metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >2-
Weather Report for December 2024 - North India Region
The Meteorological Wing at Pathankot Air Base has released the following
weather report for the northern region of India for December 2024:
Temperature Overview:
- Daytime temperatures will range between 5°C to 15°C in most areas.
- Night temperatures are expected to drop to -2°C to 5°C, with frost
conditions likely in high-altitude regions.
Precipitation Forecast:
- Light to moderate rainfall is expected in the foothills of Himachal
Pradesh.
- Snowfall Alert: Heavy snowfall is likely in Ladakh and the upper reaches
of Uttarakhand.
Impact on Operations:
- Low visibility conditions in the morning due to fog in plains.
- Personnel involved in convoy movements are advised to follow cold
weather protocols.
Recommendations:
Ensure adequate stocks of winter supplies, including warm clothing and
antifreeze solutions for vehicles. Updates will be provided weekly or as
conditions change.
- text: >-
helicopters carried personnel and equipment to remote sites where
traditional rescue methods were not feasible.
The second phase of the drill tested medical emergency response. Field
hospitals were set up to simulate the treatment of injured individuals,
while IAF medical teams were dispatched via helicopters to deliver first
aid kits and emergency medical supplies. Casualty evacuation operations
were also carried out, with helicopters airlifting critically injured
individuals to medical facilities.
The final phase of the drill involved logistical operations for the aerial
delivery of relief supplies. Aircraft like the C-130 Hercules and IL-76
performed air drops of food rations, water bottles, tents, and blankets to
the affected areas. The exercise highlighted the need for rapid deployment
of resources and the importance of coordination between the Indian Air
Force and NDRF.
5. Community Involvement and Awareness
The drill also included a public awareness campaign, with IAF personnel
giving briefings on the importance of disaster preparedness. A special
session was held for students and community members, explaining the roles
of different agencies in disaster management and educating the public on
basic safety protocols during earthquakes.
The drill was open to local communities in Pune, where they were
encouraged to participate in mock evacuation drills and familiarize
themselves with emergency evacuation routes. Over 500 citizens took part
in this exercise, gaining valuable insights into how to respond during an
actual disaster.
6. Media Coverage and Reporting
The event was extensively covered by the media, with both local and
national outlets attending the drill to capture the scale and operations
involved. Journalists were given access to the operation control room,
where they could observe the real-time coordination of various agencies.
The Indian Air Force and other agencies provided detailed reports on the
outcomes of the drill, which will be analyzed to improve future response
efforts.
7. Challenges and Learnings
While the drill was largely successful, several areas for improvement were
identified:
- Communication gaps: During the aerial supply drops, there were brief
communication delays between the aircraft and ground teams. This led to
minor delays in supply distribution, which will be addressed by improving
communication systems.
- Infrastructure limitations: Some of the temporary shelters were found to
be insufficient in size to accommodate the high number of evacuees. The
need for larger and more mobile shelters was identified.
- Coordination between agencies: While the drill saw successful
collaboration, future exercises will focus on further enhancing the
coordination between air force and civilian emergency services to
streamline the overall response.
- text: >2-
Training Manual on Vehicle Maintenance
The following document outlines procedures and technical specifications
for maintaining military transport vehicles deployed in operational and
non-operational zones.
Overview:
- This manual provides guidelines for preventive maintenance, routine
inspections, and troubleshooting.
- The instructions are applicable to BMP-2 armored personnel carriers and
TATA light utility vehicles used across remote regions in Rajasthan and
Sikkim.
Key Topics:
1. Inspection Protocols:
- Weekly checks for fuel injectors, transmission systems, and braking
mechanisms.
- Criteria for identifying wear and tear in high-altitude deployment.
2. Repair Guidelines:
- Steps to replace hydraulic components using standard-issue repair kits.
- Emergency repair protocols during field operations.
3. Confidential Notes:
- These procedures are strictly for official use and are not to be shared
with external parties. Unauthorized dissemination may lead to disciplinary
action.
Fig- BMP-2 Armoured Infantry Vehicle
Movement of Non-Operational Units
The following report records the logistical movements of non-operational
units within Madhya Pradesh and Uttar Pradesh, unrelated to active combat
zones.
Details:
1. Transportation Routes:
- Supplies from Gwalior Air Base to Lucknow Storage Depot via railway
freight.
- Average travel time: 48 hours under standard conditions.
2. Personnel Deployment:
- Transfer of 120 non-combatant personnel from Nagpur Logistics Hub to
Kanpur Air Command.
- text: >-
Additionally, meteorological updates for the Thar Desert were circulated
to the Indian Army’s Desert Warfare School in Jodhpur, ensuring that
troops have the latest information on temperature fluctuations, wind
conditions, and sandstorm predictions that could affect training
activities in the region.
5. Encrypted Cipher Transmission for Logistical Operations
Routine encrypted communications were conducted between Indian Army
Headquarters in New Delhi and the Air Force Command in Nagpur regarding
the movement of supplies for upcoming field exercises in Gujarat. The
cipher codes, though routine, were strictly monitored to ensure that all
logistics data remained secure. These communications included:
|Cipher Code: FV9-PW1-QX|Refers to the relocation of ammunition crates
from Mumbai to Gujarat, involving a fleet of military trucks. These
shipments are critical for supporting training exercises and were
classified as non-sensitive in nature.|
|---|---|
|Cipher Code: RW2-QY4-TL|Involves the transfer of communications equipment
from Pune to Rajasthan, ensuring that field units remain in contact with
command centers during simulated operations.|
|Cipher Code: ZF1-RG6-ML|Refers to the movement of fuel supplies for
helicopters and non-combat vehicles used in Chandigarh during routine
exercise planning.|
Though these communications are encrypted, the information remains
non-sensitive as the contents are about routine supply transfers, which do
not involve high-priority or classified assets.
6. Equipment Distribution for Ongoing Exercises
Routine distribution of training and technical manuals for Sukhoi Su-30MKI
fighter jets took place between Air Force Command in Bangalore and
Chandigarh Air Base. The manuals contained updated procedures for aircraft
operations and routine maintenance, ensuring the aircraft remain in
optimal operational condition. Similar manuals for BrahMos Missile Systems
were circulated to Naval Base Kochi to maintain system readiness for
long-range surface-to-air engagements.
The documents also included revised operational protocols for using radar
systems, with a special focus on techniques for evading electronic warfare
tactics. These updates were part of ongoing training regimens and did not
contain classified information related to any sensitive operations or
technological advancements.
7. Surveillance and Intelligence Reports
Routine surveillance operations by Indian Air Force UAVs have been
conducted across the Ladakh region to track weather conditions and the
geographical layout of the terrain. This is to support future mission
planning and ensure that the terrain data is up-to-date. Additionally,
aerial photographs of the Sikkim-Tibet border taken by IAF reconnaissance
planes were analyzed for minor discrepancies in terrain mapping,
contributing to the optimization of supply routes in remote regions.
A similar routine reconnaissance mission was conducted over Pondicherry by
Naval Intelligence. The goal of this mission was to update nautical charts
of nearby waters to aid in naval operations, particularly focusing on new
ports and potential strategic locations for refueling stations. These
photographs are crucial for the Indian Navy's future operational planning.
- text: >2-
Dissemination of Technical Training Materials
Updated technical training materials were issued for the maintenance of
upgraded missile systems. The new manuals include guidelines for
diagnosing software malfunctions and implementing hardware upgrades. Teams
stationed at the Hyderabad depot completed an intensive workshop to
familiarize themselves with these procedures.
To enhance accessibility, digital versions of the manuals were uploaded to
the secure intranet portal. Maintenance staff have praised the
comprehensive structure and clarity of the documentation, which has
significantly streamlined troubleshooting processes.
Lessons from Joint Military Exercises
Joint military exercises conducted near the Rann of Kutch yielded
actionable insights into force coordination. These drills involved air and
ground units practicing synchronized maneuvers to counter hypothetical
infiltration scenarios.
Post-drill assessments highlighted the success of integrated command
structures and the need for further refinement in communication protocols.
Recommendations from these exercises are being incorporated into training
modules, ensuring continuous improvement in operational efficiency.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9897959183673469
name: Accuracy
SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 4 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| 0 |
|
| 2 |
|
| 3 |
|
| 1 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.9898 |
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("Amlan99/iaf_setfit_reclassification")
# Run inference
preds = model(" Weather Report for December 2024 - North India Region
The Meteorological Wing at Pathankot Air Base has released the following weather report for the northern region of India for December 2024:
Temperature Overview:
- Daytime temperatures will range between 5°C to 15°C in most areas.
- Night temperatures are expected to drop to -2°C to 5°C, with frost conditions likely in high-altitude regions.
Precipitation Forecast:
- Light to moderate rainfall is expected in the foothills of Himachal Pradesh.
- Snowfall Alert: Heavy snowfall is likely in Ladakh and the upper reaches of Uttarakhand.
Impact on Operations:
- Low visibility conditions in the morning due to fog in plains.
- Personnel involved in convoy movements are advised to follow cold weather protocols.
Recommendations:
Ensure adequate stocks of winter supplies, including warm clothing and antifreeze solutions for vehicles. Updates will be provided weekly or as conditions change.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 276.2959 | 485 |
| Label | Training Sample Count |
|---|---|
| 0 | 25 |
| 1 | 27 |
| 2 | 24 |
| 3 | 22 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- 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: -1
- load_best_model_at_end: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0022 | 1 | 0.1465 | - |
| 0.1111 | 50 | 0.2448 | - |
| 0.2222 | 100 | 0.1783 | - |
| 0.3333 | 150 | 0.1146 | - |
| 0.4444 | 200 | 0.0693 | - |
| 0.5556 | 250 | 0.0147 | - |
| 0.6667 | 300 | 0.0086 | - |
| 0.7778 | 350 | 0.0082 | - |
| 0.8889 | 400 | 0.0091 | - |
| 1.0 | 450 | 0.0076 | 0.0054 |
| 1.1111 | 500 | 0.0049 | - |
| 1.2222 | 550 | 0.0084 | - |
| 1.3333 | 600 | 0.0062 | - |
| 1.4444 | 650 | 0.0089 | - |
| 1.5556 | 700 | 0.005 | - |
| 1.6667 | 750 | 0.0054 | - |
| 1.7778 | 800 | 0.0058 | - |
| 1.8889 | 850 | 0.0068 | - |
| 2.0 | 900 | 0.0067 | 0.0054 |
| 2.1111 | 950 | 0.0036 | - |
| 2.2222 | 1000 | 0.0057 | - |
| 2.3333 | 1050 | 0.0093 | - |
| 2.4444 | 1100 | 0.0067 | - |
| 2.5556 | 1150 | 0.0076 | - |
| 2.6667 | 1200 | 0.0046 | - |
| 2.7778 | 1250 | 0.007 | - |
| 2.8889 | 1300 | 0.0035 | - |
| 3.0 | 1350 | 0.0048 | 0.0050 |
| 3.1111 | 1400 | 0.0045 | - |
| 3.2222 | 1450 | 0.0075 | - |
| 3.3333 | 1500 | 0.005 | - |
| 3.4444 | 1550 | 0.0046 | - |
| 3.5556 | 1600 | 0.0056 | - |
| 3.6667 | 1650 | 0.0056 | - |
| 3.7778 | 1700 | 0.0075 | - |
| 3.8889 | 1750 | 0.0058 | - |
| 4.0 | 1800 | 0.0046 | 0.0049 |
Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.4.1
- Transformers: 4.42.2
- PyTorch: 2.6.0+cu124
- Datasets: 3.4.1
- Tokenizers: 0.19.1
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}
}