--- 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) - **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) ### 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `telecom-ir-eval` * Evaluated with [InformationRetrievalEvaluator](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 | ## 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: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is multi-user multiple input, multiple output (MU-MIMO) in IEEE 802.11-2020? | 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. | | What is the purpose of wireless network virtualization? | 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. | | What is the E2E (end-to-end) latency requirement for factory automation applications? | Factory automation applications require an E2E latency of 0.25-10 ms. | * Loss: [MultipleNegativesRankingLoss](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: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which standard enables building Digital Twins of different Physical Twins using combinations of XML (eXtensible Markup Language) and C codes? | The functional mockup interface (FMI) is a standard that enables building Digital Twins of different Physical Twins using combinations of XML and C codes. | | What algorithm is commonly used for digital signatures in S/MIME? | RSA is commonly used for digital signatures in S/MIME. | | What are the three modes of operation based on the communication range and the SA (subarray) separation? | 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. | * Loss: [MultipleNegativesRankingLoss](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
Click to expand - `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
### 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} } ```