nikcheerla / amd-partial-phonetree-v1

huggingface.co
Total runs: 3.8K
24-hour runs: 0
7-day runs: 348
30-day runs: 1.7K
Model's Last Updated: January 04 2024
text-classification

Introduction of amd-partial-phonetree-v1

Model Details of amd-partial-phonetree-v1

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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
Label Examples
voicemail
  • 'Your call has been forwarded to an automated voice message'
  • 'Laura Burton. -- is not available. Record your message at'
  • "I'm sorry. No one is available to take your call."
phone_tree
  • 'Thank you for calling'
  • "Calling. To Connect and Park, just To Connect and Park, just say you're"
  • 'For calling the NatWest Group helpline.'
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-partial-phonetree-v1")
# Run inference
preds = model("Thank you for calling CHS. If you are a CHS owner,")
Training Details
Training Set Metrics
Training set Min Median Max
Word count 1 8.3697 29
Label Training Sample Count
phone_tree 5010
voicemail 5486
Training Hyperparameters
  • batch_size: (64, 64)
  • 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
0.0002 1 0.2457 -
1.0 6560 0.0057 0.1113
2.0 13120 0.0198 0.1127
3.0 19680 0.0193 0.117
  • 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-partial-phonetree-v1 on huggingface.co

3.8K
Total runs
0
24-hour runs
-130
3-day runs
348
7-day runs
1.7K
30-day runs

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amd-partial-phonetree-v1 is an open source model from GitHub that offers a free installation service, and any user can find amd-partial-phonetree-v1 on GitHub to install. At the same time, huggingface.co provides the effect of amd-partial-phonetree-v1 install, users can directly use amd-partial-phonetree-v1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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