Finetuners: Reduce cognitive load when finetuning transformers 🥴
Catchy intro describing the value proposition of the finetuners package.
Installation
pip install finetuners
Example
import pathlib
from finetuners import (
FinetunerArguments,
FinetunerForTextClassification,
FinetunersDataset,
)
# load dataset
dataset = FinetunersDataset.from_path(
pathlib.Path(__file__).parents[1].joinpath("datasets", "angry-tweets")
)
# define arguments
args = FinetunerArguments(
model_name="awesome_model",
pretrained_model_name_or_path="Maltehb/danish-bert-botxo",
training_args={
"output_dir": "./runs/",
"learning_rate": 5e-5,
},
)
# init finetuner
finetuner = FinetunerForTextClassification(
dataset=dataset,
args=args,
)
finetuner.finetune()
1 maintainer
Metadata
1 maintainer
Release files for finetuners 0.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| finetuners-0.0.1.tar.gz | 5.9 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| finetuners-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.9 kB
Release files / finetuners-0.0.1.tar.gz
| Download URL | finetuners-0.0.1.tar.gz |
|---|---|
| Size | 5.9 kB |
| Tags | Source |
|
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Release files / finetuners-0.0.1-py3-none-any.whl
| Download URL | finetuners-0.0.1-py3-none-any.whl |
|---|---|
| Size | 7.0 kB |
| Tags | Python 3 |
|
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|
Security for finetuners 0.0.1
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