'Your call has been forwarded to an automated voice messaging system. 6 '
'Please leave your message for 8083526996. '
"This is Bart Jumper. I'm sorry I missed your call. Please leave your name and number, and I'll return your call as soon as I "
phone_tree
'Thank you for calling Periton. A next '
'Thank you for calling Signifide. Our main number has changed. The new number is eight six six two '
'Thank you for calling Icahn Health and Fitness. If you know the extension you wish to reach, '
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("nikcheerla/amd-full-phonetree-v1")
# Run inference
preds = model("For calling WL Gore and Associates Incorporated. Please wait ")
Training Details
Training Set Metrics
Training set
Min
Median
Max
Word count
1
14.7789
214
Label
Training Sample Count
phone_tree
4979
voicemail
5519
Training Hyperparameters
batch_size: (32, 32)
num_epochs: (3, 3)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
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: True
warmup_proportion: 0.1
seed: 42
eval_max_steps: -1
load_best_model_at_end: True
Training Results
Epoch
Step
Training Loss
Validation Loss
0.0001
1
0.2196
-
1.0
13123
0.0001
0.1209
2.0
26246
0.0
0.1101
3.0
39369
0.0446
0.1108
The bold row denotes the saved checkpoint.
Framework Versions
Python: 3.10.12
SetFit: 1.0.1
Sentence Transformers: 2.2.2
Transformers: 4.35.2
PyTorch: 2.0.1+cu118
Datasets: 2.16.1
Tokenizers: 0.15.0
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}
}
Runs of nikcheerla amd-full-phonetree-v1 on huggingface.co
3.8K
Total runs
0
24-hour runs
-133
3-day runs
337
7-day runs
1.7K
30-day runs
More Information About amd-full-phonetree-v1 huggingface.co Model
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