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: >-
troubleshooting n a test results n a trouble description generator failed
to start during blackout test transfer switch died before generator could
start transfer switch need repair asap back power need to be wired to
transfer switch history of trouble n a vendor acas problem description
generator failed to start during blackout test transfer switch died before
generator could start transfer switch need repair asap back power need to
be wired to transfer switch special access n a
- text: >-
1 gen with oil pressure shutdown alarm 2 genfail alarm is not showing up
in site boss requestor banaag rommel requestor email rommel banaag
verizonwireless com requestor phone 951 8342458
- text: >-
troubleshooting triage category gen fail site id cvl02692 alarms cvl02692
rbs generator fail fieldreplaceableunit=sau 1 alarmport=12 2024 08 06 06
23 36 med generator verification yes history n a knowledge judgement
sending to vendor to check generator dispatch strategy vendor test results
triage category gen fail site id cvl02692 alarms cvl02692 rbs generator
fail fieldreplaceableunit=sau 1 alarmport=12 2024 08 06 06 23 36 med
generator verification yes history n a knowledge judgement sending to
vendor to check generator dispatch strategy vendor trouble description rbs
generator fail history of trouble triage category gen fail site id
cvl02692 alarms cvl02692 rbs generator fail fieldreplaceableunit=sau 1
alarmport=12 2024 08 06 06 23 36 med generator verification yes history n
a knowledge judgement sending to vendor to check generator dispatch
strategy vendor vendor acas problem description rbs generator fail special
access n a
- text: >-
troubleshooting triage category rbs generator fuel leak alarm cvl08526
cvl08526 rbs generator fuel leak fieldreplaceableunit=sau 1 alarmport=23
2024 07 10 13 07 38 cvl08526 cvl08526 rbs rbs generator fuel leak
fieldreplaceableunit=sau 1 alarmport=20 2024 07 10 13 05 04 mdat oremis
verification generator generac baldor magnum sd30 manufacturer generac
baldor magnum model sd30 status in use serial 3008406953 kw 30 prime power
source no still on site yes engine perkins engine co ltd 404d 22ta
manufacturer perkins engine co ltd model 404d 22ta serial gr84695u9967000g
max engine kw 36 manufacturered date 2021 02 01 engine type diesel max
brake hp 49 in service date 2022 07 13 fuel type ultra low sulfur diesel
ulsd owner cell no repeats open related tckt active eim intrusion
knowledge judgement sending to vendor to investigate and resolve gen rbs
generator fuel leak condition dispatch strategy vendor test results triage
category generator rbs generator fuel leak alarm cvl08526 cvl08526 rbs
generator fuel leak fieldreplaceableunit=sau 1 alarmport=23 2024 07 10 13
07 38 cvl08526 cvl08526 rbs generator rbs generator fuel leak
fieldreplaceableunit=sau 1 alarmport=20 2024 07 10 13 05 04 mdat oremis
verification generator generac baldor magnum sd30 manufacturer generac
baldor magnum model sd30 status in use serial 3008406953 kw 30 prime power
source no still on site yes engine perkins engine co ltd 404d 22ta
manufacturer perkins engine co ltd model 404d 22ta serial gr84695u9967000g
max engine kw 36 manufacturered date 2021 02 01 engine type diesel max
brake hp 49 in service date 2022 07 13 fuel type ultra low sulfur diesel
ulsd owner cell no repeats open related tckt active eim intrusion
knowledge judgement sending to vendor to investigate and resolve gen rbs
generator fuel leak condition dispatch strategy vendor trouble description
smart rbs generator fuel leak history of trouble na vendor acas problem
description smart rbs generator fuel leak special access na
- text: >-
troubleshooting triage category gen fail oss netcool alarms ccl05638 rbs
generator fail fieldreplaceableunit=sau 1 alarmport=10 rbs generator fail
ca daly city cell site guadalupe canyon parkway 2024 07 29 23 37 56 smart
alarm y mdat verification active generac sd030 2022 d 3012298793 fixed in
compound 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 ccl05638 rbs
generator fail fieldreplaceableunit=sau 1 alarmport=10 rbs generator fail
ca daly city cell site guadalupe canyon parkway 2024 07 29 23 37 56 smart
alarm y mdat verification active generac sd030 2022 d 3012298793 fixed in
compound 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 ccl05638
rbs generator fail fieldreplaceableunit=sau 1 alarmport=10 rbs generator
fail ca daly city cell site guadalupe canyon parkway 2024 07 29 23 37 56
smart alarm y mdat verification active generac sd030 2022 d 3012298793
fixed in compound 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 n a vendor acas problem description triage
category gen fail oss netcool alarms ccl05638 rbs generator fail
fieldreplaceableunit=sau 1 alarmport=10 rbs generator fail ca daly city
cell site guadalupe canyon parkway 2024 07 29 23 37 56 smart alarm y mdat
verification active generac sd030 2022 d 3012298793 fixed in compound
history no repeats tab no open related tickets in aots knowledge judgement
sending to vendor to check gen fail dispatch strategy vendor special
access n a
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: 0.6666666666666666
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 | 0.6667 |
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-01")
# Run inference
preds = model("1 gen with oil pressure shutdown alarm 2 genfail alarm is not showing up in site boss requestor banaag rommel requestor email rommel banaag verizonwireless com requestor phone 951 8342458")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 3 | 182.3273 | 915 |
| Label | Training Sample Count |
|---|---|
| 0 | 14 |
| 1 | 41 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 3
- 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 |
|---|---|---|---|
| 0.0238 | 1 | 0.2379 | - |
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}
}