--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:3056 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: intfloat/e5-base-v2 widget: - source_sentence: Assess the strengths and weaknesses of initiatives aimed at growing indigenous agribusiness. sentences: - 'In Chile, 80% of sources go to irrigation and agriculture, making irrigation in agriculture a relevant element to consider. The main aspects associated with irrigation in agriculture are salinity, toxicity, and microbiological quality due to pathogenic organisms present in wastewater. When discussing irrigation, the type of irrigation must be taken into account, as there are globally two types: restrictive irrigation, which applies to products eaten raw, and irrigation without restriction, which has no significant effects on agriculture, animals, or humans.' - Mr Yoshiyuki Arima discussed the World Bank's focus on sustainable solutions to challenges like climate change and gender equality. The World Bank is moving from Green Bonds to Sustainable Development Bonds, using SDGs as a framework. They are working with the Government Pension Investment Fund on research related to SDGs. - Technical and leadership development to grow indigenous agribusiness. Commercialisation and access to market channels – both domestic and international – for indigenous goods and services. Building networks to strengthen and increase participation in the food system of indigenous people in the Asia Pacific region. - source_sentence: What is the largest water-consuming sector in Australia's economy? sentences: - Navarrot holds a Minor in Sustainability Studies. - Australia’s agricultural sector is the largest water consuming sector in the economy, accounting for 65 percent of total consumption in 2005. In the Murray-Darling Basin, climate change will lead to decreased water levels and difficulties meeting demand for irrigation while maintaining environmental flows. Additionally, vegetation will consume more water under higher temperatures. - The project contributes to the implementation of the APEC Food Security Roadmap Towards 2030, focusing on food production, processing, and distribution. It includes targets such as improving food system related digital literacy, promoting public-private investment, modernizing food storage facilities, and sharing best practices among APEC economies. - source_sentence: How would you use anaerobic digestion to reduce landfill reliance in a city? sentences: - 'Innovation Approach: Technologies like anaerobic digestion and microbial transformation create biogas and animal feed, turning waste into valuable resources and reducing landfill reliance.' - The initiative started from the previous satellite communication project that ITU implemented in the Pacific. ITU provided 9 economies with 93 units of satellite ground stations, so the remote islands were connected with the satellites. For the islands, the satellites became essential communication means when disaster hits the region. For instance, when the hurricane hit in 2020, the satellite ground stations were the only communication means when the economies tried to initiate the disaster response efforts during the Covid lockdown. Additionally, according to ITU’s assessment, this communication means were used by communities and remote and previously unconnected communities for education and health, and to provide and receive government services. - Mexico cited changes to the lengths of growing seasons, with increased temperatures leading to shorter growing seasons in traditional agricultural areas as temperatures become too extreme for both crops and livestock. - source_sentence: What would happen if APEC economies failed to coordinate across borders? sentences: - APEC economies must co-ordinate across borders to facilitate services. The greater the coherence between industry and governments, the greater the likelihood of success. - Another key issue she made clear about the food systems was the transaction costs. To unlock the potential of the food systems, the transaction costs issues should be addressed. These transactions are all over the food systems. They are encouraged by farmers, their business partners to find each other, make deals and ensure that these deals are enforced. While the transactions being essential to the production of goods, the costs following them drive farmers to choose quantity over quality at the expense of the environment, which ultimately affect consumers product choices. - '• Mortality risk: lack of real time data to react. • Yield optimization: no proper water quality data for yield optimization.' - source_sentence: Identify the main goal of closing resource loops. sentences: - Closing resource loops aims to create new value through the reuse and recycling of used materials. - Shelf life can be extended up to 18 month, would this violate the expiration date? - Closing resource loops aims to create new value through the reuse and recycling of used materials. 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_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on intfloat/e5-base-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7447643979057592 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8992146596858639 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.93717277486911 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9607329842931938 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7447643979057592 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32504363001745196 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20863874345549735 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10863874345549739 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6882635253054101 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8697643979057592 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9212478184991274 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9526614310645725 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.849824960377896 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8267877919055926 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8125610657293678 name: Cosine Map@100 --- # SentenceTransformer based on intfloat/e5-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). 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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### 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': False, 'architecture': 'BertModel'}) (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 = [ 'Identify the main goal of closing resource loops.', 'Closing resource loops aims to create new value through the reuse and recycling of used materials.', 'Shelf life can be extended up to 18 month, would this violate the expiration date?', ] 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, 0.8682, 0.4450], # [0.8682, 1.0000, 0.4960], # [0.4450, 0.4960, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7448 | | cosine_accuracy@3 | 0.8992 | | cosine_accuracy@5 | 0.9372 | | cosine_accuracy@10 | 0.9607 | | cosine_precision@1 | 0.7448 | | cosine_precision@3 | 0.325 | | cosine_precision@5 | 0.2086 | | cosine_precision@10 | 0.1086 | | cosine_recall@1 | 0.6883 | | cosine_recall@3 | 0.8698 | | cosine_recall@5 | 0.9212 | | cosine_recall@10 | 0.9527 | | **cosine_ndcg@10** | **0.8498** | | cosine_mrr@10 | 0.8268 | | cosine_map@100 | 0.8126 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 3,056 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How does the proximity of energy generation to consumption benefit floating solar plants? | What are the benefits of using a floating solar plant? At first, the interest is the use solar energy to generate electricity. The performance peak of solar panels is at 25 degrees Celcius, anything above generates a performance loss of 0.4%. Thus, when using water as a cooling system, the photovoltaic panel stays close to 25 degrees. Another aspect to consider is the point of energy consumption, which is close to the generation point. | | Who won the Chilean award for women entrepreneurs at the regional level? | Mrs Curumilla won the Chilean award for women entrepreneurs at the regional level. | | How did the follow-up survey contribute to the establishment of working groups? | The answers and interventions collected from the survey helped establish the different working groups and address common challenges in the workshop. | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 384, 256 ], "matryoshka_weights": [ 1.0, 0.8, 0.6, 0.4 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 4 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `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`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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`: False - `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 - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | cosine_ndcg@10 | |:------:|:----:|:--------------:| | 0.7812 | 100 | 0.7980 | | 1.0 | 128 | 0.8078 | | 1.5625 | 200 | 0.8259 | | 2.0 | 256 | 0.8463 | | 2.3438 | 300 | 0.8446 | | 3.0 | 384 | 0.8483 | | 3.125 | 400 | 0.8498 | ### Framework Versions - Python: 3.10.18 - Sentence Transformers: 5.0.0 - Transformers: 4.53.1 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 2.14.0 - Tokenizers: 0.21.2 ## 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### 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} } ```