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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 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@5 |
|
- cosine_ndcg@10 |
|
- cosine_ndcg@100 |
|
- cosine_mrr@5 |
|
- cosine_mrr@10 |
|
- cosine_mrr@100 |
|
- cosine_map@100 |
|
- dot_accuracy@1 |
|
- dot_accuracy@5 |
|
- dot_accuracy@10 |
|
- dot_precision@1 |
|
- dot_precision@5 |
|
- dot_precision@10 |
|
- dot_recall@1 |
|
- dot_recall@5 |
|
- dot_recall@10 |
|
- dot_ndcg@5 |
|
- dot_ndcg@10 |
|
- dot_ndcg@100 |
|
- dot_mrr@5 |
|
- dot_mrr@10 |
|
- dot_mrr@100 |
|
- dot_map@100 |
|
pipeline_tag: sentence-similarity |
|
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:900 |
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- loss:GISTEmbedLoss |
|
widget: |
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- source_sentence: How many more Kiosks was SBI planning to establish in the next |
|
one year? |
|
sentences: |
|
- '''First installment due on (date) : ii). Last Installment due on (date) |
|
: 6. b). Cash Credit : Limit: Drawing Power: Outstanding: Comments |
|
on Irregularity ( if any): Any adverse comments on the unit by inspecting |
|
official in last inspection report: 7. A. Cost of Project (as accepted by |
|
sanctioning authority)(In Rs. Lakh) B. Means of Finance (as accepted by sanctioning |
|
authority)(In Rs. Lakh) Give component wise details a. Term loan of Bank: b. |
|
Promoter Equity c. Unsecured loan : d. Others if any Total Total 8. A. |
|
Forward Linkages: B. Backward Linkages with Small/Marginal farmers: 1 No. |
|
of members: 2 Details of Primary and Collateral Securities taken by the |
|
bank (if any) 3 a. Primary Securities b. Collateral Securities 4 5 6 (Please |
|
enclose details separately) 9 NameoftheConsortium(ifany)associatedwithCreditFacilitywithcompleteaddress,contac |
|
t details and email: 9 a) Address (*with pin-code) : 9 b) Contact Details |
|
: 9 c) Email Address : Request of Branch head for Credit Guarantee:- In |
|
view of the above information, we request Credit Guarantee Cover against Credit |
|
Facility of Rs.....................(in Rupees ) to FPO(copy of sanction letter |
|
along with appraisal/process note of competent authority is enclosed for your |
|
perusal and record ). Further we confirm that : 1. The KYC norms in respect of |
|
the Promoters have been complied by us. 2. Techno-feasibility and economic viability |
|
aspect of the project has been taken care of by the sanctioning authority and |
|
the branch. 3. On quarterly basis, bank will apprise the ........................(Name |
|
of Implementing Agency)about progress of unit, recovery of bank''s dues and present |
|
status of account to........................(Name of Implementing Agency) 4. |
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We undertake to abide by the Terms & Conditions of the Scheme.''' |
|
- '''Date: To, (i) The Managing Director Small Farmers'' Agri-Business Consortium |
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(SFAC), NCUI Auditorium, August Kranti Marg, Hauz Khas, New Delhi 110016. (ii)The |
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Managing Director National Co-operative Development Corporation (NCDC), 4, Siri |
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Institutional Area, Hauz Khas, New Delhi 110016. (iii) The Chief General Manager |
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National Bank for Agriculture and Rural Development (NABARD), Regional Office |
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--------------------------------------------------------------- (iv) To any other |
|
additional Implementing Agency allowed/designated, as the case may be. Sub: Application |
|
for Equity Grant under scheme of Formation and Promotion of 10,000 Farmer Producer |
|
Organizations (FPOs) Dear Sir/Madam, We herewith apply for Equity Grant as per |
|
the provisions under the captioned scheme. 1. The details of the FPO are as under- S. |
|
No. Particulars to be furnished Details 1. Name of the FPO 2. Correspondence |
|
address of FPO 3. Contact details of FPO 4. Registration Number 5. Date |
|
of registration/incorporation of FPO 6. Brief account of business of FPO 7. Number |
|
of Shareholder Members 8. Number of Small, Marginal and Landless Shareholder |
|
Members''' |
|
- '''1.6 Due to greater acceptability of the federations in the villages, State |
|
Bank of India (SBI) approved opening of Kiosks under BC model through federations |
|
to achieve financial inclusion. As on September 2014, 16 kiosks were working through |
|
three farmer club federations. SBI was in the process of establishing 14 more |
|
Kiosks at other village centres in the next one year in the district. The Kiosks |
|
were attached to the nearest branch and worked under the guidance of the concerned |
|
Branch Manager. The Branch Manager supervises and monitors the work of the Kiosks |
|
(BC). 1.7 At present, the Kiosks are mainly involved in providing banking services |
|
like, opening of savings bank accounts, recurring deposit accounts, acceptance |
|
of deposits and payment towards withdrawal. The kiosks are also dispensing old |
|
age pensions, student scholarships, MNREGA payments and other social sector payments, |
|
routed by the Government. The present monthly income (Rs. 8000 to Rs. 14,000) |
|
of the Kiosk is mainly from banking services. The expenditure involved was salary |
|
to the operator, rent of the premises, interest on the initial investment etc., |
|
which is about Rs. 8000 to Rs. 10,000 (Salary of the operator-Rs.4000 to Rs. 5000, |
|
Premises rent-about Rs. 2000 to Rs. 3000).''' |
|
- source_sentence: How are the kiosks attached to the nearest branch? |
|
sentences: |
|
- '''In addition, past yield data for requisite number of years will have to be |
|
made available separately for both 7.2.6 While notifying the crop(s) where a |
|
specific conversion factor is being used for reporting of yield such as in the |
|
case of rice/paddy etc, due care should be taken by the State Nodal Department |
|
to use the relevant specific nomenclature for disclosure of Average Yield, Threshold |
|
Yield and Actual Yield while releasing the Tender Document and submission of Yield |
|
data and CCE data for calculation of admissible claims. Insurance Companies will |
|
also be responsible for prior scrutiny of Tender document. Information/data provided |
|
in Tender document will be treated as final and in case of any error/ misreporting/disparity, |
|
State Govt. and Insurance Company will be equally liable for payment of additional |
|
claims arising on account of it, if any. 7.2.7 For the current season or subsequent |
|
seasons (in a multi-year contract), the States, if required, can notify additional |
|
IUs or de-notify certain IUs subject to maximum deviation of 10% of already |
|
notified IUs for the crop within a district at the same premium rate, before |
|
the cut-off date for debit of premium. If the deviation is >10% or in case of |
|
addition of new crop, actuarial premium rate may be worked out either by calculation |
|
of weighted average premium rate as prevalent in contiguous districts or by |
|
applying appropriate loading on the existing premium rate. The rates for such |
|
crops will be determined /verified by TSU and its decision will be binding on |
|
both States and ICs. 7.2.8 States implementing PMFBY at Village/ Village Panchayat |
|
level for major crops shall be entitled for 50% reimbursement of incremental |
|
expenses of CCEs and cost of smart phones/ improved technology **from GOI.** Only |
|
eligible items will be considered for reimbursement.''' |
|
- '''i. The credit guarantee cover per FPO will be limited to the project loan of |
|
Rs. 2 crore. In case of project loan up to Rs. 1 crore, credit guarantee cover |
|
will be 85% of bankable project loan with ceiling of Rs. 85 lakh; while in case |
|
of project loan above Rs.1 crore and up to Rs. 2 crore, credit guarantee cover |
|
will be 75% of bankable project loan with a maximum ceiling of Rs. 150 lakh. However, |
|
for project loan over Rs. 2 crore of bankable projet loan, credit guarantee cover |
|
will be limited maximum upto Rs.2.0 crore only. ii. ELI shall be eligible to |
|
seek Credit Guarantee Cover for a credit facility sanctioned in respect of a |
|
single FPO borrower for a maximum of 2 times over a period of 5 years. iii. |
|
In case of default, claims shall be settled up to 85% or 75 % of the amount in default |
|
subject to maximum cover as specified above. iv. Other charges such as penal |
|
interest, commitment charge, service charge, or any other levies/ expenses, or |
|
any costs whatsoever debited to the account of FPO by the ELI other than the |
|
contracted interest shall not qualify for Credit Guarantee Cover. v. The Cover |
|
shall only be granted after the ELI enters into an agreement with NABARD or NCDC, |
|
as the case may be, and shall be granted or delivered in accordance with the Terms |
|
and Conditions decided upon by NABARD or NCDC, as the case may be, from time to |
|
time.''' |
|
- '''1.6 Due to greater acceptability of the federations in the villages, State |
|
Bank of India (SBI) approved opening of Kiosks under BC model through federations |
|
to achieve financial inclusion. As on September 2014, 16 kiosks were working through |
|
three farmer club federations. SBI was in the process of establishing 14 more |
|
Kiosks at other village centres in the next one year in the district. The Kiosks |
|
were attached to the nearest branch and worked under the guidance of the concerned |
|
Branch Manager. The Branch Manager supervises and monitors the work of the Kiosks |
|
(BC). 1.7 At present, the Kiosks are mainly involved in providing banking services |
|
like, opening of savings bank accounts, recurring deposit accounts, acceptance |
|
of deposits and payment towards withdrawal. The kiosks are also dispensing old |
|
age pensions, student scholarships, MNREGA payments and other social sector payments, |
|
routed by the Government. The present monthly income (Rs. 8000 to Rs. 14,000) |
|
of the Kiosk is mainly from banking services. The expenditure involved was salary |
|
to the operator, rent of the premises, interest on the initial investment etc., |
|
which is about Rs. 8000 to Rs. 10,000 (Salary of the operator-Rs.4000 to Rs. 5000, |
|
Premises rent-about Rs. 2000 to Rs. 3000).''' |
|
- source_sentence: What is the principle on which the Scheme operates? |
|
sentences: |
|
- '''| Sl. No Section | Page No. |\n|-------------------------------------------------------------|---------------------------------------------------------------------------------|\n| |
|
Abbreviations | I-II |\n| |
|
1 | Objective of the |
|
Scheme |\n| 2 | |
|
Adoption of Technology for Scheme Administration |\n| |
|
3 | Coverage of Farmers |\n| |
|
4 | Coverage of Crops |\n| |
|
5 | Coverage of Risks |
|
& Exclusions |\n| 6 | |
|
Preconditions for implementation of the Scheme |\n| |
|
7 | Notification |\n| |
|
8 | |\n| |
|
Engagement of Common Service Centres and Intermediaries for | |\n| |
|
coverage of non loanee Farmers | |\n| |
|
11 | |\n| |
|
9 | Electronic Remittance |
|
of Funds |\n| 10 | |
|
Census code Mapping of Entities |\n| |
|
11 | Digitization of |
|
Land Records |\n| 12 | |
|
Sum Insured/Coverage Limit |\n| |
|
13 | Premium Rates and |
|
Premium Subsidy |\n| 14 | |
|
Budget for Administrative Expenses |\n| |
|
15 | Technical Support |
|
Unit(TSU)/Central Programme Management Unit(CPMU) |\n| 16 | |
|
Seasonality Discipline |\n| |
|
17 | Collection of Proposals |
|
and Premium from Farmers |\n| 18 | |
|
Assessment of Loss/Short Fall in Yield |\n| |
|
19 | Dispute Resolution |
|
regarding Yield Data/Crop Loss |\n| 20 | |
|
Use of Innovative Technologies |\n| |
|
21 | Assessment of Claims |\n| |
|
22 | Participation of |
|
Loss Assessors/Evaluators for Loss Assessment under the Scheme |\n| 23 | |
|
Procedure for Settlement of Claims |\n| |
|
24 | Important Conditions/Clauses |
|
Applicable for Coverage of Risks |\n| 25 | |
|
Acreage Discrepancy |\n| |
|
26 | Publicity and Awareness |\n| |
|
27 | Service Charges |\n| |
|
28 | Goods & Service |
|
Tax(GST) |\n| 29 | |
|
Monitoring and Review of the Scheme |\n| |
|
30 | Grievance Redressal |
|
Mechanism |\n| 31 | |
|
Empanelment and Selection of Insurance Companies |\n| |
|
32 | Clustering/Clubbing |
|
of districts for bidding by the State |\n| 33 | |
|
Assessment of Performance and De-empanelment of Insurance Companies |\n| |
|
34 | Evaluation of Efficiency |
|
of Nodal Department of the State |\n| 35 | |
|
Role & Responsibilities of Various Agencies |\n| |
|
36 | National Crop Insurance |
|
Portal for administration of Crop Insurance Program |\n| Annexure - 1 | |
|
78-85 |\n| |
|
Annexure - 2 | 86-89 |\n| |
|
Annexure - 3 | 90-93 |''' |
|
- '''16.1 The cut-off date is uniform for both loanee and non-loanee cultivators. |
|
The State-wise cut-off dates for different crops shall be based on Crop Calendar |
|
of major crops published from time to time by the Directorate of Economics and |
|
Statistics, DAC&FW,GOI. The latest copy of the Crop Calendar (District Wise, |
|
Crop Wise) is available on www.pmfby.gov.in. The SLCCCI, shall besides considering |
|
the prevailing agro-climatic conditions, rainfall distribution/ availability |
|
of water for irrigation, sowing pattern etc. in consultation with the Insurance |
|
Company fix seasonality discipline of the coverage and other activities in such |
|
a way that it does not encourage adverse selection or moral hazards. If this |
|
is violated by SLCCCI, GOI may decide not to provide premium subsidy. 16.2 The |
|
**broad indicative seasonality discipline** is given in the Table 2 below:''' |
|
- '''7.2.1 The Scheme shall operate on the principle of \''Area Approach\'' in |
|
the selected defined areas called Insurance Unit (IU). State Govt. /UT will notify |
|
crops and defined areas covered during the season in accordance with decision |
|
taken in the meeting of SLCCCI. State/UT Govt. should notify Village/Village Panchayat |
|
or any other equivalent unit as an insurance unit for major crops defined at District |
|
/ Taluka or equivalent level. For **other crops** it may be a unit of size above |
|
the level of Village/village Panchayat. For defining a crop as a major crop for |
|
deciding the Insurance Unit level, the sown area of''' |
|
- source_sentence: How can the government prioritize FPOs? |
|
sentences: |
|
- ''' 2.7 Secured credential/login, preferably linked with Aadhaar Number and |
|
mobile OTP based, for all Stakeholders viz, Central Government, State Governments, |
|
Banks, empanelled Insurance Companies and their designated field functionaries |
|
will be provided on the Portal to enable them to enter/upload/download the |
|
requisite information. 2.8 Insurance Companies shall not distribute/collect/allow |
|
any other proforma/utility/web Portal etc for collecting details of insured |
|
farmers separately. However they may provide all requisite support to facilitate |
|
Bank Branches/PACS for uploading the farmer''s details on the Portal well within |
|
the prescribed cut-off dates. 2.9 Only farmers whose data is uploaded on |
|
the National Crop Insurance Portal shall be eligible for Insurance coverage |
|
and the premium subsidy from State and Central Govt. will be released accordingly. 2.10 All |
|
data pertaining to crop-wise, area-wise historical yield data, weather data, sown |
|
area, coverage and claims data, calamity years and actual yield shall be made |
|
available on the National Crop Insurance Portal for the purpose of premium |
|
rating, claim calculation etc. 2.11 Banks/Financial Institutions/other intermediaries |
|
need to compulsorily transfer the individual farmer''s data electronically |
|
to the National Crop Insurance Portal. Accordingly Banks/FIs may endeavour to undertake |
|
CBS integration in a time bound manner for real time transfer of information/data. 2.12 It |
|
is also proposed to develop an integrated platform/portal for both PMFBY and Interest |
|
Subvention Scheme. The data/information of both the Schemes shall be auto synchronized |
|
to enable real time sharing of information and better program monitoring. 2.13 Insurance |
|
Companies shall compulsorily use technology/mobile applications for monitoring |
|
of crop health/Crop Cutting Experiments (CCEs) in coordination with concerned |
|
States. States shall also facilitate Insurance Companies with Satellite Imagery/Usage |
|
of Drones by way of prior approval of agency from which such data can be sourced. |
|
This is required for better monitoring and ground- truthing.''' |
|
- ''' (vi) States/Union Territories may actively consider to make available appropriate |
|
size of land to FPOs for setting up of CFCs and CHCs at cheaper rate on rent/lease or |
|
otherwise; or may make available free of cost. (vii) Government may prioritize |
|
FPOs to undertake procurement operation on Minimum Support Price (MSP). (viii) |
|
States must actively consider encouraging FPOs for selling their produce through e-National |
|
Agriculture Market (e-NAM) including FPO module of e-NAM or through other electronic |
|
platform from their premises itself without physically bringing the produce to |
|
the APMC market yards. (ix) Department of Agriculture, Cooperation & Farmers |
|
Welfare is authorized to finalize Operational Guidelines of the scheme (and model |
|
Bye Laws if any) including mid-term changes thereto, and issue the same with the |
|
approval of Hon''ble Minister for Agriculture & Farmers'' Welfare. .''' |
|
- '''1.1 Chhattisgarh is among the few states in India that have recorded impressive |
|
growth in agriculture in recent years. Development of farmers own institutions |
|
catering to their various needs, has kept pace with the agricultural growth. As |
|
on 30 September 2014, the state had 3,679 farmers clubs (FCs). There were eight |
|
federations of farmer clubs in the state, five in Mahasamund, two in Bilaspur |
|
and one in Mungeli district. In Bilaspur and Mungeli districts (the study area), |
|
300 FCs were formed, of which 201 were active. Majority of the farmer clubs (129 |
|
clubs) were formed by the Regional Rural Bank (Gramin Bank). Other promoting institutions |
|
include Chhattisgarh Agricon Samiti (30), CARMDAKSH (12), SBI (12), ARDB (8) and |
|
IFFDC (5). While all the clubs were active in the initial three years, many slipped |
|
into dormancy through inaction and non-availability of hand-holding support. These |
|
clubs did not have any vision or roadmap for the future. 1.2 The Chhattisgarh |
|
RO and DDM Bilaspur were keen to make the farmer clubs a sustainable entity and |
|
felt the need to federate the clubs to a higher tier so as to make the entire |
|
farmer clubs programme sustainable and the organization a viable model. With this |
|
in view, the farmer clubs were federated into four farmer club federations and |
|
were registered under ''Chhattisgarh Society Registrikaran Adhiniyam, 1973'' in |
|
the year 2012.''' |
|
- source_sentence: What is the credit guarantee cover for a project loan up to Rs. |
|
1 crore? |
|
sentences: |
|
- ''' (ii) Ongoing schemes of Government will be used in convergence to enhance |
|
the cost effectiveness of FPOs in production and raising productivity and also |
|
to meet the cost of infrastructure requirement of the FPOs. Implementing Agency |
|
may converge the fund available with various on-going Government of India schemes |
|
such as Rashtriya Krishi Vikas Yojna (RKVY), Mission for Integrated Development |
|
for Horticulture (MIDH),National Food Security Mission (NFSM), Pradhan Mantri |
|
Kisan Sampada Yojna (PM-SAMPADA), Deendayal Antyodaya Yojna-National Rural Livelihood |
|
Mission (DAY-NRLM), PM- FME Scheme of MoFPI, TRIFED etc. in programs, activities |
|
and creation of infrastructure like Custom Hiring Centre/Common Facilitation Centre |
|
with machinery/equipment relating to production and post-production, value addition |
|
and farm level processing, storage and other activities to make FPOs sustainable |
|
and economically viable. (iii) Further, Agricultural Marketing Infrastructure |
|
(AMI) Sub-Scheme of Integrated Scheme for Agriculture Marketing (ISAM) will also |
|
be converged and an FPO willing to develop post-harvest management and marketing |
|
infrastructure can avail assistance thereunder. (iv) States/ Union Territories |
|
can avail assistance for development of marketing and farm level value addition |
|
infrastructure/facilities for FPOs including setting up of Custom Hiring Centre |
|
(CHC)/Common Facilitation Center (CFC) for marketing and supply chain etc. under |
|
Agri- Market Infrastructure Fund (AMIF) approved for creation in NABARD for development |
|
of marketing and farm level value addition infrastructure/facilities in Gramin |
|
Agriculture Markets (GrAMs). In this case, operational guidelines of AMIF and |
|
NABARD''s procedure and terms and conditions of sanction and repayment of loan |
|
for AMIF shall be applicable. (v) States/Union Territories can top up and additionally |
|
supplement the activities of FPOs from their own fund for activities and infrastructure |
|
not covered under Government of India Scheme.''' |
|
- '''7.2.1 The Scheme shall operate on the principle of \''Area Approach\'' in |
|
the selected defined areas called Insurance Unit (IU). State Govt. /UT will notify |
|
crops and defined areas covered during the season in accordance with decision |
|
taken in the meeting of SLCCCI. State/UT Govt. should notify Village/Village Panchayat |
|
or any other equivalent unit as an insurance unit for major crops defined at District |
|
/ Taluka or equivalent level. For **other crops** it may be a unit of size above |
|
the level of Village/village Panchayat. For defining a crop as a major crop for |
|
deciding the Insurance Unit level, the sown area of''' |
|
- '''i. The credit guarantee cover per FPO will be limited to the project loan of |
|
Rs. 2 crore. In case of project loan up to Rs. 1 crore, credit guarantee cover |
|
will be 85% of bankable project loan with ceiling of Rs. 85 lakh; while in case |
|
of project loan above Rs.1 crore and up to Rs. 2 crore, credit guarantee cover |
|
will be 75% of bankable project loan with a maximum ceiling of Rs. 150 lakh. However, |
|
for project loan over Rs. 2 crore of bankable projet loan, credit guarantee cover |
|
will be limited maximum upto Rs.2.0 crore only. ii. ELI shall be eligible to |
|
seek Credit Guarantee Cover for a credit facility sanctioned in respect of a |
|
single FPO borrower for a maximum of 2 times over a period of 5 years. iii. |
|
In case of default, claims shall be settled up to 85% or 75 % of the amount in default |
|
subject to maximum cover as specified above. iv. Other charges such as penal |
|
interest, commitment charge, service charge, or any other levies/ expenses, or |
|
any costs whatsoever debited to the account of FPO by the ELI other than the |
|
contracted interest shall not qualify for Credit Guarantee Cover. v. The Cover |
|
shall only be granted after the ELI enters into an agreement with NABARD or NCDC, |
|
as the case may be, and shall be granted or delivered in accordance with the Terms |
|
and Conditions decided upon by NABARD or NCDC, as the case may be, from time to |
|
time.''' |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: val evaluator |
|
type: val_evaluator |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.54 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@5 |
|
value: 0.89 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.92 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.54 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@5 |
|
value: 0.17799999999999994 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09199999999999997 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.54 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@5 |
|
value: 0.89 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.92 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@5 |
|
value: 0.7328328247017718 |
|
name: Cosine Ndcg@5 |
|
- type: cosine_ndcg@10 |
|
value: 0.7420327705006653 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@100 |
|
value: 0.7588813763663693 |
|
name: Cosine Ndcg@100 |
|
- type: cosine_mrr@5 |
|
value: 0.6800000000000002 |
|
name: Cosine Mrr@5 |
|
- type: cosine_mrr@10 |
|
value: 0.6835000000000001 |
|
name: Cosine Mrr@10 |
|
- type: cosine_mrr@100 |
|
value: 0.6868224050433418 |
|
name: Cosine Mrr@100 |
|
- type: cosine_map@100 |
|
value: 0.6868224050433418 |
|
name: Cosine Map@100 |
|
- type: dot_accuracy@1 |
|
value: 0.55 |
|
name: Dot Accuracy@1 |
|
- type: dot_accuracy@5 |
|
value: 0.89 |
|
name: Dot Accuracy@5 |
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- type: dot_accuracy@10 |
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value: 0.92 |
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name: Dot Accuracy@10 |
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- type: dot_precision@1 |
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value: 0.55 |
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name: Dot Precision@1 |
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- type: dot_precision@5 |
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value: 0.17799999999999994 |
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name: Dot Precision@5 |
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- type: dot_precision@10 |
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value: 0.09199999999999997 |
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name: Dot Precision@10 |
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- type: dot_recall@1 |
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value: 0.55 |
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name: Dot Recall@1 |
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- type: dot_recall@5 |
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value: 0.89 |
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name: Dot Recall@5 |
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- type: dot_recall@10 |
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value: 0.92 |
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name: Dot Recall@10 |
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- type: dot_ndcg@5 |
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value: 0.7365235271660572 |
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name: Dot Ndcg@5 |
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- type: dot_ndcg@10 |
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value: 0.7457234729649508 |
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name: Dot Ndcg@10 |
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- type: dot_ndcg@100 |
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value: 0.7625720788306548 |
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name: Dot Ndcg@100 |
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- type: dot_mrr@5 |
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value: 0.6850000000000002 |
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name: Dot Mrr@5 |
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- type: dot_mrr@10 |
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value: 0.6885000000000001 |
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name: Dot Mrr@10 |
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- type: dot_mrr@100 |
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value: 0.6918224050433417 |
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name: Dot Mrr@100 |
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- type: dot_map@100 |
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value: 0.6918224050433417 |
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name: Dot Map@100 |
|
--- |
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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 is the credit guarantee cover for a project loan up to Rs. 1 crore?', |
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"'i. The credit guarantee cover per FPO will be limited to the project loan of Rs. 2 crore. In case of project loan up to Rs. 1 crore, credit guarantee cover will be 85% of bankable project loan with ceiling of Rs. 85 lakh; while in case of project loan above Rs.1 crore and up to Rs. 2 crore, credit guarantee cover will be 75% of bankable project loan with a maximum ceiling of Rs. 150 lakh. However, for project loan over Rs. 2 crore of bankable projet loan, credit guarantee cover will be limited maximum upto Rs.2.0 crore only. ii. ELI shall be eligible to seek Credit Guarantee Cover for a credit facility sanctioned in respect of a single FPO borrower for a maximum of 2 times over a period of 5 years. iii. In case of default, claims shall be settled up to 85% or 75 % of the amount in default subject to maximum cover as specified above. iv. Other charges such as penal interest, commitment charge, service charge, or any other levies/ expenses, or any costs whatsoever debited to the account of FPO by the ELI other than the contracted interest shall not qualify for Credit Guarantee Cover. v. The Cover shall only be granted after the ELI enters into an agreement with NABARD or NCDC, as the case may be, and shall be granted or delivered in accordance with the Terms and Conditions decided upon by NABARD or NCDC, as the case may be, from time to time.'", |
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"'7.2.1 The Scheme shall operate on the principle of \\'Area Approach\\' in the selected defined areas called Insurance Unit (IU). State Govt. /UT will notify crops and defined areas covered during the season in accordance with decision taken in the meeting of SLCCCI. State/UT Govt. should notify Village/Village Panchayat or any other equivalent unit as an insurance unit for major crops defined at District / Taluka or equivalent level. For **other crops** it may be a unit of size above the level of Village/village Panchayat. For defining a crop as a major crop for deciding the Insurance Unit level, the sown area of'", |
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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 | 0.54 | |
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| cosine_accuracy@5 | 0.89 | |
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| cosine_accuracy@10 | 0.92 | |
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| cosine_precision@1 | 0.54 | |
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| cosine_precision@5 | 0.178 | |
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| cosine_precision@10 | 0.092 | |
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| cosine_recall@1 | 0.54 | |
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| cosine_recall@5 | 0.89 | |
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| cosine_recall@10 | 0.92 | |
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| cosine_ndcg@5 | 0.7328 | |
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| cosine_ndcg@10 | 0.742 | |
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| cosine_ndcg@100 | 0.7589 | |
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| cosine_mrr@5 | 0.68 | |
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| cosine_mrr@10 | 0.6835 | |
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| cosine_mrr@100 | 0.6868 | |
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| cosine_map@100 | 0.6868 | |
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| dot_accuracy@1 | 0.55 | |
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| dot_accuracy@5 | 0.89 | |
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| dot_accuracy@10 | 0.92 | |
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| dot_precision@1 | 0.55 | |
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| dot_precision@5 | 0.178 | |
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| dot_precision@10 | 0.092 | |
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| dot_recall@1 | 0.55 | |
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| dot_recall@5 | 0.89 | |
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| dot_recall@10 | 0.92 | |
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| dot_ndcg@5 | 0.7365 | |
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| dot_ndcg@10 | 0.7457 | |
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| dot_ndcg@100 | 0.7626 | |
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| dot_mrr@5 | 0.685 | |
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| dot_mrr@10 | 0.6885 | |
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| dot_mrr@100 | 0.6918 | |
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| **dot_map@100** | **0.6918** | |
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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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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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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`: 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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| **0.5172** | **15** | **1.8109** | **1.2075** | **0.6918** | |
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| 1.0 | 29 | - | 1.2075 | 0.6918 | |
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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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