metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
category generator refuel fixed diesel special access no vendor acas
problem description rbs generator fuel low
- text: >-
troubleshooting generator has been running non stop need emergency refuel
alarms are as follows rbs commercial power fail rbs generator fuel low rbs
generator running rbs generator shut down rbs gen transfer sw operated
test results site will go off the air without urgent refuel trouble
description per ticket tt000080377504 sfp_as cvl06424 5 external alarm
fieldreplaceableunit=sau 1 alarmport=2 rbs commercial power fail history
of trouble none vendor acas problem description fixed gen special access
none
- text: >-
troubleshooting triage category generator shut down oss netcool alarms
dxl05963 rbs generator shut down fieldreplaceableunit=sau alarmport=22
2024 08 21 02 11 15 smart alarm rbs generator shut down mdat verification
y no repeats no open related tckt no active eim no intrusion knowledge
judgement sending to vendor to investigate and resolve gen shut down
condition dispatch strategy vendor test results triage category generator
shut down oss netcool alarms dxl05963 rbs generator shut down
fieldreplaceableunit=sau alarmport=22 2024 08 21 02 11 15 smart alarm rbs
generator shut down mdat verification y no repeats no open related tckt no
active eim no intrusion knowledge judgement sending to vendor to
investigate and resolve gen shut down condition dispatch strategy vendor
trouble description triage category generator shut down oss netcool alarms
dxl05963 rbs generator shut down fieldreplaceableunit=sau alarmport=22
2024 08 21 02 11 15 smart alarm rbs generator shut down mdat verification
y no repeats no open related tckt no active eim no intrusion knowledge
judgement sending to vendor to investigate and resolve gen shut down
condition dispatch strategy vendor history of trouble triage category
generator shut down oss netcool alarms dxl05963 rbs generator shut down
fieldreplaceableunit=sau alarmport=22 2024 08 21 02 11 15 smart alarm rbs
generator shut down mdat verification y no repeats no open related tckt no
active eim no intrusion knowledge judgement sending to vendor to
investigate and resolve gen shut down condition dispatch strategy vendor
vendor acas problem description triage category generator shut down oss
netcool alarms dxl05963 rbs generator shut down fieldreplaceableunit=sau
alarmport=22 2024 08 21 02 11 15 smart alarm rbs generator shut down mdat
verification y no repeats no open related tckt no active eim no intrusion
knowledge judgement sending to vendor to investigate and resolve gen shut
down condition dispatch strategy vendor special access triage category
generator shut down oss netcool alarms dxl05963 rbs generator shut down
fieldreplaceableunit=sau alarmport=22 2024 08 21 02 11 15 smart alarm rbs
generator shut down mdat verification y no repeats no open related tckt no
active eim no intrusion knowledge judgement sending to vendor to
investigate and resolve gen shut down condition dispatch strategy vendor
- text: >-
troubleshooting triage category gen fail oss netcool alarms rbs generator
fail fieldreplaceableunit=sau alarmport=22 rbs generator fail ca
placerville cell site lotus carlsen 2024 08 20 06 27 41 smart alarm rbs
generator fail fieldreplaceableunit=sau alarmport=22 2024 08 20 06 27 36
mdat verification fixed gen history no repeats tab no open related tickets
in aots knowledge judgement sending to vendor to check gen fail dispatch
strategy vendor test results triage category gen fail oss netcool alarms
rbs generator fail fieldreplaceableunit=sau alarmport=22 rbs generator
fail ca placerville cell site lotus carlsen 2024 08 20 06 27 41 smart
alarm rbs generator fail fieldreplaceableunit=sau alarmport=22 2024 08 20
06 27 36 mdat verification fixed gen history no repeats tab no open
related tickets in aots knowledge judgement sending to vendor to check gen
fail dispatch strategy vendor trouble description triage category gen fail
oss netcool alarms rbs generator fail fieldreplaceableunit=sau
alarmport=22 rbs generator fail ca placerville cell site lotus carlsen
2024 08 20 06 27 41 smart alarm rbs generator fail
fieldreplaceableunit=sau alarmport=22 2024 08 20 06 27 36 mdat
verification fixed gen history no repeats tab no open related tickets in
aots knowledge judgement sending to vendor to check gen fail dispatch
strategy vendor history of trouble na vendor acas problem description
triage category gen fail oss netcool alarms rbs generator fail
fieldreplaceableunit=sau alarmport=22 rbs generator fail ca placerville
cell site lotus carlsen 2024 08 20 06 27 41 smart alarm rbs generator fail
fieldreplaceableunit=sau alarmport=22 2024 08 20 06 27 36 mdat
verification fixed gen history no repeats tab no open related tickets in
aots knowledge judgement sending to vendor to check gen fail dispatch
strategy vendor special access na
- text: >-
investigate gen fail requestor olivarez zachary requestor email zachary
olivarez verizonwireless com requestor phone 760 927 0406
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.625
name: Accuracy
SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 2 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 |
|---|---|
| 1 |
|
| 0 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.625 |
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("edwsiew/phantom-dispatch-02")
# Run inference
preds = model("category generator refuel fixed diesel special access no vendor acas problem description rbs generator fuel low")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 16 | 168.2540 | 915 |
| Label | Training Sample Count |
|---|---|
| 0 | 14 |
| 1 | 49 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0032 | 1 | 0.31 | - |
| 0.1587 | 50 | 0.0308 | - |
| 0.3175 | 100 | 0.0131 | - |
| 0.4762 | 150 | 0.0023 | - |
| 0.6349 | 200 | 0.0056 | - |
| 0.7937 | 250 | 0.0009 | - |
| 0.9524 | 300 | 0.0003 | - |
Framework Versions
- Python: 3.12.0
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.15.2
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}
}