished message.",
]
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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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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 | 0.968 |
| cosine_accuracy@3 | 0.9916 |
| cosine_accuracy@5 | 0.9916 |
| cosine_accuracy@10 | 0.9924 |
| cosine_precision@1 | 0.968 |
| cosine_recall@1 | 0.968 |
| **cosine_ndcg@10** | **0.9823** |
| cosine_mrr@10 | 0.9789 |
| cosine_map@100 | 0.9791 |
<!--
## 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
#### telecom-qa-multiple_choice
* Dataset: [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) at [73aebbb](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice/tree/73aebbb16651212e4b1947ac0d64fc80a6bc9398)
* Size: 6,552 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: 18.8 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 29.27 tokens</li><li>max: 92 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is multi-user multiple input, multiple output (MU-MIMO) in IEEE 802.11-2020?</code> | <code>MU-MIMO is a technique by which multiple stations (STAs) either simultaneously transmit to a single STA or simultaneously receive from a single STA independent data streams over the same radio frequencies.</code> |
| <code>What is the purpose of wireless network virtualization?</code> | <code>The purpose of wireless network virtualization is to improve resource utilization, support diverse services/use cases, and be cost-effective and flexible for new services.</code> |
| <code>What is the E2E (end-to-end) latency requirement for factory automation applications?</code> | <code>Factory automation applications require an E2E latency of 0.25-10 ms.</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
#### telecom-qa-multiple_choice
* Dataset: [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) at [73aebbb](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice/tree/73aebbb16651212e4b1947ac0d64fc80a6bc9398)
* Size: 6,552 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: 18.5 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.83 tokens</li><li>max: 85 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which standard enables building Digital Twins of different Physical Twins using combinations of XML (eXtensible Markup Language) and C codes?</code> | <code>The functional mockup interface (FMI) is a standard that enables building Digital Twins of different Physical Twins using combinations of XML and C codes.</code> |
| <code>What algorithm is commonly used for digital signatures in S/MIME?</code> | <code>RSA is commonly used for digital signatures in S/MIME.</code> |
| <code>What are the three modes of operation based on the communication range and the SA (subarray) separation?</code> | <code>The three modes of operation based on the communication range and the SA separation are: (1) a mode where the channel paths are independent and the channel is always well-conditioned, (2) a mode where the channel is ill-conditioned, and (3) a mode where the channel is highly correlated.</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`: 256
- `per_device_eval_batch_size`: 256
- `weight_decay`: 0.01
- `num_train_epochs`: 10
- `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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 10
- `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 |
|:------:|:----:|:-------------:|:---------------:|:------------------------------:|
| 0.7143 | 15 | 0.824 | 0.1333 | 0.9701 |
| 1.3810 | 30 | 0.1731 | 0.0759 | 0.9776 |
| 2.0476 | 45 | 0.0917 | 0.0657 | 0.9807 |
| 2.7619 | 60 | 0.0676 | 0.0609 | 0.9813 |
| 3.4286 | 75 | 0.0435 | 0.0596 | 0.9818 |
| 4.0952 | 90 | 0.038 | 0.0606 | 0.9814 |
| 4.8095 | 105 | 0.0332 | 0.0594 | 0.9820 |
| 5.4762 | 120 | 0.0269 | 0.0607 | 0.9817 |
| 6.1429 | 135 | 0.0219 | 0.0600 | 0.9819 |
| 6.8571 | 150 | 0.0244 | 0.0599 | 0.9823 |
### 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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