| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:52 |
| - loss:CosineSimilarityLoss |
| widget: |
| - source_sentence: Students will be able to analyse and evaluate moderate or severe |
| food insecurity in the population in the context of end hunger, achieve food security |
| and improved nutrition and promote sustainable agriculture |
| sentences: |
| - 'SDG 2 - Zero Hunger: End hunger, achieve food security and improved nutrition |
| and promote sustainable agriculture. Target 2.1: By 2030, end hunger and ensure |
| access by all people to safe, nutritious and sufficient food all year round. Indicator |
| 2.1.2: Prevalence of moderate or severe food insecurity in the population' |
| - 'SDG 3 - Good Health and Well-Being: Ensure healthy lives and promote well-being |
| for all at all ages. Target 3.8: Achieve universal health coverage, including |
| financial risk protection, access to quality essential health-care services and |
| access to safe effective medicines and vaccines for all. Indicator 3.8.1: Coverage |
| of essential health services' |
| - 'SDG 17 - Partnerships for the Goals: Strengthen the means of implementation and |
| revitalize the Global Partnership for Sustainable Development. Target 17.8: Fully |
| operationalize the technology bank and science technology and innovation capacity-building |
| mechanism for least developed countries. Indicator 17.8.1: Proportion of individuals |
| using the Internet' |
| - source_sentence: Students will apply nutrition science principles to design diet |
| plans for maternal and child health |
| sentences: |
| - 'SDG 6 - Clean Water and Sanitation: Ensure availability and sustainable management |
| of water and sanitation for all. Target 6.2: By 2030, achieve access to adequate |
| and equitable sanitation and hygiene for all and end open defecation. Indicator |
| 6.2.1: Proportion of population using safely managed sanitation services and a |
| hand-washing facility with soap and water' |
| - 'SDG 3 - Good Health and Well-Being: Ensure healthy lives and promote well-being |
| for all at all ages. Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, |
| malaria and neglected tropical diseases and combat hepatitis and other communicable |
| diseases. Indicator 3.3.1: Number of new HIV infections per 1,000 uninfected population' |
| - 'SDG 5 - Gender Equality: Achieve gender equality and empower all women and girls. |
| Target 5.1: End all forms of discrimination against all women and girls everywhere. |
| Indicator 5.1.1: Whether or not legal frameworks are in place to promote enforce |
| and monitor equality and non-discrimination on the basis of sex' |
| - source_sentence: Students will be able to analyse and evaluate direct economic loss |
| attributed to disasters in relation to global gross domestic product in the context |
| of end poverty in all its forms everywhere |
| sentences: |
| - 'SDG 15 - Life on Land: Protect restore and promote sustainable use of terrestrial |
| ecosystems sustainably manage forests combat desertification and halt biodiversity |
| loss. Target 15.5: Take urgent and significant action to reduce the degradation |
| of natural habitats halt the loss of biodiversity and protect and prevent the |
| extinction of threatened species. Indicator 15.5.1: Red List Index' |
| - 'SDG 15 - Life on Land: Protect restore and promote sustainable use of terrestrial |
| ecosystems sustainably manage forests combat desertification and halt biodiversity |
| loss. Target 15.1: By 2020, ensure the conservation restoration and sustainable |
| use of terrestrial and inland freshwater ecosystems including forests wetlands |
| mountains and drylands. Indicator 15.1.2: Proportion of important sites for terrestrial |
| and freshwater biodiversity that are covered by protected areas' |
| - 'SDG 4 - Quality Education: Ensure inclusive and equitable quality education and |
| promote lifelong learning opportunities for all. Target 4.1: By 2030, ensure that |
| all girls and boys complete free equitable and quality primary and secondary education |
| leading to relevant and effective learning outcomes. Indicator 4.1.1: Proportion |
| of children and young people achieving at least a minimum proficiency level in |
| reading and mathematics' |
| - source_sentence: Students will evaluate the effectiveness of renewable energy policies |
| in reducing carbon emissions |
| sentences: |
| - 'SDG 3 - Good Health and Well-Being: Ensure healthy lives and promote well-being |
| for all at all ages. Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, |
| malaria and neglected tropical diseases and combat hepatitis and other communicable |
| diseases. Indicator 3.3.3: Malaria incidence per 1,000 population' |
| - 'SDG 4 - Quality Education: Ensure inclusive and equitable quality education and |
| promote lifelong learning opportunities for all. Target 4.1: By 2030, ensure that |
| all girls and boys complete free equitable and quality primary and secondary education |
| leading to relevant and effective learning outcomes. Indicator 4.1.2: Completion |
| rate in primary education, lower secondary education, upper secondary education' |
| - 'SDG 13 - Climate Action: Take urgent action to combat climate change and its |
| impacts. Target 13.2: Integrate climate change measures into national policies |
| strategies and planning. Indicator 13.2.1: Number of countries with nationally |
| determined contributions long-term strategies national adaptation plans' |
| - source_sentence: Students will be able to analyse and evaluate red list index in |
| the context of protect restore and promote sustainable use of terrestrial ecosystems |
| sustainably manage forests combat desertification and halt biodiversity loss |
| sentences: |
| - 'SDG 8 - Decent Work and Economic Growth: Promote sustained, inclusive and sustainable |
| economic growth, full and productive employment and decent work for all. Target |
| 8.8: Protect labour rights and promote safe and secure working environments for |
| all workers including migrant workers. Indicator 8.8.2: Level of national compliance |
| with labour rights including freedom of association and collective bargaining' |
| - 'SDG 15 - Life on Land: Protect restore and promote sustainable use of terrestrial |
| ecosystems sustainably manage forests combat desertification and halt biodiversity |
| loss. Target 15.1: By 2020, ensure the conservation restoration and sustainable |
| use of terrestrial and inland freshwater ecosystems including forests wetlands |
| mountains and drylands. Indicator 15.1.1: Forest area as a proportion of total |
| land area' |
| - 'SDG 5 - Gender Equality: Achieve gender equality and empower all women and girls. |
| Target 5.b: Enhance the use of enabling technology in particular information and |
| communications technology to promote the empowerment of women. Indicator 5.b.1: |
| Proportion of individuals who own a mobile telephone, by sex' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 384 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'}) |
| (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'Students will be able to analyse and evaluate red list index in the context of protect restore and promote sustainable use of terrestrial ecosystems sustainably manage forests combat desertification and halt biodiversity loss', |
| 'SDG 5 - Gender Equality: Achieve gender equality and empower all women and girls. Target 5.b: Enhance the use of enabling technology in particular information and communications technology to promote the empowerment of women. Indicator 5.b.1: Proportion of individuals who own a mobile telephone, by sex', |
| 'SDG 15 - Life on Land: Protect restore and promote sustainable use of terrestrial ecosystems sustainably manage forests combat desertification and halt biodiversity loss. Target 15.1: By 2020, ensure the conservation restoration and sustainable use of terrestrial and inland freshwater ecosystems including forests wetlands mountains and drylands. Indicator 15.1.1: Forest area as a proportion of total land area', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[ 1.0000, -0.0038, 0.6881], |
| # [-0.0038, 1.0000, 0.0142], |
| # [ 0.6881, 0.0142, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 52 training samples |
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| * Approximate statistics based on the first 52 samples: |
| | | sentence_0 | sentence_1 | label | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 15 tokens</li><li>mean: 32.27 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 71.71 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | <code>Students will be able to analyse and evaluate informal employment in total employment, by sector and sex in the context of promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all</code> | <code>SDG 8 - Decent Work and Economic Growth: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation. Indicator 8.3.1: Proportion of informal employment in total employment, by sector and sex</code> | <code>1.0</code> | |
| | <code>Students will design sustainable urban drainage systems to manage stormwater in cities</code> | <code>SDG 11 - Sustainable Cities and Communities: Make cities and human settlements inclusive, safe, resilient and sustainable. Target 11.2: By 2030, provide access to safe affordable accessible and sustainable transport systems for all improving road safety notably by expanding public transport. Indicator 11.2.1: Proportion of population that has convenient access to public transport</code> | <code>1.0</code> | |
| | <code>Students will evaluate the effectiveness of renewable energy policies in reducing carbon emissions</code> | <code>SDG 13 - Climate Action: Take urgent action to combat climate change and its impacts. Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries. Indicator 13.1.2: Number of countries that adopt and implement national disaster risk reduction strategies</code> | <code>1.0</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| ```json |
| { |
| "loss_fct": "torch.nn.modules.loss.MSELoss" |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `num_train_epochs`: 2 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 8 |
| - `per_device_eval_batch_size`: 8 |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 5e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1 |
| - `num_train_epochs`: 2 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: None |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `enable_jit_checkpoint`: False |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `use_cpu`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `bf16`: False |
| - `fp16`: False |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: -1 |
| - `ddp_backend`: None |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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 |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `use_cache`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.3 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.2 |
|
|
| ## 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *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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