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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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- dense |
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- generated_from_trainer |
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- dataset_size:3056 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: intfloat/e5-base-v2 |
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widget: |
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- source_sentence: Assess the strengths and weaknesses of initiatives aimed at growing |
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indigenous agribusiness. |
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sentences: |
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- 'In Chile, 80% of sources go to irrigation and agriculture, making irrigation |
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in agriculture a relevant element to consider. The main aspects associated with |
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irrigation in agriculture are salinity, toxicity, and microbiological quality |
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due to pathogenic organisms present in wastewater. When discussing irrigation, |
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the type of irrigation must be taken into account, as there are globally two types: |
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restrictive irrigation, which applies to products eaten raw, and irrigation without |
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restriction, which has no significant effects on agriculture, animals, or humans.' |
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- Mr Yoshiyuki Arima discussed the World Bank's focus on sustainable solutions to |
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challenges like climate change and gender equality. The World Bank is moving from |
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Green Bonds to Sustainable Development Bonds, using SDGs as a framework. They |
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are working with the Government Pension Investment Fund on research related to |
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SDGs. |
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- Technical and leadership development to grow indigenous agribusiness. Commercialisation |
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and access to market channels – both domestic and international – for indigenous |
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goods and services. Building networks to strengthen and increase participation |
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in the food system of indigenous people in the Asia Pacific region. |
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- source_sentence: What is the largest water-consuming sector in Australia's economy? |
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sentences: |
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- Navarrot holds a Minor in Sustainability Studies. |
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- Australia’s agricultural sector is the largest water consuming sector in the economy, |
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accounting for 65 percent of total consumption in 2005. In the Murray-Darling |
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Basin, climate change will lead to decreased water levels and difficulties meeting |
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demand for irrigation while maintaining environmental flows. Additionally, vegetation |
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will consume more water under higher temperatures. |
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- The project contributes to the implementation of the APEC Food Security Roadmap |
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Towards 2030, focusing on food production, processing, and distribution. It includes |
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targets such as improving food system related digital literacy, promoting public-private |
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investment, modernizing food storage facilities, and sharing best practices among |
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APEC economies. |
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- source_sentence: How would you use anaerobic digestion to reduce landfill reliance |
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in a city? |
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sentences: |
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- 'Innovation Approach: Technologies like anaerobic digestion and microbial transformation |
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create biogas and animal feed, turning waste into valuable resources and reducing |
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landfill reliance.' |
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- The initiative started from the previous satellite communication project that |
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ITU implemented in the Pacific. ITU provided 9 economies with 93 units of satellite |
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ground stations, so the remote islands were connected with the satellites. For |
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the islands, the satellites became essential communication means when disaster |
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hits the region. For instance, when the hurricane hit in 2020, the satellite ground |
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stations were the only communication means when the economies tried to initiate |
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the disaster response efforts during the Covid lockdown. Additionally, according |
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to ITU’s assessment, this communication means were used by communities and remote |
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and previously unconnected communities for education and health, and to provide |
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and receive government services. |
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- Mexico cited changes to the lengths of growing seasons, with increased temperatures |
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leading to shorter growing seasons in traditional agricultural areas as temperatures |
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become too extreme for both crops and livestock. |
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- source_sentence: What would happen if APEC economies failed to coordinate across |
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borders? |
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sentences: |
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- APEC economies must co-ordinate across borders to facilitate services. The greater |
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the coherence between industry and governments, the greater the likelihood of |
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success. |
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- Another key issue she made clear about the food systems was the transaction costs. |
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To unlock the potential of the food systems, the transaction costs issues should |
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be addressed. These transactions are all over the food systems. They are encouraged |
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by farmers, their business partners to find each other, make deals and ensure |
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that these deals are enforced. While the transactions being essential to the production |
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of goods, the costs following them drive farmers to choose quantity over quality |
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at the expense of the environment, which ultimately affect consumers product choices. |
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- '• Mortality risk: lack of real time data to react. |
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• Yield optimization: no proper water quality data for yield optimization.' |
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- source_sentence: Identify the main goal of closing resource loops. |
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sentences: |
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- Closing resource loops aims to create new value through the reuse and recycling |
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of used materials. |
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- Shelf life can be extended up to 18 month, would this violate the expiration date? |
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- Closing resource loops aims to create new value through the reuse and recycling |
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of used materials. |
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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_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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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 intfloat/e5-base-v2 |
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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: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7447643979057592 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8992146596858639 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.93717277486911 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9607329842931938 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7447643979057592 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.32504363001745196 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.20863874345549735 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10863874345549739 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6882635253054101 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8697643979057592 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9212478184991274 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9526614310645725 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.849824960377896 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.8267877919055926 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.8125610657293678 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on intfloat/e5-base-v2 |
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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. |
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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:** [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) <!-- at revision f52bf8ec8c7124536f0efb74aca902b2995e5bcd --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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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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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'}) |
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(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}) |
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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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'Identify the main goal of closing resource loops.', |
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'Closing resource loops aims to create new value through the reuse and recycling of used materials.', |
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'Shelf life can be extended up to 18 month, would this violate the expiration date?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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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) |
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# tensor([[1.0000, 0.8682, 0.4450], |
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# [0.8682, 1.0000, 0.4960], |
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# [0.4450, 0.4960, 1.0000]]) |
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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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* 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 | 0.7448 | |
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| cosine_accuracy@3 | 0.8992 | |
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| cosine_accuracy@5 | 0.9372 | |
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| cosine_accuracy@10 | 0.9607 | |
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| cosine_precision@1 | 0.7448 | |
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| cosine_precision@3 | 0.325 | |
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| cosine_precision@5 | 0.2086 | |
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| cosine_precision@10 | 0.1086 | |
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| cosine_recall@1 | 0.6883 | |
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| cosine_recall@3 | 0.8698 | |
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| cosine_recall@5 | 0.9212 | |
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| cosine_recall@10 | 0.9527 | |
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| **cosine_ndcg@10** | **0.8498** | |
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| cosine_mrr@10 | 0.8268 | |
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| cosine_map@100 | 0.8126 | |
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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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#### Unnamed Dataset |
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* Size: 3,056 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 17.94 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 82.66 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>How does the proximity of energy generation to consumption benefit floating solar plants?</code> | <code>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.</code> | |
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| <code>Who won the Chilean award for women entrepreneurs at the regional level?</code> | <code>Mrs Curumilla won the Chilean award for women entrepreneurs at the regional level.</code> | |
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| <code>How did the follow-up survey contribute to the establishment of working groups?</code> | <code>The answers and interventions collected from the survey helped establish the different working groups and address common challenges in the workshop.</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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384, |
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256 |
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], |
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"matryoshka_weights": [ |
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1.0, |
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0.8, |
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0.6, |
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0.4 |
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], |
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"n_dims_per_step": -1 |
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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`: 6 |
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- `per_device_eval_batch_size`: 6 |
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- `num_train_epochs`: 4 |
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- `multi_dataset_batch_sampler`: round_robin |
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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`: 6 |
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- `per_device_eval_batch_size`: 6 |
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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`: 0.0 |
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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 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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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`: False |
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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`: False |
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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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- `hub_revision`: None |
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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 |
|
|
- `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`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### 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} |
|
|
} |
|
|
``` |
|
|
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