README.md

---
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](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [avsolatorio/GIST-Embedding-v0](https://huggingface.co/avsolatorio/GIST-Embedding-v0)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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)

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.6      |

## 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("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")
```

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## 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
```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}
}
```

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