metadata
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "Copy env-production to .env (setting up)\nHi, very sorry to ask, dont know if here would be ok... but where can I get the env-production file to be copied to .env? because here https://github.com/frappe/frappe_docker/wiki/Easiest-Install says so but cant be found...```\r\n\r\nThanks,\r\n\r\n$ **cp env-production .env**\r\n$ sed -i -e \"s/FRAPPE_VERSION=edge/FRAPPE_VERSION=v12.9.4/g\" .env\r\n$ sed -i -e \"s/ERPNEXT_VERSION=edge/ERPNEXT_VERSION=v12.6.2/g\" .env\r\n$ sed -i -e \"s/email@example.com/hello@myweb.com/g\" .env\r\n$ sed -i -e \"s/erp.example.com/erp.myweb.com/g\" .env\r\n$ sed -i -e \"s/ADMIN_PASSWORD=admin/ADMIN_PASSWORD=supersecret/g\" .env\r\n$ sed -i -e \"s/MYSQL_ROOT_PASSWORD=admin/MYSQL_ROOT_PASSWORD=longsecretpassword/g\" .env\r\n```"
- text: "[BUG] Unwanted \"supported\" or \"unknown\" message\n## User Story\r\nI see string \"supported\" on \"start\" command.\r\n\r\n## Basic info\r\n\r\n* **Distro:** Ubuntu 20.04.3 LTS\r\n* **Game:** Any\r\n* **Command:** start\r\n* **LinuxGSM version:** v21.5.0\r\n\r\n## Further Information\r\n\r\nProbably it is debug message from deps check. \"supported\" is replaced by \"unknown\" on unsupported distro.\r\nThis LGSM is upgraded from previous version.\r\n```\r\naaa@hostname:~$ ./arma3server start\r\nsupported\r\nsupported\r\nsupported\r\n[ OK ] Starting arma3server: server name\r\n```\r\n\r\n## To Reproduce\r\n\r\nSteps to reproduce the behaviour:\r\n1. Use start command\r\n\r\n## Expected behaviour\r\nSee only \"[ OK ] Starting arma3server: server name\" message,"
- text: |-
Docs are still using `DBT_PROJECT_DIR`
This was switched to `ARTEFACTS_ DBT_PROJECT_DIR` last release.
- text: |-
Document CNI upgrade strategies
Document supported CNIs + supported CNI upgrade strategies.
- text: |-
Read the Docs
Implement read the docs for documentation
inference: true
SetFit
This is a SetFit model that can be used for Text Classification. 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
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 384 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 |
---|---|
bug |
|
non-bug |
|
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("setfit_model_id")
# Run inference
preds = model("Read the Docs
Implement read the docs for documentation")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 186.9402 | 10443 |
Label | Training Sample Count |
---|---|
bug | 47 |
non-bug | 137 |
Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 1)
- 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: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0022 | 1 | 0.6468 | - |
0.1087 | 50 | 0.2755 | - |
0.2174 | 100 | 0.0535 | - |
0.3261 | 150 | 0.0011 | - |
0.4348 | 200 | 0.0004 | - |
0.5435 | 250 | 0.0003 | - |
0.6522 | 300 | 0.0003 | - |
0.7609 | 350 | 0.0002 | - |
0.8696 | 400 | 0.0002 | - |
0.9783 | 450 | 0.0001 | - |
Framework Versions
- Python: 3.11.6
- SetFit: 1.1.0
- Sentence Transformers: 3.0.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Datasets: 2.21.0
- 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}
}