🎉SetFit v1.1.0 is out! Training efficient classifiers on CPU or GPU now uses the Sentence Transformers Trainer, and we resolved a lot of issues caused by updates of third-party libraries (like Transformers). Details:
text
🎉SetFit v1.1.0 is out! Training efficient classifiers on CPU or GPU now uses the Sentence Transformers Trainer, and we resolved a lot of issues caused by updates of third-party libraries (like Transformers). Details:
new_line
new_line
Training a SetFit classifier model consists of 2 phases: 
text
Training a SetFit classifier model consists of 2 phases: 
new_line
1. Finetuning a Sentence Transformer embedding model
text
1. Finetuning a Sentence Transformer embedding model
new_line
2. Training a Classifier to map embeddings -> classes
text
2. Training a Classifier to map embeddings -> classes
new_line
new_line
🔌The first phase now uses the SentenceTransformerTrainer that was introduced in the Sentence Transformers v3 update. This brings some immediate upsides like MultiGPU support, without any (intended) breaking changes.
text
🔌The first phase now uses the SentenceTransformerTrainer that was introduced in the Sentence Transformers v3 update. This brings some immediate upsides like MultiGPU support, without any (intended) breaking changes.
new_line
new_line
➡️ Beyond that, we softly deprecated the "evaluation_strategy" argument in favor of "eval_strategy" (following a Transformers deprecation), and deprecated Python 3.7. In return, we add official support for Python 3.11 and 3.12.
text
➡️ Beyond that, we softly deprecated the "evaluation_strategy" argument in favor of "eval_strategy" (following a Transformers deprecation), and deprecated Python 3.7. In return, we add official support for Python 3.11 and 3.12.
new_line
new_line
✨ There's some more minor changes too, like max_steps and eval_max_steps now being a hard limit instead of an approximate one, training/validation losses now logging nicely in Notebooks, and the "device" parameter no longer being ignored in some situations.
text
✨ There's some more minor changes too, like max_steps and eval_max_steps now being a hard limit instead of an approximate one, training/validation losses now logging nicely in Notebooks, and the "device" parameter no longer being ignored in some situations.
new_line
new_line
Check out the full release notes here: 
text
Check out the full release notes here: 
https://github.com/huggingface/setfit/releases/tag/v1.1.0
https://github.com/huggingface/setfit/releases/tag/v1.1.0
link
new_line
Or read the documentation: 
text
Or read the documentation: 
https://huggingface.co/docs/setfit
https://huggingface.co/docs/setfit
link
new_line
Or check out the public SetFit models for inspiration: 
text
Or check out the public SetFit models for inspiration: 
https://huggingface.co/models?library=setfit&sort=created
https://huggingface.co/models?library=setfit&sort=created
link
new_line
new_line
P.s. the model in the code snippet trained in 1 minute and it can classify ~6000 sentences per second on my GPU.
text
P.s. the model in the code snippet trained in 1 minute and it can classify ~6000 sentences per second on my GPU.
🎉SetFit v1.1.0 is out! Training efficient classifiers on CPU or GPU now uses the Sentence Transformers Trainer, and we resolved a lot of issues caused by updates of third-party libraries (like Transformers). Details:

Training a SetFit classifier model consists of 2 phases: 
1. Finetuning a Sentence Transformer embedding model
2. Training a Classifier to map embeddings -> classes

🔌The first phase now uses the SentenceTransformerTrainer that was introduced in the Sentence Transformers v3 update. This brings some immediate upsides like MultiGPU support, without any (intended) breaking changes.

➡️ Beyond that, we softly deprecated the "evaluation_strategy" argument in favor of "eval_strategy" (following a Transformers deprecation), and deprecated Python 3.7. In return, we add official support for Python 3.11 and 3.12.

✨ There's some more minor changes too, like max_steps and eval_max_steps now being a hard limit instead of an approximate one, training/validation losses now logging nicely in Notebooks, and the "device" parameter no longer being ignored in some situations.

Check out the full release notes here: https://github.com/huggingface/setfit/releases/tag/v1.1.0
Or read the documentation: https://huggingface.co/docs/setfit
Or check out the public SetFit models for inspiration: https://huggingface.co/models?library=setfit&sort=created

P.s. the model in the code snippet trained in 1 minute and it can classify ~6000 sentences per second on my GPU.
❤️
lbourdois trollek AtAndDev privategeek24 Bruhn osanseviero louisbrulenaudet
👀
John6666 lbourdois AtAndDev osanseviero
🔥
lbourdois AtAndDev cnmoro osanseviero
🚀
lbourdois AtAndDev