--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: "Technology is also playing a pivotal role. GPS-enabled waste collection vehicles,\ \ mobile apps for \ncitizen grievances, and RFID-tagged bins are improving efficiency\ \ and accountability. In Surat, \nreal-time dashboards track the status of each\ \ waste collection route, helping civic staff respond to \nmissed pickups within\ \ hours. Mobile apps like â\x80\x9CSwachhta Sarathiâ\x80\x9D allow citizens to\ \ schedule bulk \nwaste pickups or report overflowing bins. \nSegregation remains\ \ a cornerstone of effective waste management. Behavior change campaigns \ninvolving\ \ school children, resident welfare associations, and self-help groups have helped\ \ increase \nawareness. In Indore and Ambikapur, waste warriors â\x80\x94 mostly\ \ women â\x80\x94 conduct daily household \nvisits, offering feedback on segregation\ \ and distributing color-coded bins. These cities now boast \n95%+ compliance\ \ in source segregation. \nPartnerships with the informal sector have also evolved.\ \ Waste pickers, who once operated without \nsafety or recognition, are being\ \ integrated into formal systems. Municipalities in Pune and Nagpur \nhave signed\ \ MoUs with waste picker cooperatives, offering ID cards, training, and inclusion\ \ in \npension and insurance schemes. This approach not only improves livelihoods\ \ but ensures better \nmaterial recovery rates. \nPlastic waste, a growing concern,\ \ is being addressed through extended producer responsibility \n(EPR) frameworks\ \ and plastic reuse innovations. Startups in Bhubaneswar and Vadodara are \nturning\ \ low-value plastic into construction material, such as plastic paver blocks and\ \ tiles. In \nUdaipur, a plastic buy-back initiative pays citizens â\x82¹5/kg\ \ for clean, dry plastic, promoting better \nhousehold-level segregation. \nBiomedical\ \ and hazardous waste management has improved through tighter regulations and\ \ \nmonitoring. Cities with medical colleges and large hospitals now operate dedicated\ \ biomedical \nwaste treatment facilities. In Varanasi, over 400 hospitals and\ \ clinics are geo-tagged and monitored \nfor waste disposal compliance, reducing\ \ risks of infection and illegal dumping. \nCommunity engagement continues to\ \ drive innovation. In Ranchi, school â\x80\x9Cgreen brigadesâ\x80\x9D conduct\ \ \nweekly clean-up drives and environmental education sessions. In Davanagere,\ \ a â\x80\x9CZero Waste Wardâ\x80\x9D \nmodel incentivizes citizens through tax\ \ rebates and public recognition for sustained segregation and \ncomposting practices.\ \ These grassroots efforts build a sense of ownership and civic responsibility.\ \ \nPublic-private partnerships are gaining traction. Many cities now engage private\ \ agencies for \noperation and maintenance of waste processing units, often under\ \ performance-based contracts. \nThese partnerships bring technical expertise\ \ and efficiency, though transparency in procurement and \nmonitoring remains\ \ crucial. \nLooking ahead, the emphasis is shifting toward circular economy principles.\ \ Instead of just \ndisposal, cities are exploring how waste can become a resource.\ \ Compost, recyclables, bio-CNG, \nand refuse-derived fuel (RDF) are being extracted\ \ from waste streams. Smart cities like Surat and \nBhopal are investing in waste-to-energy\ \ projects, aiming to reduce landfill dependency and generate \nclean energy.\ \ \nCapacity building of municipal staff is essential for sustainability. Training\ \ modules on waste \nauditing, decentralized processing, and public engagement\ \ are being rolled out through the National \nUrban Learning Platform. Cities\ \ are also setting up â\x80\x9CSolid Waste Management Cellsâ\x80\x9D within \n\ municipal corporations to ensure focused, expert-led implementation. \n \n" - text: "Routine Operational and Supply Update â\x80\x94 Eastern Zone Sector \nDate:\ \ 25-09-2017 \nLocation: Forward Supply Base Delta-6, Eastern Command Theater\ \ \nSummary: \nThis document serves as a consolidated update on routine operations,\ \ supply chain integrity, \npersonnel morale, and minor disciplinary matters across\ \ sub-sector E1-E4 of the Eastern Theater. \nWhile it does not contain high-level\ \ intelligence, the details herein are to remain restricted to \nauthorized personnel\ \ due to potential administrative sensitivities. \nPersonnel & Discipline Update:\ \ \nâ\x80¢ Three cases of late check-ins at FOB Lambda were reported. Following\ \ standard protocol, \nthe personnel were reprimanded and assigned additional\ \ perimeter watch shifts. \nâ\x80¢ One junior officer from the logistics crew\ \ at Base Echo-7 has been reassigned after an \ninternal inquiry concluded misuse\ \ of priority requisition tags for personal gain. A follow-up \nreview is being\ \ conducted. \nâ\x80¢ Morale surveys conducted at four key outposts indicate a\ \ minor dip in satisfaction ratings \n(avg. 76.4%) due to extended deployment\ \ without rotation. Recommendations for 2-week \nstaggered rest cycles have been\ \ submitted to Command for approval. \nMeteorological Observations: \nWeather\ \ across Sector E remains stable, with isolated reports of electrical storms over\ \ Grid Point \nE17. The Meteorological Unit has issued advisories for supply convoys\ \ to reroute via E2-Highland \nbypass during storm activity. \nNotably, data from\ \ weather drones confirm a temperature anomaly over Ridge Valley â\x80\x94 a +3.7°C\ \ \nsustained temperature increase over three consecutive nights. Though not presently\ \ a tactical \nconcern, the anomaly is being monitored for environmental impact.\ \ \nSupply Chain & Resource Allocation: \nRoutine inspection of stockpiles at\ \ Base Delta-6, Gamma-2, and Omega Outpost revealed: \nâ\x80¢ Rations and medical\ \ supplies sufficient for 34 days. \nâ\x80¢ Ammunition resupply is 94% complete;\ \ minor delay in delivery of Class C explosives due \nto transportation backlog\ \ at the central depot. \nâ\x80¢ Water purification units operating at full capacity,\ \ with minor repairs completed on two \nfiltration units at Base Gamma-2. \nA\ \ new consignment of field repair kits and reinforced body armor (Batch T9-B)\ \ is en route and \nexpected within 72 hours. \n \n \n \n" - text: "Field Surveillance System Technical Manual Review and Deployment \nBrief\ \ \nIn light of recent feedback from forward-operating units and maintenance crews,\ \ a comprehensive \nrevision of the technical manuals for the Modular Terrain\ \ Surveillance System (MTSS) has been \ncompleted. This system, deployed widely\ \ across sectors with elevated perimeter security \nrequirements, has been integral\ \ in early threat detection, terrain mapping, and signal triangulation \nefforts.\ \ However, inconsistencies in field usage, maintenance outcomes, and operator\ \ feedback \nnecessitated a structured audit and documentation overhaul. \nThe\ \ review, led by the Systems Operations Branch in conjunction with technical representatives\ \ \nfrom the original equipment manufacturer (OEM), focused on bridging operational\ \ ambiguities \nfound in the previous version of the manual, particularly in troubleshooting\ \ procedures, diagnostics \ninterpretation, and component interchangeability across\ \ variants. \nThe audit team analyzed incident logs from 27 installations over\ \ a 12-month period, identifying \npatterns in system malfunctions. The most frequent\ \ category involved erratic infrared detection \noutput, especially during rapid\ \ temperature shifts at dusk or dawn. While environmental variables \nplayed a\ \ role, improper calibration procedures were cited in over 60% of these incidents.\ \ The \nupdated manual now includes expanded calibration flowcharts, automated\ \ routine instructions, and \nconditional response matrices based on region-specific\ \ climate profiles. \nAnother prominent issue addressed is the mismatch between\ \ sensor serial batches and firmware \nupdate compatibility. In multiple cases,\ \ field teams attempted to apply a universal firmware package \nacross sensors\ \ from different production runs, causing system stalling and partial data blackouts.\ \ \nThe new documentation now includes a detailed compatibility table and introduces\ \ a secure \nverification script that flags incompatible firmware uploads before\ \ execution. \nMaintenance teams also reported inconsistent power draw behavior\ \ in the multi-sensor interface \nmodules, often leading to false fault indicators.\ \ Analysis revealed that cable shielding degradation \nin high-moisture environments\ \ caused electrical noise that mimicked hardware failure signals. The \nmanual\ \ now prescribes updated shielding inspection intervals and includes photographic\ \ \nbenchmarks of early corrosion stages to aid quicker field diagnosis. \nA key\ \ upgrade in the manual involves the layout of emergency override procedures.\ \ Previously \nfragmented across appendices, all critical fallback configurationsâ\x80\ \x94including manual sensor resets, \nnetwork bypass triggers, and local-only\ \ data retention switchesâ\x80\x94have been consolidated into a \nsingle quick-reference\ \ section with diagrammatic support. This change follows multiple after-action\ \ \nreports that highlighted response delays during high-tempo operations where\ \ immediate access to \noverride protocols was critical. \nThe revised manual\ \ also better accounts for the evolving deployment scenarios. With MTSS \nsystems\ \ increasingly being installed in remote and rugged terrain, new field tips have\ \ been added \nfor temporary stabilization on uneven or loosely packed soil. These\ \ include anchor plate \nschematics, modular support frames, and improvised stabilization\ \ strategies using locally available \nmaterialsâ\x80\x94all vetted through live\ \ field trials during the last joint equipment readiness event. \nOperator feedback\ \ was instrumental in refining interface terminology. Numerous field users \n\ reported confusion between \"signal attenuation\" and \"signal loss,\" which in\ \ prior documentation \nwere often interchanged or presented with minimal differentiation.\ \ The manual now includes \nprecise technical definitions, updated UI screenshots\ \ reflecting firmware version 6.2+, and standard \nmessaging formats for logbook\ \ entriesâ\x80\x94ensuring that incident reporting aligns accurately with \nbackend\ \ diagnostics. \n" - text: "Overview of Regional Infrastructure Development Plans (2024â\x80\x932026)\ \ \nThe Ministry of Infrastructure and Rural Development has recently finalized\ \ the first phase of its \ncomprehensive Regional Infrastructure Development Plans\ \ for the fiscal years 2024â\x80\x932026. These \nplans emphasize a broad-based\ \ approach to rural modernization, improving logistics efficiency, and \nreinforcing\ \ national integration through enhanced connectivity. While the emphasis remains\ \ on \nboosting economic potential in tier-II and tier-III regions, urban expansion\ \ corridors will also see \ncapacity upgrades through multimodal projects. \n\ The strategic planning process was initiated in Q1 2023 with a public consultation\ \ period spanning \n90 days, during which various stakeholder groups, including\ \ municipal corporations, state public \nworks departments, environmental agencies,\ \ and citizen forums, submitted their feedback. These \ninputs were analyzed and\ \ integrated into the first draft of the regional development strategy, \nfocusing\ \ on three main pillars: connectivity, sustainability, and community integration.\ \ \nOn the connectivity front, over 5,000 kilometers of feeder roads are scheduled\ \ for upgrading to all-\nweather concrete roads. Additionally, 42 key railway\ \ stations in emerging economic zones will \nundergo modernization including digital\ \ ticketing infrastructure, platform extensions, and improved \naccessibility\ \ for differently-abled citizens. Expansion plans also include the establishment\ \ of four \nnew dry ports and the refurbishment of 27 rural airstrips for short-haul\ \ cargo and emergency \nresponse functions. \nSustainability has been integrated\ \ through extensive environmental impact assessments (EIAs), \nwhich are now mandatory\ \ for every major highway and bridge project under the revised 2023 \nInfrastructure\ \ Code. EIA data will be updated and published biannually to ensure public visibility\ \ \nand cross-agency coordination. Projects situated near ecological sensitive\ \ zones, including riparian \nreserves and forest buffer belts, will be subject\ \ to stricter scrutiny under the new joint guidelines \nissued by the Ministry\ \ of Environment and the National Infrastructure Coordination Committee. \nCommunity\ \ integration has taken the form of local resource training hubs being set up\ \ in 86 rural \ndistricts. These centers are designed to upskill local populations\ \ in infrastructure maintenance, \nequipment handling, and site safety practices.\ \ With 67% of the contracted labor for the Phase-1 \nprojects expected to be sourced\ \ locally, these initiatives aim to create employment and economic \nmobility\ \ while reducing project dependency on external labor pools. \nDigital dashboards\ \ have also been introduced to improve public engagement. Citizens can now \n\ access real-time updates on project progress, submit grievances or suggestions,\ \ and monitor budget \nutilization through the â\x80\x9CInfraConnectâ\x80\x9D\ \ portal. Additionally, a third-party auditing body has been \nbrought on board\ \ to verify the milestones and timelines published by the project agencies. These\ \ \ntransparency measures are expected to contribute to both time and cost efficiencies\ \ while \nreinforcing public trust. \nIn terms of budget, a total outlay of â\x82\ ¹19,850 crores has been allocated for the 2024â\x80\x932026 period, \nsourced\ \ primarily through central funds and assisted by state contributions and international\ \ \ninfrastructure grants. So far, 67% of the planned capital has been earmarked\ \ for transport \ninfrastructure, while 23% is allocated to energy transmission\ \ corridors and the remaining 10% to \nlogistics and warehousing upgrades. \n\ The Ministry is also piloting the â\x80\x9CGreen Roadsâ\x80\x9D initiative in\ \ five states, which will use recycled \nplastic and fly ash in road construction.\ \ This aligns with the National Green Commitment signed \n" - text: "Tactical Relocation of Electronic Warfare Units in Proximity to Northern\ \ Surveillance \nCorridor \nIn response to elevated electromagnetic probing from\ \ adversarial forces near the Eastern Himalayan \nMonitoring Array, Command Group\ \ Delta has approved the phased relocation of our primary \nElectronic Warfare\ \ (EW) units to concealed forward-operation bases within Range Sector 5. The \n\ relocation includes both mobile disruption teams and sensor-jamming modules, intended\ \ to fortify \nour electronic resilience while introducing unpredictable interference\ \ into known surveillance paths. \nThe decision follows a four-week intelligence\ \ assessment conducted jointly by Signals Analysis \nTask Force and Drone Recon\ \ Group 22, which identified abnormal signal clusters consistent with \nenemy\ \ pre-intrusion mapping activity. \nThe key element in this strategic move is\ \ the redeployment of the HYDRA-V EW platformâ\x80\x94a \nmodular, multi-frequency\ \ jammer equipped with auto-resonance scanning and low-power passive \ninterference\ \ arrays. These systems were previously stationed in low-activity monitoring posts\ \ near \nGrid Point S-304, but will now be repositioned along Ridge Point Vale,\ \ which offers natural \nelevation cover and reduced electromagnetic reflection\ \ due to its basaltic soil composition. The \nHYDRA units are transported via\ \ heavy-duty camouflage convoys with ECM-shielded trailers, \nescorted by Quick\ \ Response Armor units under radio blackout protocols. \nThis operation, internally\ \ designated Ghost Crown, also involves the encryption realignment of \ncomms\ \ infrastructure in the 19th Armored Brigadeâ\x80\x99s command hierarchy. Nodes\ \ at outposts Elm 1 \nthrough Elm 4 will be upgraded with directional signal filtering,\ \ allowing secure uplinks even \nduring coordinated jamming attempts. Each node\ \ will operate under the new LunaNet secure packet \nprotocol with triple-failover\ \ encryption chaining, first introduced during Operation Sky Fuse. All \noperational\ \ parameters related to these upgrades are under Secure Directive 19-Tau and are\ \ \naccessible only to personnel with clearance level Theta-5 or higher. \nVital\ \ to this relocation effort is the insertion of false telemetry signals across\ \ known observation \nvectors. Signal Echo Drones have been launched under pre-assigned\ \ trajectories to simulate \ncommunication chatter and heat signatures of standard\ \ EW truck formations. These drones are \nremotely operated from a hardened control\ \ unit beneath Fort Asmund and are programmed to \nmaintain noise profiles that\ \ mimic live systems for up to 72 hours. This deception strategy is \nintended\ \ to redirect enemy satellite recon sweeps and throw off pattern recognition algorithms\ \ used \nby opposing signal intelligence cells. \nDuring the second phase of Ghost\ \ Crown, passive reflectors will be embedded into the local terrain \nnear the\ \ new deployment corridor. These reflectors, hidden beneath natural rock formations\ \ and \nsnow cover, will scramble long-range signal capture and misrepresent node\ \ density to thermal \nsatellite passes. Initial trials conducted last month at\ \ the Markan Test Range showed a 74% \nreduction in signal triangulation success\ \ when reflectors were deployed at strategic choke points. \nThese same reflectors\ \ will also serve as secondary detonation triggers for short-range EMP mines in\ \ \nthe event of a breach. \nTo ensure continuity of mission control, Command\ \ Center Zulu has begun issuing flash-coded \ndirectives through fiber-based Q-Link\ \ pathways. All communications are routed through triple-path \nrelays to prevent\ \ isolation or disruption. Physical orders are couriered via encrypted flash nodes\ \ \ndelivered by autonomous vehicles operating under camouflage routines mapped\ \ to regional wildlife \nmovement patterns. These systems reduce heat signature\ \ visibility while maintaining GPS-denied \nnavigation efficiency. \n" metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: nomic-ai/modernbert-embed-base model-index: - name: SetFit with nomic-ai/modernbert-embed-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9663865546218487 name: Accuracy --- # SetFit with nomic-ai/modernbert-embed-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 8192 tokens - **Number of Classes:** 4 classes ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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| 1 | | | 3 | | | 2 | | | 0 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9664 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("amritzeon/setfit_modernbert_finetunednew") # Run inference preds = model("Routine Operational and Supply Update — Eastern Zone Sector Date: 25-09-2017 Location: Forward Supply Base Delta-6, Eastern Command Theater Summary: This document serves as a consolidated update on routine operations, supply chain integrity, personnel morale, and minor disciplinary matters across sub-sector E1-E4 of the Eastern Theater. While it does not contain high-level intelligence, the details herein are to remain restricted to authorized personnel due to potential administrative sensitivities. Personnel & Discipline Update: • Three cases of late check-ins at FOB Lambda were reported. Following standard protocol, the personnel were reprimanded and assigned additional perimeter watch shifts. • One junior officer from the logistics crew at Base Echo-7 has been reassigned after an internal inquiry concluded misuse of priority requisition tags for personal gain. A follow-up review is being conducted. • Morale surveys conducted at four key outposts indicate a minor dip in satisfaction ratings (avg. 76.4%) due to extended deployment without rotation. Recommendations for 2-week staggered rest cycles have been submitted to Command for approval. Meteorological Observations: Weather across Sector E remains stable, with isolated reports of electrical storms over Grid Point E17. The Meteorological Unit has issued advisories for supply convoys to reroute via E2-Highland bypass during storm activity. Notably, data from weather drones confirm a temperature anomaly over Ridge Valley — a +3.7°C sustained temperature increase over three consecutive nights. Though not presently a tactical concern, the anomaly is being monitored for environmental impact. Supply Chain & Resource Allocation: Routine inspection of stockpiles at Base Delta-6, Gamma-2, and Omega Outpost revealed: • Rations and medical supplies sufficient for 34 days. • Ammunition resupply is 94% complete; minor delay in delivery of Class C explosives due to transportation backlog at the central depot. • Water purification units operating at full capacity, with minor repairs completed on two filtration units at Base Gamma-2. A new consignment of field repair kits and reinforced body armor (Batch T9-B) is en route and expected within 72 hours. ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:----| | Word count | 166 | 441.7377 | 559 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 15 | | 1 | 16 | | 2 | 15 | | 3 | 15 | ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (1, 16) - max_steps: 50 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: 50 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-----:|:----:|:-------------:|:---------------:| | 0.02 | 1 | 0.1914 | - | | 0.2 | 10 | 0.2452 | 0.1920 | | 0.4 | 20 | 0.1494 | 0.1849 | | 0.6 | 30 | 0.1523 | 0.1514 | | 0.8 | 40 | 0.1147 | 0.1255 | | 1.0 | 50 | 0.1071 | 0.1174 | ### Framework Versions - Python: 3.11.13 - SetFit: 1.1.3 - Sentence Transformers: 4.1.0 - Transformers: 4.48.0 - PyTorch: 2.6.0+cu124 - Datasets: 4.0.0 - Tokenizers: 0.21.4 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```