pytorch_model.bin upload/update

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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+---
+base_model: BAAI/bge-small-en-v1.5
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- 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:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:900
+- loss:GISTEmbedLoss
+widget:
+- source_sentence: How does the Equity Grant contribute to the creditworthiness of
+    FPOs?
+  sentences:
+  - ''' Date………………………………   ……………………………… Signature of Branch Manager with branch seal  Name……………………………………
+    … Designation …………………………………… ………………………………  ……………………………… Signature of Authorized
+    Person in zonal office Name………………………………… Designation ……………………………………  5. Promoter''s
+    request letter  List of Enclosures  1. Recommendation  9. List of shareholders  addressed
+    to the Bank Manager on original letter head of FPO  confirmed by promoter and
+    bank  with amount of CGC  sought on Bank''s  Original letterhead with date and
+    dispatch number duly signed by the Branch Manager on each page.  2. Sanction letter
+    of  6. Implementation Schedule  10. Affidavit of promoters that  confirmed by
+    the bank.  they have not availed CGC  from any other institution for  sanctioned
+    Credit Facility.  sanctioning authority  addressed to recommending  branch.  3.
+    Bank''s approved  7. Up-to-date statement of account of  11. Field inspection
+    report of  Term loan and Cash Credit (if Sanctioned).  Bank official as on recent
+    date.  Appraisal/Process note bearing signature of sanctioning authority.  4.
+    Potential Impact on  8. a).Equity Certificate, C.A/CS  * Pin Code at Column No.
+    1. a),  certificate/RCS certificate  2. b), 2. c), 4. a) and 9. a) is Mandatory  b).
+    FORM-2, FORM-5 and FORM-23  filed with ROC for Company/RCS.  small farmer producers  1.
+    Social Impact,  2. Environmental  Impact  3.'''
+  - '''i. Shareholder List and Share Capital contribution by each Member verified
+    and certified by a Chartered Accountant (CA) prior to submission (Format attached,
+    Annexure I- Enclosure-I). ii. Resolution of FPO Board/Governing Council to seek
+    Equity Grant for Members (Format attached, Annexure I- Enclosure-II).  iii. Consent
+    of Shareholders, stating name of shareholder, gender, number of shares held, face
+    value of shares, land holding, and signature, signifying consent for Implementing
+    Agency to directly transfer the Equity Grant sanctioned to the FPC on their behalf,
+    to FPC Bank account, against the consideration of additional shares of equivalent
+    value to be issued to them by FPC and on exit- transfer of the shares as per rules
+    (Format attached, Annexure I-Enclosure-III).   iv. Audited Financials of FPO for
+    a minimum 1 year/for all years of existence of the FPO if formed less than three
+    years prior to application/ for the last 3 years for FPO in existence for 3 years
+    or more, verified and certified by a Chartered Accountant (CA) prior to submission.
+    v. Photocopy of FPO Bank Account Statement for last six months authenticated by
+    Branch Manager. vi. Business plan and budget for next 18 months. vii. Names, photographs,
+    and identity proof (one from among ration card, Aadhaar card, election identification
+    card, and passport of Representatives/ Directors authorized by the Board for executing
+    and signing all documents under the Scheme. viii. Each page of Application Form   and
+    accompanying documents should be signed by a minimum of two Board Member Authorised
+    Representatives of FPO;'''
+  - '''11.1 Producer members'' own equity supplemented by a matching Equity Grant
+    from  Government, which is required to strengthen financial base of FPOs and help
+    them to get credit from financial institutions for their projects and working
+    capital requirements for business development. Equity Grant shall be in the form
+    of matching grant upto Rs. 2,000 per farmer member of FPO subject to maximum limit
+    of Rs. 15.00 lakh fixed per FPO. This Equity Grant is not in the form of government
+    participation in equity, but only as a matching grant to the FPOs as  farmer members''
+    equity. Therefore, Rs.1,500 crore with DAC&FW is proposed in the scheme to cover
+    all the 10,000 FPOs, if maximum permissible equity is contributed to all 10,000
+    FPOs.  11.2 **Objectives of Equity Grant:** The objectives of Equity Grant are   to
+    (i) enhance  viability and sustainability of FPOs; (ii) increase credit worthiness
+    of FPOs; and  (iii) enhance shareholding of members to increase their ownership
+    and participation in their FPO.  11.3 **Eligibility Criteria for FPOs:** An FPO
+    fulfilling following criteria shall be eligible to  apply  for Equity Grant under
+    the Scheme-    (i) It shall be a legal entity as per para 2  of this guidelines.
+    (ii) It has raised equity from its Members as laid down in its Articles of  Association/
+    Bye laws, as the case may be.  (iii) The number of its Individual Shareholders
+    is in accordance with the terms  hereto read together with the Scheme.  (iv) Minimum
+    50% of its shareholders are small, marginal and landless tenant  farmers as defined
+    by the Agriculture Census carried out periodically by the Ministry of Agriculture,
+    GoI. Women farmers'' participation as its shareholders is to be preferred.  (v)
+    Maximum shareholding by any one member shall not be more than 10% of  total equity
+    of the FPO.'''
+- source_sentence: What is the purpose of the National Crop Insurance Portal?
+  sentences:
+  - '''i. Shareholder List and Share Capital contribution by each Member verified
+    and certified by a Chartered Accountant (CA) prior to submission (Format attached,
+    Annexure I- Enclosure-I). ii. Resolution of FPO Board/Governing Council to seek
+    Equity Grant for Members (Format attached, Annexure I- Enclosure-II).  iii. Consent
+    of Shareholders, stating name of shareholder, gender, number of shares held, face
+    value of shares, land holding, and signature, signifying consent for Implementing
+    Agency to directly transfer the Equity Grant sanctioned to the FPC on their behalf,
+    to FPC Bank account, against the consideration of additional shares of equivalent
+    value to be issued to them by FPC and on exit- transfer of the shares as per rules
+    (Format attached, Annexure I-Enclosure-III).   iv. Audited Financials of FPO for
+    a minimum 1 year/for all years of existence of the FPO if formed less than three
+    years prior to application/ for the last 3 years for FPO in existence for 3 years
+    or more, verified and certified by a Chartered Accountant (CA) prior to submission.
+    v. Photocopy of FPO Bank Account Statement for last six months authenticated by
+    Branch Manager. vi. Business plan and budget for next 18 months. vii. Names, photographs,
+    and identity proof (one from among ration card, Aadhaar card, election identification
+    card, and passport of Representatives/ Directors authorized by the Board for executing
+    and signing all documents under the Scheme. viii. Each page of Application Form   and
+    accompanying documents should be signed by a minimum of two Board Member Authorised
+    Representatives of FPO;'''
+  - '''i.  \''Credit Facility\'' means any  fund based credit facility  extended by
+    an Eligible  Lending Institution (ELI) to an Eligible Borrower  without any Collateral
+    Security or Third Party Guarantee ;  ii.  \''Credit Guarantee Fund\'' means the
+    Credit Guarantee Fund for FPOs created  with NABARD and NCDC  respectively under
+    the Scheme with matching grant from DAC&FW for the purpose of extending guarantee
+    to the eligible lending institution(s) against their collateral free lending to  eligible
+    FPOs;  iii.  \''Eligible Lending Institution (ELI)\'' means a Scheduled Commercial
+    Bank for  the time being included in the second Schedule to the Reserve Bank of
+    India Act, 1934, Regional Rural Banks, Co-operative Banks, Cooperative Credit  Society,
+    NEDFI, or any other institution (s) as may be decided by the NABARD and/or NCDC,
+    as the case may be, in consultation with Government of India from time to time.
+    NABARD and NCDC can also finance, if they so desire with the approval of DAC&FW/N-PMFSC.
+    NBFCs and such other financing institutions with required net worth and track
+    record may also serve as Eligible  Lending Institutions (ELIs), for lending to
+    FPOs with a moderate spread between their cost of capital and lending rate. However,
+    Standard Financial  Sector Rating Agency should have rated NBFC **to be AAA**
+    to be considered as  ELI;  iv.  \''Guarantee Cover\'' means maximum cover available
+    per eligible FPO  borrower;  v.  \''Guarantee Fee\'' means the onetime fee at
+    a specified rate of the eligible  credit facility sanctioned by the ELI, payable
+    by the ELI to NABARD or NCDC, as the case may be; and  vi.'''
+  - '''  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.'''
+- source_sentence: What should the business plan of an FPO be based on?
+  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.
+    We undertake to abide by the Terms & Conditions of the Scheme.'''
+  - '''19.1   It has been seen, during first two years of implementation of PMFBY,
+    there are various types of yield disputes, which unnecessarily delays the claim
+    settlement. Following figure shows the procedures to  be adopted in various cases.    Figure.
+    Procedures to be followed in different yield dispute cases     19.2   Wherever
+    the yield estimates reported at IU level are abnormally low or high vis-à-vis
+    the general crop  condition the Insurance Company in consultation with State Govt.
+    can make use of various products (e.g. Satellite based Vegetation Index, Weather
+    parameters, etc.) or other technologies (including  statistical test, crop models
+    etc.) to confirm yield estimates. If Insurance Company witnesses any  anomaly/deficiency
+    in the actual yield data(partial /consolidated) received from the State Govt.,
+    the  same shall be brought into the notice of concerned State department within
+    7 days from date of receipt of yield data with specific observations/remarks under
+    intimation to Govt. of India and anomaly, if any, may be resolved  in next 7 days
+    by the  State Level Coordination Committee (SLCC)  headed by Additional Chief
+    Secretary/Principal Secretary/Secretary of the concerned department. This committee
+    shall be authorized to decide all such cases and the decision in such cases shall
+    be final. The SLCC may refer the case to State Level Technical Advisory Committee
+    (STAC) for dispute resolution (Constitution of STAC is defined in Para 19.5).
+    In case the matter stands unresolved even after examination by STAC, it may be
+    escalated to TAC along with all relevant documents including minutes of meetings/records
+    of discussion and report of the STAC and SLCC. Reference to TAC can be made thereafter
+    only in conditions specified in Para 19.7.1 However, data with anomalies which
+    is not reported within 7 days will be treated as accepted to insurance company.'''
+  - '''  (vi) A farmer can be member in more than one FPO with different produce  clusters
+    but he/she will be eligible only once(for any one FPO that he/she is a member)
+    for the  matching equity grant up to his/her share.  (vii) In the Board of Directors
+    (BoD) and Governing Body (GB), as the case may  be, there shall be adequate representation
+    of women farmer member(s) and there should be minimum one woman member.   (viii)  It
+    has a duly constituted Management Committee responsible for the  business of the
+    FPO.  (ix) It has a business plan and budget for next 18 months that is based
+    on a  sustainable, revenue model as may be determined by the Implementing Agency.'''
+- source_sentence: How often does DAC&FW release advances to Implementing Agencies?
+  sentences:
+  - '''| Picking 1                                             | Picking 2      |
+    Picking 4    |\n|-------------------------------------------------------|----------------|--------------|\n|
+    Total Yield Kg)                                       |                |              |\n|
+    Picking 3                                             |                |              |\n|
+    Yield (Kg)                                            |                |              |\n|
+    Crop                                                  | Experiment no. |              |\n|
+    Yield                                                 |                |              |\n|
+    (Kg)                                                  |                |              |\n|
+    Yield                                                 |                |              |\n|
+    (Kg)                                                  |                |              |\n|
+    Yield                                                 |                |              |\n|
+    (Kg)                                                  |                |              |\n|
+    P1                                                    | P2             | P3           |\n|
+    Well Conducted CCEs in the  Taluka with 4 pickings    |                |              |\n|
+    Cotton                                                | E1             | 1            |\n|
+    Cotton                                                | E2             | 1            |\n|
+    Cotton                                                | E3             | 0.75         |\n|
+    Cotton                                                | E4             | 0.8          |\n|
+    Cotton                                                | E5             | 0.95         |\n|                                                       |
+    Average        | 0.9          |\n| 6.373                                                 |
+    2.128          | 1.282        |\n| (1                                                    |                |              |\n|
+    st                                                    |                |              |\n|
+    + 2                                                   |                |              |\n|
+    nd                                                    |                |              |\n|
+    +3                                                    |                |              |\n|
+    rd                                                    |                |              |\n|                                                       |                |              |\n|
+    Factor (Total yield/                                  |                |              |\n|
+    Picking Yield)                                        |                |              |\n|                                                       |                |              |\n|
+    (1                                                    |                |              |\n|
+    st                                                    |                |              |\n|
+    )                                                     | (1             |              |\n|
+    st                                                    |                |              |\n|
+    +                                                     |                |              |\n|
+    2                                                     |                |              |\n|
+    nd                                                    |                |              |\n|
+    )                                                     | )              |              |\n|
+    CCEs with Less Pickings in any IU within that  Taluka |                |              |\n|
+    Cotton                                                | E6 (only 1     |              |\n|
+    st                                                    |                |              |\n|
+    Picking)                                              | 1              |              |\n|
+    Cotton                                                | E7 (1          |              |\n|
+    st                                                    |                |              |\n|
+    and 2                                                 |                |              |\n|
+    nd                                                    |                |              |\n|
+    Picking)                                              | 1.2            | 1.75         |\n|
+    Cotton                                                | E8 (1          |              |\n|
+    st                                                    |                |              |\n|
+    , 2                                                   |                |              |\n|
+    nd                                                    |                |              |\n|
+    & 3                                                   |                |              |\n|
+    rd                                                    |                |              |\n|
+    Picking)                                              | 1.1            | 1.85         |'''
+  - '''8.2.1   DAC&FW will make the advance release to the Implementing Agencies (IAs)
+    on  six monthly basis based on recommendation of N-PMAFSC, Annual Action Plan
+    (AAP) of IAs and the due utilization certificate submitted to meet out the expenses
+    for engaging NPMA, FPO formation & incubation cost to CBBO and also meeting out
+    the cost of FPO management cost direct to concerned FPOs account on recommendation
+    of concerned CBBO and Equity Grant etc. for effective and timely implementation
+    of the programme. The Implementing Agencies will develop the payment schedule
+    based on their various stages and component of payment involved. The Implementing
+    Agencies will raise the demand to DAC&FW for release of payment. The Implementing
+    Agencies will submit utilization certificate of last payment released as per GFR
+    for releasing the next payment to them. In case of training, NABARD and NCDC will
+    submit to N- PMAFSC the training schedule for a year with tentative expenditure
+    for training  through specialised training institutes organised through their
+    respective nodal training Institute. DAC&FW will make due payment to NABARD and
+    NCDC for training through specialised Institutions based on the demand raised
+    by NABARD and NCDC respectively and utilisation certificate will be submitted
+    to DAC&FW by both as due. Further, as regards DAC&FW''s share towards Credit Guarantee
+    Fund (CGF) to be maintained and managed by NABARD and NCDC, the DAC&FW will provide
+    its matching share to NABARD and NCDC, as the case  may be,  which in turn will
+    submit detailed status of utilization to DAC&FW before raising the further demand
+    for next installment of CGF.'''
+  - '''7.5.1   Only those AWS/ARGs of IMD/State Govt. /private agencies should be
+    considered and notified which are as per standards defined by IMD/WMO and are
+    certified and approved by IMD/any agency to be  notified by the State/Central
+    govt. These must be optimally operational and be able to provide real  time  weather
+    data.  AWS/ARG   of private agencies should only be considered in absence of properly  functioning
+    AWS/ARGs of IMD/ State Govt. AWS /ARG data sourced for crop insurance should be  transferred
+    on real time basis to National Portal. The detailed guidelines for sharing of
+    weather data on the Portal will be circulated separately.   7.5.2   State govt
+    can explore the possibility to create dense AWS/ARG network on PPP Mode for which
+    GOI  will provide 50% of the viability gap funding.    7.5.3   The following data
+    sources may be used for validation of on account   claims and claims for prevented  sowing:'''
+- source_sentence: Who is considered as the nodal agency for engagement with the Ministry
+    of Agriculture and Farmers Welfare and Insurance Companies?
+  sentences:
+  - '''8.2.1   DAC&FW will make the advance release to the Implementing Agencies (IAs)
+    on  six monthly basis based on recommendation of N-PMAFSC, Annual Action Plan
+    (AAP) of IAs and the due utilization certificate submitted to meet out the expenses
+    for engaging NPMA, FPO formation & incubation cost to CBBO and also meeting out
+    the cost of FPO management cost direct to concerned FPOs account on recommendation
+    of concerned CBBO and Equity Grant etc. for effective and timely implementation
+    of the programme. The Implementing Agencies will develop the payment schedule
+    based on their various stages and component of payment involved. The Implementing
+    Agencies will raise the demand to DAC&FW for release of payment. The Implementing
+    Agencies will submit utilization certificate of last payment released as per GFR
+    for releasing the next payment to them. In case of training, NABARD and NCDC will
+    submit to N- PMAFSC the training schedule for a year with tentative expenditure
+    for training  through specialised training institutes organised through their
+    respective nodal training Institute. DAC&FW will make due payment to NABARD and
+    NCDC for training through specialised Institutions based on the demand raised
+    by NABARD and NCDC respectively and utilisation certificate will be submitted
+    to DAC&FW by both as due. Further, as regards DAC&FW''s share towards Credit Guarantee
+    Fund (CGF) to be maintained and managed by NABARD and NCDC, the DAC&FW will provide
+    its matching share to NABARD and NCDC, as the case  may be,  which in turn will
+    submit detailed status of utilization to DAC&FW before raising the further demand
+    for next installment of CGF.'''
+  - ''' 13.4 Laxmanrao  Imandar  National  Academy  for  Co-operative  Research  &  Development
+    (LINAC), Gurugram promoted by NCDC is designated as Nodal Training Institution
+    at central level for FPOs registered under Co-operative Societies Act and promoted
+    by NCDC. The LINAC will work in partnership with other reputed national and regional
+    training institutions like NIAM, VAMNICOM, MANAGE, NIRD, NCCT, IRMA, ASCI, State
+    and Central Agriculture Universities,  KVK, very reputed National level Management
+    and Skill Development Institutions/Universities etc.  The LINAC in consultation
+    with NCDC and DAC&FW will prepare a training module and training schedule for
+    the ensuing year, which will be got approved by N-PMAFSC. As regards training
+    expenses, in case of LINAC being nodal agency, the LINAC through NCDC will claim
+    the expenses from DAC&FW and will also submit the utilization certificate through
+    NCDC after the training programme is over.  13.5 DAC&FW in due course may also
+    identify and designate other training institute(s)  as additional Nodal Training
+    Institute at central level, which will undertake training and skill development
+    partnering with other national and regional level institutes.   13.6 The central
+    Nodal Training Institutes will ensure that training programme be held  preferably
+    in same State/UT wherein FPO trainees located are proposed to participate to reduce
+    the burden on transportation(TA/DA) cost. While formulating the training schedule,
+    Nodal Training Institutes will ensure that BoDs, CEOs/Managers and other stakeholders
+    etc. are trained twice in a year. Nodal Training Institutes will have to make
+    boarding and lodging arrangements for the  trainees and will also reimburse to
+    and fro journey tickets to the extent of sleeper class train tickets and/or ordinary
+    bus fare. Nodal Training Institutions will also evolve methodology to monitor
+    and track the performance of trainees and their FPO organization to ensure effectiveness
+    of training being provided.'''
+  - '''8.1    CSCs under Ministry of Electronics and Information Technology (MeITY)
+    have been engaged to enrol    non-loanee farmers. The Insurance Companies are
+    required to enter into a separate agreement with    CSC and pay service charges
+    as fixed by DAC&FW, GOI per farmer per village per season. No other    agreement
+    or payment is required to be made for this purpose. Nodal agency for engagement
+    with    Ministry of Agriculture and Farmers Welfare and Insurance Companies will
+    be CSC-SPV, a company    established under MeITY for carrying out e-governance
+    initiatives of GoI.  8.2    No charges/fee shall be borne or paid by the farmers
+    being enrolled through CSCs i.e. CSC-SPV and    CSC-VLE  8.3    As per IRDA circular,
+    no separate qualification/certification will be required for the VLEs of CSCs
+    to    facilitate enrolment of non-loanee farmers.  8.4    All empanelled Insurance
+    Companies will compulsorily be required to enter into an agreement with    CSC
+    for enrolment of non-loanee farmers and for provision of other defined services
+    to farmers.   8.5    Other designated intermediaries may be linked with the Portal
+    in due course.   8.6    Empanelled Insurance Companies have to necessarily register
+    on the portal and submit list and details    of agents/intermediaries engaged
+    for enrolment of non-loanee farmers in the beginning of each    season  within
+    10 days of award of work in the State.  Further all agents/intermediaries have
+    to work    strictly as per the provisions of the Scheme and IRDA regulations'''
+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: 0.51
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@5
+      value: 0.9
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.96
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.51
+      name: Cosine Precision@1
+    - type: cosine_precision@5
+      value: 0.17999999999999997
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.096
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.51
+      name: Cosine Recall@1
+    - type: cosine_recall@5
+      value: 0.9
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.96
+      name: Cosine Recall@10
+    - type: cosine_ndcg@5
+      value: 0.7319026681359824
+      name: Cosine Ndcg@5
+    - type: cosine_ndcg@10
+      value: 0.7503025597337694
+      name: Cosine Ndcg@10
+    - type: cosine_ndcg@100
+      value: 0.7590365063330959
+      name: Cosine Ndcg@100
+    - type: cosine_mrr@5
+      value: 0.6745
+      name: Cosine Mrr@5
+    - type: cosine_mrr@10
+      value: 0.6815000000000002
+      name: Cosine Mrr@10
+    - type: cosine_mrr@100
+      value: 0.6834441946057421
+      name: Cosine Mrr@100
+    - type: cosine_map@100
+      value: 0.6834441946057419
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.51
+      name: Dot Accuracy@1
+    - type: dot_accuracy@5
+      value: 0.9
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.96
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.51
+      name: Dot Precision@1
+    - type: dot_precision@5
+      value: 0.17999999999999997
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.096
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.51
+      name: Dot Recall@1
+    - type: dot_recall@5
+      value: 0.9
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.96
+      name: Dot Recall@10
+    - type: dot_ndcg@5
+      value: 0.7319026681359824
+      name: Dot Ndcg@5
+    - type: dot_ndcg@10
+      value: 0.7503025597337692
+      name: Dot Ndcg@10
+    - type: dot_ndcg@100
+      value: 0.7590365063330959
+      name: Dot Ndcg@100
+    - type: dot_mrr@5
+      value: 0.6745
+      name: Dot Mrr@5
+    - type: dot_mrr@10
+      value: 0.6815000000000002
+      name: Dot Mrr@10
+    - type: dot_mrr@100
+      value: 0.6834441946057421
+      name: Dot Mrr@100
+    - type: dot_map@100
+      value: 0.6834441946057419
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-small-en-v1.5
+
+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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 384 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (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})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("SamagraDataGov/embedding_finetuned_test")
+# Run inference
+sentences = [
+    'Who is considered as the nodal agency for engagement with the Ministry of Agriculture and Farmers Welfare and Insurance Companies?',
+    "'8.1    CSCs under Ministry of Electronics and Information Technology (MeITY) have been engaged to enrol    non-loanee farmers. The Insurance Companies are required to enter into a separate agreement with    CSC and pay service charges as fixed by DAC&FW, GOI per farmer per village per season. No other    agreement or payment is required to be made for this purpose. Nodal agency for engagement with    Ministry of Agriculture and Farmers Welfare and Insurance Companies will be CSC-SPV, a company    established under MeITY for carrying out e-governance initiatives of GoI.  8.2    No charges/fee shall be borne or paid by the farmers being enrolled through CSCs i.e. CSC-SPV and    CSC-VLE  8.3    As per IRDA circular, no separate qualification/certification will be required for the VLEs of CSCs to    facilitate enrolment of non-loanee farmers.  8.4    All empanelled Insurance Companies will compulsorily be required to enter into an agreement with    CSC for enrolment of non-loanee farmers and for provision of other defined services to farmers.   8.5    Other designated intermediaries may be linked with the Portal in due course.   8.6    Empanelled Insurance Companies have to necessarily register on the portal and submit list and details    of agents/intermediaries engaged for enrolment of non-loanee farmers in the beginning of each    season  within 10 days of award of work in the State.  Further all agents/intermediaries have to work    strictly as per the provisions of the Scheme and IRDA regulations'",
+    "' 13.4 Laxmanrao  Imandar  National  Academy  for  Co-operative  Research  &  Development (LINAC), Gurugram promoted by NCDC is designated as Nodal Training Institution at central level for FPOs registered under Co-operative Societies Act and promoted by NCDC. The LINAC will work in partnership with other reputed national and regional training institutions like NIAM, VAMNICOM, MANAGE, NIRD, NCCT, IRMA, ASCI, State and Central Agriculture Universities,  KVK, very reputed National level Management and Skill Development Institutions/Universities etc.  The LINAC in consultation with NCDC and DAC&FW will prepare a training module and training schedule for the ensuing year, which will be got approved by N-PMAFSC. As regards training expenses, in case of LINAC being nodal agency, the LINAC through NCDC will claim the expenses from DAC&FW and will also submit the utilization certificate through NCDC after the training programme is over.  13.5 DAC&FW in due course may also identify and designate other training institute(s)  as additional Nodal Training Institute at central level, which will undertake training and skill development partnering with other national and regional level institutes.   13.6 The central Nodal Training Institutes will ensure that training programme be held  preferably in same State/UT wherein FPO trainees located are proposed to participate to reduce the burden on transportation(TA/DA) cost. While formulating the training schedule, Nodal Training Institutes will ensure that BoDs, CEOs/Managers and other stakeholders etc. are trained twice in a year. Nodal Training Institutes will have to make boarding and lodging arrangements for the  trainees and will also reimburse to and fro journey tickets to the extent of sleeper class train tickets and/or ordinary bus fare. Nodal Training Institutions will also evolve methodology to monitor and track the performance of trainees and their FPO organization to ensure effectiveness of training being provided.'",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `val_evaluator`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.51       |
+| cosine_accuracy@5   | 0.9        |
+| cosine_accuracy@10  | 0.96       |
+| cosine_precision@1  | 0.51       |
+| cosine_precision@5  | 0.18       |
+| cosine_precision@10 | 0.096      |
+| cosine_recall@1     | 0.51       |
+| cosine_recall@5     | 0.9        |
+| cosine_recall@10    | 0.96       |
+| cosine_ndcg@5       | 0.7319     |
+| cosine_ndcg@10      | 0.7503     |
+| cosine_ndcg@100     | 0.759      |
+| cosine_mrr@5        | 0.6745     |
+| cosine_mrr@10       | 0.6815     |
+| cosine_mrr@100      | 0.6834     |
+| **cosine_map@100**  | **0.6834** |
+| dot_accuracy@1      | 0.51       |
+| dot_accuracy@5      | 0.9        |
+| dot_accuracy@10     | 0.96       |
+| dot_precision@1     | 0.51       |
+| dot_precision@5     | 0.18       |
+| dot_precision@10    | 0.096      |
+| dot_recall@1        | 0.51       |
+| dot_recall@5        | 0.9        |
+| dot_recall@10       | 0.96       |
+| dot_ndcg@5          | 0.7319     |
+| dot_ndcg@10         | 0.7503     |
+| dot_ndcg@100        | 0.759      |
+| dot_mrr@5           | 0.6745     |
+| dot_mrr@10          | 0.6815     |
+| dot_mrr@100         | 0.6834     |
+| dot_map@100         | 0.6834     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `num_train_epochs`: 1.0
+- `warmup_ratio`: 0.1
+- `load_best_model_at_end`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 32
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1e-05
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1.0
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step   | Training Loss | loss      | val_evaluator_cosine_map@100 |
+|:----------:|:------:|:-------------:|:---------:|:----------------------------:|
+| **0.5172** | **15** | **2.0908**    | **1.008** | **0.6834**                   |
+| 1.0        | 29     | -             | 1.0080    | 0.6834                       |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.0.1
+- Transformers: 4.43.4
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.33.0
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, 
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.43.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.43.4",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4622c440c264399c23ac0e14ec6e14d6fb96180d6e6a6113d5ce008dcd5ef3f3
+size 133462128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}