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--- |
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base_model: BAAI/bge-small-en-v1.5 |
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datasets: [] |
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language: [] |
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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@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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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@5 |
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- cosine_recall@10 |
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- cosine_ndcg@5 |
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- cosine_ndcg@10 |
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- cosine_ndcg@100 |
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- cosine_mrr@5 |
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- cosine_mrr@10 |
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- cosine_mrr@100 |
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- cosine_map@100 |
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- dot_accuracy@1 |
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- dot_accuracy@5 |
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- dot_accuracy@10 |
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- dot_precision@1 |
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- dot_precision@5 |
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- dot_precision@10 |
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- dot_recall@1 |
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- dot_recall@5 |
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- dot_recall@10 |
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- dot_ndcg@5 |
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- dot_ndcg@10 |
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- dot_ndcg@100 |
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- dot_mrr@5 |
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- dot_mrr@10 |
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- dot_mrr@100 |
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- dot_map@100 |
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pipeline_tag: sentence-similarity |
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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:7033 |
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- loss:GISTEmbedLoss |
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widget: |
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- source_sentence: How will the performance of CBBOs be assessed in the third and |
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fourth year? |
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sentences: |
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- ''' (iv) In third and fourth year, performance of the CBBOs will be assessed based |
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on - (a) issuing Share Certificates to each member in third year, if any; (b) |
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audited Financial Statements for FPOs for second year and third year in due time |
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and filing as required; (c) MoU and vendor registration as per Business Plan with |
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Marketing Agencies/Institutional Buyers; (d) trading/uploading of produce in e-NAM/other |
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sources, if any; (e) second tranche equity grant to FPOs, if any; and (f) second |
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tranche of credit guarantee facility, if any . (v) In the fifth year, performance |
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of the CBBOs will be assessed based on (a) audited Statements of accounts of |
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FPO and filing it; (b) 100% of agri-business plan executed and value chain developed; (c) |
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revenue model showing financial growth in last 3 consecutive years; (d) detailed |
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project completion Report; and (e) third tranche of credit guarantee facility |
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if any.''' |
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- '''5. Tussock caterpillar, Notolopus (=Orygyia) postica , Lymantriidae, Lepidoptera |
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Symptom of damage: Defoliation. Nature of damage: Caterpillars of the moth feed |
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on the leaves. Egg: Eggs are laid in clusters on the leaves and covered over with |
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hairs. Larva: Caterpillars are gregarious in young stages. Full grown larva possess |
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a brown head, a pair of long pencil of hairs projecting forwardly from the prothorax, |
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yellowish tuft of hairs arising from the lateral side of the first two abdominal |
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segment and long brownish hairs arising from 8 th abdominal segment. Pupa: Pupation |
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takes place in silken cocoon. Adult: Small adult with yellowish brown wings. |
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Female moth is wingless. Presence of bipectinate antenna.''' |
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- '''The Kisan Credit Card (KCC) scheme was introduced in 1998 for issue of Kisan |
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Credit Cards to farmers on the basis of their holdings for uniform adoption by |
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the banks so that farmers may use them to readily purchase agriculture inputs |
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such as seeds, fertilizers, pesticides etc. and draw cash for their production |
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needs. The scheme was further extended for the investment credit requirement of |
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farmers viz. allied and non-farm activities in the year 2004. The scheme was further |
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revisited in 2012 by a working Group under the Chairmanship of Shri T. M. Bhasin, |
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CMD, Indian Bank with a view to simplify the scheme and facilitate issue of Electronic |
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Kisan Credit Cards. The scheme provides broad guidelines to banks for operationalizing |
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the KCC scheme. Implementing banks will have the discretion to adopt the same |
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to suit institution/location specific requirements.''' |
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- source_sentence: How should State Government disclose ceiling premium rate for a |
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crop in the tender document? |
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sentences: |
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- '''However, in absence of insured area of last year/season for all proposed crops |
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or any crop, net sown area of that crop(s) will be considered for calculation |
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of weighted premium of district. This data will be used for calculation of L1 |
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only. 7.1.5 Bidding **shall be done through e-tendering** and work order may |
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be released within 2 weeks of the opening of the Tender. 7.1.6 Depending on |
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the risk profile, historical loss cost and cost benefit analysis for the proposed |
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crop(s) in district(s) of any cluster, if the State Government feels that the |
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premium rate likely to be offered by bidding Insurance Companies would be abnormally |
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high, then the State Govt. can fix a ceiling on premium rates for such crop(s) |
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proposed to be included in the bidding evaluation for the bidding period. However, |
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recourse to this ceiling provision may be done only in well justified cases and |
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not as a general practice. The ceiling premium rate may be derived based on statistical |
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evaluation/actuarial premium analysis, loss cost, historical payout etc and name |
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of such crop should be disclosed by State Govt. compulsorily in the tender document. 7.1.7 In |
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such cases where a ceiling has been indicated, State government must call financial |
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bids in two step bidding or in two separate envelopes. First bid/envelop is for |
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disclosing the premium rate offered by each participating Insurance Company for |
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such ceiling crops and must be categorised under \''Ceiling Premium Rate\'' and 2nd bid |
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envelop is for bidding of crop wise premium rate for all crops included in tender. |
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Time interval for opening of both bid/envelop should be compulsorily mentioned |
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in the bidding documents and should preferably be on the same day. All participating |
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Insurance Companies have to submit the bid offer as per the procedure mentioned |
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above. 7.1.8 State Govt.''' |
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- '''| Chapters | Particulars | |
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Page No. |\n|---------------|------------------------------------------------------------|-------------|\n| |
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1 | Concept of Producer Organisation | 1 |\n| |
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2 | Producer Organisation Registered as Cooperative Society | 15 |\n| |
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3 | Producer Organisation Registered as Producer Company | 19 |\n| |
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4 | Producer Organisation Registered as Non-Profit Society | 33 |\n| |
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5 | Producer Organisation Registered as Trust | 36 |\n| |
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6 | Producer Organisation Registered as Section 8 Company | 39 |\n| |
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7 | Business Planning | 42 |\n| |
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8 | Financial Management | 55 |\n| |
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9 | Funding Arrangement | 60 |\n| |
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10 | Monitoring by the PO, POPI and Funding Agencies | 80 |\n| |
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Attachment | | |\n| |
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1 | Producer Company Act provisions | |\n| |
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2 | PRODUCE Fund Operational Guidelines | 106 |\n| |
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3 | SFAC Circular on Promoting / supporting Producer Companies | 114 |\n| |
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4 | Case Study on Bilaspur Model of PO | 125 |\n| |
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5 | Indicative Framework of the process of forming a PO | 131 |\n| |
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6 | References | 138 |\n| |
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7 | Memorandum of Agreement between NABARD and POPI | 139 |\n| |
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8 | Memorandum of Understanding between NABARD and RSA | 143 |\n| |
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9 | | |\n| |
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Abbreviations | | |\n| | | |\n| |
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146 | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |\n| | | |''' |
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- '''Agro-industries generate residues like husk, hull, shell, peel, testa, skin, |
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fibre, bran, linter, stone, seed, cob, prawn, head, frog legs, low grade fish, |
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leather waste, hair, bones, coir dust, saw dust, bamboo dust, etc. which could |
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be recycled or used efficiently through agro-processing centres. In the last three |
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decades, rice and sugarcane residues have increased by 162 and 172 %, respectively. |
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Their disposal problem needs serious rethinking (Vimal, 1981). To some extent |
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these organic residues are used as soil conditioner, animal feed, fuel, thatching |
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and packing materials. These can also be put to new uses for manufacture of various |
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chemicals and specific products (like silica, alcohol, tannins, glue, gelatine, |
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wax, etc), feed, pharmaceuticals (Iycogenin, antibiotics, vitamins, etc.), fertilizers, |
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energy, construction materials, paper pulp, handicraft materials etc. Residues |
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from fruit and vegetable industries, fish and marine industries and slaughter |
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o straw decrease their efficiency without pretreatment.''' |
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- source_sentence: What is the purpose of using pectolytic enzymes in fruit juice |
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processing? |
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sentences: |
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- '''Aggregating producers into collectives is one of the best mechanism to improve |
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access of small producers to investment, technology and market. The facilitating |
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agency should however keep the following factors in view: a. Types of small |
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scale producers in the target area, volume of production, socioeconomic status, |
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marketing arrangement b. Sufficient demand in the existing market to absorb the |
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additional production without significantly affecting the prices c. Willingness |
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of producers to invest and adopt new technology, if identified, to increase productivity |
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or quality of produce d. Challenges in the market chain and market environment |
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e. Vulnerability of the market to shocks, trends and seasonality f. Previous |
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experience of collective action (of any kind) in the community g. Key commodities, |
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processed products or semi-finished goods demanded by major retailers or processing |
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companies in the surrounding areas/districts h. Support from Government Departments, |
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NGOs, specialist support agencies and private companies for enterprise development i. |
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Incentives for members (also disincentives) for joining the PO Keeping in view |
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the sustainability of a Producer Organisation, a flow chart of activities along |
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with timeline, verifiable indicators and risk factors is provided at Attachment-5.''' |
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- '''2. Sampling method to be adopted – Random Size of the card including area for |
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label and other details = 20 x 30 cmm = 600 cm 2 No. of Grids = 30 Area of each |
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grid = 7 x 2 cm = 14 cm 2 Total No. of eggs / cm 2 to be accommodated = 96,000 |
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– 1,08,000 Mean number of egg / cm 2 of the card in the grid area excluding area |
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for labeling = 200 – 250 Number of counts/ card of size 20 x 30 cm to be taken |
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No. of parasitised eggs = 12 • 3-4 days old parasitised egg card has to be selected |
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for examination • count the number of eggs and eggs parasitised in an area by |
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1 cm 2 • Per card of size 20 x 30 cm count randomly in 12 positions • Repeat the |
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process for three different cards of same age • Express the per cent parasitisation |
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. The result should fall in range of 85-90 per cent.''' |
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- '''Pectins are colloidal in nature, making solutions viscous and holding other |
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materials in suspension. Pectinesterase removes methyl groups from the pectin |
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molecules exposing carboxyl groups which in the presence of bi- or multivalent |
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cations, such as calcium, form insoluble salts which can readily be removed. At |
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the same time, polygalacturonase degrades macromolecular pectin, causing reduction |
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in viscosity and destroying the protective colloidal action so that suspended |
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materials will settle out. Extensive use of pectolytic enzymes is made in processing |
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fruit juices. Addition of pectic enzymes to grapes or other fruits during crushing |
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or grinding results in increased yields of juice on pressing. Wine from grapes |
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so treated will usually clear faster when fermentation is complete, and have better |
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color.''' |
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- source_sentence: What is the purpose of the PM-Kisan Portal? |
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sentences: |
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- ''' 2) In case of cultivable land in the State of Nagaland which is categorised |
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as Jhum land as per definition under Section–2(7) of the Nagaland Jhum Land Act, |
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1970 and which is owned by the community/clan/village council/village chieftan, |
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the identification of beneficiaries under PM-Kisan scheme, shall be on the basis |
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of certification of land holding by the village council/chief/head of the village, |
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duly verified by the administrative head of the circle/sub division and countersigned |
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by the Deputy Commissioner of the District. Provided that the name of the beneficiary |
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is included in the state of Nagaland''s Agriculture Census of 2015-16. This proviso |
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shall not be applicable in cases of succession and family partition. The list |
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of such beneficiaries shall be subject to the exclusions under the operational |
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guidelines. 5.6 For identification of *bona fide* beneficiary under PM-Kisan |
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Scheme in Jharkhand, the following proposal of Government of Jharkhand was considered |
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and approved by the Committee: \''The farmer will be asked to submit ''Vanshavali |
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(Lineage)'' linked to the entry of land record comprising his \\ her ancestor''s |
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name giving a chart of successor. This lineage chart shall be submitted before |
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the Gram Sabha for calling objections. After approval of the Gram Sabha, the village |
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level \\ circle level revenue officials will verify and authenticate the Vanshawali |
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and possession of holding. This authenticated list of farmers after due verification |
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of succession chart shall be countersigned by the District level revenue authority. |
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Farmers'' names, subject to the exclusion criterion after following the aforementioned |
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process, shall be uploaded on the PM-Kisan portal along with other required details |
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for this disbursement of benefit under the scheme.\''''' |
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- '''Deep summer ploughing should be done for field preparation for pulses,apply |
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FYM and compost @ 8-10 t/ha and mix well. Sowing of Pigeon pea should be done |
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by the end of June in rows at the spacing of 60-90x15-20 cm. Seed rate should |
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be 12-15 kg/ha Seed should be treated with Carbendazim or Thirum @3g/kg seed Fertilizer |
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dose should be scheduled as per the soil test results. In general, 20-25 kg N, |
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45-50 kg P and 15-20 kg K and 20 kg S should be given basal. Improved varieties |
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like Chhattisgarh Arhar -1, Chhattisgarh-2, Rajivlochan and TJT-501 should be |
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sown. Soybean and other pulse crops should be sown with proper drainage arrangement. |
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For this seed should be treated with culture before sowing. The quantity of Rhizobium |
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culture@5g + PSB @ 10 g/kg seed should be used for this seed treatment.''' |
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- '''Union Territory. The details of farmers are being maintained by the States |
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/ UTs either in electronic form or in manual register. To make integrated platform |
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available in the country to assist in benefit transfer, a platform named **PM-Kisan |
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Portal** available at URL (**http://pmkisan.gov.in**) has been be launched for |
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uploading the farmers'' details at a single web-portal in a uniform structure. |
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9.2 The PM-Kisan Portal has been created with the following objectives - i) To |
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provide verified and single source of truth on farmers'' details at the portal. ii) Timely |
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assistance to the farmers in farm operation iii) A unified e-platform for transferring |
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of cash benefits into farmer''s bank account through Public Financial Management |
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System (PFMS) integration. iv) Location wise availability of benefited farmers'' |
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list. v) Ease of monitoring across the country on fund transaction details.''' |
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- source_sentence: What should be done before sowing pigeonpea in fields where it |
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is being sown for the first time after a long time? |
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sentences: |
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- '''The sole arbitrator shall be appointed by NABARD in case of dispute raised |
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by NABARD, from the panel of three persons nominated by RSA. Similarly, the sole |
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arbitrator shall be appointed by RSA if dispute is raised by RSA from the panel |
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of three persons nominated by NABARD. The language of the Arbitration shall be |
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English and the arbitrator shall be fluent in English. The arbitrator should be |
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person of repute and integrity and place of arbitration shall be Mumbai.\'' 9. |
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NABARD shall have the right to enter into similar MoU/agreements with any other RSA/Institution. 10. |
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Any notice required to be given under this MoU/Agreement shall be served on the |
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party at their respective address given below by hand delivery or by registered |
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post :''' |
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- '''y Firstly, Treat 1kg seeds with a mixture of 2 grams of thiram and one gram |
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of carbendazim or 4 grams of Trichoderma + 1 gram of carboxyne or carbendazim. |
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Before planting, treat each seed with a unique Rhizobium culture of pigeon pea. |
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A packet of this culture has to be sprinkled over 10 kg of seeds, then mix it |
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lightly with hands, so that a light layer is formed on the seeds. Sow this seed |
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immediately. There is a possibility of the death of culture organisms from strong |
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sunlight. In fields where pigeonpea is being sown for the first time after a long |
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time, it must use culture.''' |
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- '''Organic farming is one of the several approaches found to meet the objectives |
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of sustainable agriculture. Organic farming is often associated directly with, |
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\''Sustainable farming.\'' However, ‘organic farming’ and ‘sustainable farming’, |
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policy and ethics-wise are t wo different terms. Many techniques used in organic |
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farming like inter-cropping, mulching and integration of crops and livestock are |
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not alien to various agriculture systems including the traditional agriculture |
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practiced in old countries like India. However, organic farming is based on various |
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laws and certification programmes, which prohibit the use of almost all synthetic |
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inputs, and health of the soil is recognized as the central theme of the method. |
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Organic products are grown under a system of agriculture without the use of chemical |
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fertilizers and pesticides with an environmentally and socially responsible approach. |
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This is a method of farming that works at''' |
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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: val evaluator |
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type: val_evaluator |
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metrics: |
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- type: cosine_accuracy@1 |
|
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value: 0.4680306905370844 |
|
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name: Cosine Accuracy@1 |
|
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- type: cosine_accuracy@5 |
|
|
value: 0.9092071611253197 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.9603580562659847 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.4680306905370844 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.18184143222506394 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.09603580562659846 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.4680306905370844 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.9092071611253197 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.9603580562659847 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@5 |
|
|
value: 0.7079399335444153 |
|
|
name: Cosine Ndcg@5 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.724527850349024 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_ndcg@100 |
|
|
value: 0.732682390595948 |
|
|
name: Cosine Ndcg@100 |
|
|
- type: cosine_mrr@5 |
|
|
value: 0.6404518329070746 |
|
|
name: Cosine Mrr@5 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.6473191450493229 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_mrr@100 |
|
|
value: 0.649235332852707 |
|
|
name: Cosine Mrr@100 |
|
|
- type: cosine_map@100 |
|
|
value: 0.6492353328527082 |
|
|
name: Cosine Map@100 |
|
|
- type: dot_accuracy@1 |
|
|
value: 0.46675191815856776 |
|
|
name: Dot Accuracy@1 |
|
|
- type: dot_accuracy@5 |
|
|
value: 0.9092071611253197 |
|
|
name: Dot Accuracy@5 |
|
|
- type: dot_accuracy@10 |
|
|
value: 0.9603580562659847 |
|
|
name: Dot Accuracy@10 |
|
|
- type: dot_precision@1 |
|
|
value: 0.46675191815856776 |
|
|
name: Dot Precision@1 |
|
|
- type: dot_precision@5 |
|
|
value: 0.18184143222506394 |
|
|
name: Dot Precision@5 |
|
|
- type: dot_precision@10 |
|
|
value: 0.09603580562659846 |
|
|
name: Dot Precision@10 |
|
|
- type: dot_recall@1 |
|
|
value: 0.46675191815856776 |
|
|
name: Dot Recall@1 |
|
|
- type: dot_recall@5 |
|
|
value: 0.9092071611253197 |
|
|
name: Dot Recall@5 |
|
|
- type: dot_recall@10 |
|
|
value: 0.9603580562659847 |
|
|
name: Dot Recall@10 |
|
|
- type: dot_ndcg@5 |
|
|
value: 0.7074679767075504 |
|
|
name: Dot Ndcg@5 |
|
|
- type: dot_ndcg@10 |
|
|
value: 0.7240558935121589 |
|
|
name: Dot Ndcg@10 |
|
|
- type: dot_ndcg@100 |
|
|
value: 0.7322104337590828 |
|
|
name: Dot Ndcg@100 |
|
|
- type: dot_mrr@5 |
|
|
value: 0.6398124467178163 |
|
|
name: Dot Mrr@5 |
|
|
- type: dot_mrr@10 |
|
|
value: 0.6466797588600646 |
|
|
name: Dot Mrr@10 |
|
|
- type: dot_mrr@100 |
|
|
value: 0.6485959466634487 |
|
|
name: Dot Mrr@100 |
|
|
- type: dot_map@100 |
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value: 0.6485959466634499 |
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name: Dot Map@100 |
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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). 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 tokens |
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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': 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("SamagraDataGov/embedding_finetuned") |
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# Run inference |
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sentences = [ |
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'What should be done before sowing pigeonpea in fields where it is being sown for the first time after a long time?', |
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"'y Firstly, Treat 1kg seeds with a mixture of 2 grams of thiram and one gram of carbendazim or 4 grams of Trichoderma + 1 gram of carboxyne or carbendazim. Before planting, treat each seed with a unique Rhizobium culture of pigeon pea. A packet of this culture has to be sprinkled over 10 kg of seeds, then mix it lightly with hands, so that a light layer is formed on the seeds. Sow this seed immediately. There is a possibility of the death of culture organisms from strong sunlight. In fields where pigeonpea is being sown for the first time after a long time, it must use culture.'", |
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"'Organic farming is one of the several approaches found to meet the objectives of sustainable agriculture. Organic farming is often associated directly with, \\'Sustainable farming.\\' However, ‘organic farming’ and ‘sustainable farming’, policy and ethics-wise are t wo different terms. Many techniques used in organic farming like inter-cropping, mulching and integration of crops and livestock are not alien to various agriculture systems including the traditional agriculture practiced in old countries like India. However, organic farming is based on various laws and certification programmes, which prohibit the use of almost all synthetic inputs, and health of the soil is recognized as the central theme of the method. Organic products are grown under a system of agriculture without the use of chemical fertilizers and pesticides with an environmentally and socially responsible approach. This is a method of farming that works at'", |
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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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# 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: `val_evaluator` |
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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 | 0.468 | |
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| cosine_accuracy@5 | 0.9092 | |
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| cosine_accuracy@10 | 0.9604 | |
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| cosine_precision@1 | 0.468 | |
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| cosine_precision@5 | 0.1818 | |
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| cosine_precision@10 | 0.096 | |
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| cosine_recall@1 | 0.468 | |
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| cosine_recall@5 | 0.9092 | |
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| cosine_recall@10 | 0.9604 | |
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| cosine_ndcg@5 | 0.7079 | |
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| cosine_ndcg@10 | 0.7245 | |
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| cosine_ndcg@100 | 0.7327 | |
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| cosine_mrr@5 | 0.6405 | |
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| cosine_mrr@10 | 0.6473 | |
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| cosine_mrr@100 | 0.6492 | |
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| cosine_map@100 | 0.6492 | |
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| dot_accuracy@1 | 0.4668 | |
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| dot_accuracy@5 | 0.9092 | |
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| dot_accuracy@10 | 0.9604 | |
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| dot_precision@1 | 0.4668 | |
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| dot_precision@5 | 0.1818 | |
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| dot_precision@10 | 0.096 | |
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| dot_recall@1 | 0.4668 | |
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| dot_recall@5 | 0.9092 | |
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| dot_recall@10 | 0.9604 | |
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| dot_ndcg@5 | 0.7075 | |
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| dot_ndcg@10 | 0.7241 | |
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| dot_ndcg@100 | 0.7322 | |
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| dot_mrr@5 | 0.6398 | |
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| dot_mrr@10 | 0.6467 | |
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| dot_mrr@100 | 0.6486 | |
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| **dot_map@100** | **0.6486** | |
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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 Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 1e-05 |
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- `weight_decay`: 0.01 |
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- `num_train_epochs`: 1.0 |
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- `warmup_ratio`: 0.1 |
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- `load_best_model_at_end`: True |
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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`: 8 |
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- `per_device_eval_batch_size`: 8 |
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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`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 1e-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`: 1.0 |
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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`: 0.1 |
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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`: 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`: 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`: False |
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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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- `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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- `eval_use_gather_object`: False |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | loss | val_evaluator_dot_map@100 | |
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|:----------:|:-------:|:-------------:|:---------:|:-------------------------:| |
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| 0.0682 | 15 | 0.6463 | 0.3498 | 0.6152 | |
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| 0.1364 | 30 | 0.3071 | 0.1975 | 0.6212 | |
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| 0.2045 | 45 | 0.2023 | 0.1576 | 0.6248 | |
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| 0.2727 | 60 | 0.1457 | 0.1357 | 0.6321 | |
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| 0.3409 | 75 | 0.2456 | 0.1228 | 0.6370 | |
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| 0.4091 | 90 | 0.1407 | 0.1130 | 0.6365 | |
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| 0.4773 | 105 | 0.1727 | 0.1042 | 0.6393 | |
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| 0.5455 | 120 | 0.1311 | 0.0975 | 0.6428 | |
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| 0.6136 | 135 | 0.13 | 0.0910 | 0.6433 | |
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| 0.6818 | 150 | 0.0919 | 0.0872 | 0.6466 | |
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| 0.75 | 165 | 0.1587 | 0.0851 | 0.6490 | |
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| 0.8182 | 180 | 0.1098 | 0.0834 | 0.6481 | |
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| 0.8864 | 195 | 0.1013 | 0.0824 | 0.6461 | |
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| **0.9545** | **210** | **0.1144** | **0.082** | **0.6486** | |
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| 1.0 | 220 | - | 0.0820 | 0.6486 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.43.4 |
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- PyTorch: 2.4.1+cu121 |
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- Accelerate: 0.33.0 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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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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#### GISTEmbedLoss |
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```bibtex |
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@misc{solatorio2024gistembed, |
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title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, |
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author={Aivin V. Solatorio}, |
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year={2024}, |
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eprint={2402.16829}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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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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<!-- |
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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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--> |
