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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6552 |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-small-en-v1.5 |
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widget: |
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- source_sentence: What problem can reconfigurable intelligent surfaces mitigate in |
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light fidelity systems? |
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sentences: |
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- The document mentions that blind channel estimation requires a large number of |
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data symbols to improve accuracy, which may not be feasible in practice. |
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- Empirical evidence suggests that the power decay can even be exponential with |
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distance. |
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- Reconfigurable intelligent surface-enabled environments can enhance light fidelity |
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coverage by mitigating the dead-zone problem for users at the edge of the cell, |
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improving link quality. |
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- source_sentence: What is the advantage of conformal arrays in UAV (Unmanned Aerial |
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Vehicle) communication systems? |
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sentences: |
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- Overfitting occurs when a model fits the training data too well and fails to generalize |
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to unseen data, while underfitting occurs when a model does not fit the training |
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data well enough to capture the underlying patterns. |
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- A point-to-multipoint service is a service type in which data is sent to all service |
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subscribers or a pre-defined subset of all subscribers within an area defined |
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by the Service Requester. |
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- Conformal arrays offer good aerodynamic performance, enable full-space beam scanning, |
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and provide more DoFs for geometry design. |
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- source_sentence: What is a Virtual Home Environment? |
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sentences: |
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- Compressive spectrum sensing utilizes the sparsity property of signals to enable |
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sub-Nyquist sampling. |
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- A Virtual Home Environment is a concept that allows for the portability of personal |
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service environments across network boundaries and between terminals. |
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- In the Client Server model, a Client application waits passively on contact while |
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a Server starts the communication actively. |
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- source_sentence: What is multi-agent RL (Reinforcement learning) concerned with? |
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sentences: |
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- Data centers account for about 1% of global electricity demand, as stated in the |
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document. |
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- Fog Computing and Communication in the Frugal 5G network architecture brings intelligence |
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to the edge and enables more efficient communication with reduced resource usage. |
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- Multi-agent RL is concerned with learning in presence of multiple agents and encompasses |
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unique problem formulation that draws from game theoretical concepts. |
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- source_sentence: What is the trade-off between privacy and convergence performance |
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when using artificial noise obscuring in federated learning? |
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sentences: |
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- The 'decrypt_error' alert indicates a handshake cryptographic operation failed, |
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including being unable to verify a signature, decrypt a key exchange, or validate |
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a finished message. |
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- The trade-off between privacy and convergence performance when using artificial |
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noise obscuring in federated learning is that increasing the noise variance improves |
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privacy but degrades convergence. |
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- The design rules for sub-carrier allocations to users in cellular systems are |
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to allocate the sub-carriers as spread out as possible and hop the sub-carriers |
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every OFDM symbol time. |
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datasets: |
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- dinho1597/Telecom-QA-MultipleChoice |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_recall@1 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-small-en-v1.5 |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: telecom ir eval |
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type: telecom-ir-eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.9679633867276888 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.9916094584286804 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9916094584286804 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.992372234935164 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.9679633867276888 |
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name: Cosine Precision@1 |
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- type: cosine_recall@1 |
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value: 0.9679633867276888 |
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name: Cosine Recall@1 |
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- type: cosine_ndcg@10 |
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value: 0.9823240649953693 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.9788647342995168 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.9791402442094453 |
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name: Cosine Map@100 |
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--- |
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|
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# SentenceTransformer based on BAAI/bge-small-en-v1.5 |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [telecom-qa-multiple_choice](https://huggingface.co/datasets/dinho1597/Telecom-QA-MultipleChoice) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'What is the trade-off between privacy and convergence performance when using artificial noise obscuring in federated learning?', |
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'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.', |
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"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.", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Dataset: `telecom-ir-eval` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:-------------------|:-----------| |
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| cosine_accuracy@1 | 0.968 | |
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| cosine_accuracy@3 | 0.9916 | |
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| cosine_accuracy@5 | 0.9916 | |
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| cosine_accuracy@10 | 0.9924 | |
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| cosine_precision@1 | 0.968 | |
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| cosine_recall@1 | 0.968 | |
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| **cosine_ndcg@10** | **0.9823** | |
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| cosine_mrr@10 | 0.9789 | |
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| cosine_map@100 | 0.9791 | |
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<!-- |
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## Bias, Risks and Limitations |
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*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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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### telecom-qa-multiple_choice |
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* 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) |
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* Size: 6,552 training samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | |
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|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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|
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### Evaluation Dataset |
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|
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#### telecom-qa-multiple_choice |
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* 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) |
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* Size: 6,552 evaluation samples |
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* Columns: <code>anchor</code> and <code>positive</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | |
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|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| 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> | |
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* Samples: |
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| anchor | positive | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 10 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 256 |
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- `per_device_eval_batch_size`: 256 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.01 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine_with_restarts |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | telecom-ir-eval_cosine_ndcg@10 | |
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|:------:|:----:|:-------------:|:---------------:|:------------------------------:| |
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| 0.7143 | 15 | 0.824 | 0.1333 | 0.9701 | |
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| 1.3810 | 30 | 0.1731 | 0.0759 | 0.9776 | |
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| 2.0476 | 45 | 0.0917 | 0.0657 | 0.9807 | |
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| 2.7619 | 60 | 0.0676 | 0.0609 | 0.9813 | |
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| 3.4286 | 75 | 0.0435 | 0.0596 | 0.9818 | |
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| 4.0952 | 90 | 0.038 | 0.0606 | 0.9814 | |
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| 4.8095 | 105 | 0.0332 | 0.0594 | 0.9820 | |
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| 5.4762 | 120 | 0.0269 | 0.0607 | 0.9817 | |
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| 6.1429 | 135 | 0.0219 | 0.0600 | 0.9819 | |
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| 6.8571 | 150 | 0.0244 | 0.0599 | 0.9823 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.47.1 |
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- PyTorch: 2.5.1+cu121 |
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- Accelerate: 1.2.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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|
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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