metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
ethiopia flood jul 2010 flood event lasted unknown ercs branch located
north east country reported 4 000 family affected flood 2 221 displaced
temporarily sheltered public school building 3 206 family reported
affected flood 1 565 displaced amhara region report indicated 800 family
affected displaced flood afar region total number affected family reported
field 9 000 however number affected people increasing due continuous
torrential rain part country recently 5 000 family reported displaced
amhara tigrey afar region far due flooding occurred 22 24 august 2010 ercs
icrc joint assessment tigrey amhara report ambasel tewlerda woredas south
wollo approx 1 368 hectare land crop flooded damaged hail storm based
assessment report approximately 3 745 hectare agricultural land flooded
last week several landslide reported field including 22 august 2010 mersa
worgessa word north wollo causing injury 19 death 5 people ifrc sep 2010
ethiopia
- text: >-
malaysia flood nov 2024 flood event lasted unknown end november 2024
malaysia experienced heavy rainfall attributed northeast monsoon resulting
escalating flooding across nine state kelantan terengganu kedah pahang
negeri sembilan johor perak melaka perlis heavy rain caused significant
damage livelihood house livestock severely impacting affected community 2
december 2024 national disaster management agency nadma reported
approximately 137 410 people affected ongoing flood across multiple area
malaysia deputy prime minister informed medium year flooding worst since
2014 kelantan terengganu particularly badly affected since 27 november
total 633 temporary shelter center opened accommodate 40 922 family
displaced flood disaster claimed five life kelantan terengganu confirmed
department social welfare jkm ministry agriculture food security reported
malaysia suffered approximately chf 1 79 million loss due destruction rice
paddy plantation caused flood significant damage forced country increase
reliance imported rice meet domestic need overall malaysian agriculture
sector face total estimated loss chf 3 77 million due disaster malaysian
meteorological department met malaysia forecasted continued adverse
weather condition including thunderstorm heavy rain strong wind across
peninsular malaysia 6 9 december 2024 condition expected exacerbate
ongoing flooding increasing number affected individual intensifying
challenge emergency response recovery effort persistent heavy rainfall
already caused river water level surpass designated danger threshold
posing severe risk river overflow could inundate surrounding area
relentless rainfall caused extensive damage home also critical
infrastructure road airport railway particularly east coast state severely
affected cutting intercity connection complicating relief effort combined
impact flood landslide underscore urgent need enhanced mitigation measure
coordinated response strategy ifrc 08 dec 2024 peninsular malaysia
including johor kelantan pahang perak terengganu state continues
experience heavy rainfall consequent flood resulted displacement damage
according asean disaster information network adinet past day 6 517 people
displaced 44 evacuation centre across aforementioned state echo 12 dec
2024 4 january 2025 malaysia still grappling severe flooding caused
ongoing northeast monsoon began november 2024 expected persist march 2025
eastern coastal state kelantan terengganu pahang johor hardest hit heavy
rainfall leading widespread flooding displacement significant disruption
daily life metmalaysia forecast additional five seven episode heavy
rainfall monsoon season signalling situation may continue several month
flood caused substantial damage home infrastructure livelihood road
airport railway particularly affected east coast state disrupted intercity
connectivity hampered relief effort landslide compounded crisis
underscoring need stronger disaster mitigation response strategy
additionally ministry agriculture food security reported approximately chf
1 79 million loss due destruction rice paddy plantation exacerbating
economic impact affected community flood affected nine state across
malaysia including kelantan terengganu kedah pahang negeri sembilan johor
perak melaka perlis satellite imagery unosat show terengganu kelantan
kedah severely impacted floodwaters initially covering approximately 11
000 km terengganu kelantan affecting 120 000 people kedah flood impacted 1
3 million people across 268 km significant damage cropland persists even
water begin recede ifrc 9 jan 2025 heavy rainfall affecting peninsular
malaysia since 10 january causing flood resulted population displacement
damage according asean disaster information network adinet report 12
january 3 844 people displaced 38 evacuation centre 3 779 people johor 34
perak 31 terengganu state southern peninsular malaysia echo 13 jan 2025
past day sabah sarawak state located malaysian borneo experiencing heavy
rainfall flood resulted casualty damage according medium least five people
died 7 500 people evacuated 5 385 sarawak 2 240 people affected sabah
state echo 30 jan 2025 severe monsoon flood continue devastate sabah
sarawak displacing thousand causing widespread disruption since 28 january
2025 continuous heavy rainfall compounded high tide northeast monsoon led
rising water level road inundation landslide sarawak situation worsened
due collision extreme monsoon rain high tide triggering large scale
evacuation activation multiple relief center 31 january 2025 12 486
evacuee 3 648 family relocated 62 temporary relief center pps sarawak
bintulu remains severely impacted district sheltering 5 885 evacuee 1 649
family followed serian 2 307 evacuee 709 family samarahan 2 005 evacuee
670 family significantly affected district include sibu 1 163 evacuee 293
family miri 650 evacuee 172 family kuching 475 evacuee 153 family single
evacuee recorded mukah miri continuous heavy rainfall triggered major
landslide resulting tragic loss five life ifrc 1 feb 2025 according nadma
flooding landslide sabah sarawak resulted 5 fatality miri district report
3 february 1500 hr utc 7 2 9k family 9 7k person remain displaced across
50 evacuation center sarawak bintulu serian miri sibu samarahan mukah
sabah tongod kinabatangan aha centre 3 feb 2025 heavy rainfall continued
affect eastern malaysia malaysian part borneo island since 29 january
causing flood landslide resulted casualty damage according international
federation red cross ifrc 4 february death toll stand five fatality ifrc
also report nearly 12 500 evacuated people 62 temporary relief center
across sarawak state addition around 5 200 evacuated people 33 temporary
relief center reported across sabah state ifrc 4 feb 2025 malaysia
- text: >-
paraguay flood apr 2015 flood event lasted unknown 4 apr 2015 severe storm
hit several town department concepcin northern paraguay affecting house
crop farm animal authority estimate 5 000 people affected begun response
providing roofing material food medical attention ocha processing
application emergency fund support authority response ocha 13 apr 2015 per
request paraguayan government usaid channeled 50 000 adra support response
govt 15 apr 2015 may 2015 heavy rain caused overflowing several river
affected community asuncion central department according weather expert
amount rain atypical although intensity volume short time 3 000 family
affected district ypan villeta ypacara luque mariano roque alonso villa
hayes capiat limpio yaguarn ocha 11 may 2015 june 2015 national emergency
agency sen reported around 9 602 family 48 000 people affected flooding
paraguay river asuncion 6 000 family received assistance sen coordinate
action asuncion municipal council emergency disaster paho 16 jun 2015
early july number affected family 32 000 23 000 received assistance
department hit flood alto paraguay boquern presidente hayes concepcin san
pedro cordillera central guair caazap misiones eembuc government paraguay
6 jul 2015 end july 2015 nearly 35 000 people affected flooding heavy rain
week stay shelter total 6 987 family asuncion shelter paho 24 jul 2015
last week august heavy rain strong wind hail left 900 house affected
department paraguar san pedro cordillera central gov paraguay 28 aug 2015
paraguay
- text: >-
viet nam storm rai storm surge viet nam storm rai event lasted unknown
afternoon december 16 storm rai got stronger became super typhoon 19h 16
12 center super typhoon central philippine wind level 16 gust level 17
moving northwest direction speed 25 30km h past 6 hour intensity storm
decreased one level longer level super typhoon 01 17 12 center storm right
central philippine wind level 15 gust level 17 philippine mobilized 54
response team evacuate 198 000 people prepared 26 million 414 000 food
package respond storm currently human damage recorded viet nam
- text: >-
occupied palestinian territory cold wave dec 2013 cold wave event lasted
unknown announced heavy rain fall snow storm hit west bank gaza 10
december 2013 still affecting palestinian population west bank palestine
heavy rain snow generated flood several part palestine thousand family
evacuated house extreme weather condition also caused several death
including baby gaza reported dead family home inundated ifrc 16 dec 2013
useful link ocha opt winter storm online system palestinian red crescent
society occupied palestinian territory
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: avsolatorio/GIST-Embedding-v0
model-index:
- name: SetFit with avsolatorio/GIST-Embedding-v0
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.6
name: Accuracy
SetFit with avsolatorio/GIST-Embedding-v0
This is a SetFit model that can be used for Text Classification. This SetFit model uses avsolatorio/GIST-Embedding-v0 as the Sentence Transformer embedding model. A SetFitHead instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: avsolatorio/GIST-Embedding-v0
- Classification head: a SetFitHead instance
- Maximum Sequence Length: 512 tokens
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.6 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("AlexBayer/GIST_SetFit_HIPs_v1")
# Run inference
preds = model("occupied palestinian territory cold wave dec 2013 cold wave event lasted unknown announced heavy rain fall snow storm hit west bank gaza 10 december 2013 still affecting palestinian population west bank palestine heavy rain snow generated flood several part palestine thousand family evacuated house extreme weather condition also caused several death including baby gaza reported dead family home inundated ifrc 16 dec 2013 useful link ocha opt winter storm online system palestinian red crescent society occupied palestinian territory")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 34 | 319.4125 | 2470 |
Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 16)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (3.318622110926711e-05, 3.5664318062183154e-05)
- head_learning_rate: 0.025092743459786394
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: True
- warmup_proportion: 0.1
- l2_weight: 0.05
- max_length: 512
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.1534 | 25 | 0.2384 | - |
| 0.3067 | 50 | 0.1621 | - |
| 0.4601 | 75 | 0.1389 | - |
| 0.6135 | 100 | 0.1214 | - |
| 0.7669 | 125 | 0.1115 | - |
| 0.9202 | 150 | 0.0927 | - |
Framework Versions
- Python: 3.11.12
- SetFit: 1.1.2
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Datasets: 3.5.1
- Tokenizers: 0.21.1
Citation
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}
}