How to build a custom text classifier without days of human labeling
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David Berenstein
davidberenstein1957
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reacted to
their
post with 👀
5 days ago
Post
663
The Synthetic Data Generator now directly integrates with Argilla, so you can generate and curate your own high-quality datasets from pure natural language!
Up next -> include dataset generation for text classification.
Other suggestions? Let us know.
Space: argilla/synthetic-data-generator
Up next -> include dataset generation for text classification.
Other suggestions? Let us know.
Space: argilla/synthetic-data-generator
reacted to
their
post with ➕❤️
5 days ago
Post
1663
You can now build a custom text classifier without days of human labeling!
👍 LLMs work reasonably well as text classifiers.
👎 They are expensive to run at scale and their performance drops in specialized domains.
👍 Purpose-built classifiers have low latency and can potentially run on CPU.
👎 They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.
Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback
👍 LLMs work reasonably well as text classifiers.
👎 They are expensive to run at scale and their performance drops in specialized domains.
👍 Purpose-built classifiers have low latency and can potentially run on CPU.
👎 They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.
Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback
posted
an
update
5 days ago
Post
1925
Import any dataset from the Hub and configure your labeling tasks without needing any code!
Really excited about extending the Hugging Face Hub integration with many more streamlined features and workflows, and we would love to hear your feedback and ideas, so don't feel shy and reach out 🫶🏽
https://huggingface.co/blog/argilla-ui-hub
Really excited about extending the Hugging Face Hub integration with many more streamlined features and workflows, and we would love to hear your feedback and ideas, so don't feel shy and reach out 🫶🏽
https://huggingface.co/blog/argilla-ui-hub
reacted to
their
post with 👀🚀🤗
6 days ago
Post
2942
Vector Search (most) datasets on the Hugging Face Hub 🔦
Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).
Should work fast enough for datasets up to 100K.
davidberenstein1957/vectorsearch-hub-datasets
Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).
Should work fast enough for datasets up to 100K.
davidberenstein1957/vectorsearch-hub-datasets
posted
an
update
6 days ago
Post
2942
Vector Search (most) datasets on the Hugging Face Hub 🔦
Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).
Should work fast enough for datasets up to 100K.
davidberenstein1957/vectorsearch-hub-datasets
Powered by: Polars, DuckDB, Gradio and model2vec (lightning-fast embeddings by Stéphan Tulkens).
Should work fast enough for datasets up to 100K.
davidberenstein1957/vectorsearch-hub-datasets
posted
an
update
11 days ago
Post
1717
⚡️ LLMs do a good job at NER, but don't you want to do learn how to do more with less?
Go from 🐢 -> 🐇
If you want a small model to perform well on your problem, you need to fine-tune it.
Bootstrap with a teacher model.
Correct potential mistakes to get high-quality data.
Fine-tune your student model
Go more accurate and more efficient.
Free signup: https://lu.ma/zx2t7irs
Go from 🐢 -> 🐇
If you want a small model to perform well on your problem, you need to fine-tune it.
Bootstrap with a teacher model.
Correct potential mistakes to get high-quality data.
Fine-tune your student model
Go more accurate and more efficient.
Free signup: https://lu.ma/zx2t7irs
posted
an
update
23 days ago
Post
1663
You can now build a custom text classifier without days of human labeling!
👍 LLMs work reasonably well as text classifiers.
👎 They are expensive to run at scale and their performance drops in specialized domains.
👍 Purpose-built classifiers have low latency and can potentially run on CPU.
👎 They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.
Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback
👍 LLMs work reasonably well as text classifiers.
👎 They are expensive to run at scale and their performance drops in specialized domains.
👍 Purpose-built classifiers have low latency and can potentially run on CPU.
👎 They require labeled training data.
Combine the best of both worlds: the automatic labeling capabilities of LLMs and the high-quality annotations from human experts to train and deploy a specialized model.
Blog: https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback
reacted to
nroggendorff's
post with 😎
23 days ago
Post
1248
100 followers? When did that happen?
reacted to
m-ric's
post with 👀
23 days ago
Post
1678
By far the coolest release of the day!
> The Open LLM Leaderboard, most comprehensive suite for comparing Open LLMs on many benchmarks, just released a comparator tool that lets you dig into the detail of differences between any models.
Here's me checking how the new Llama-3.1-Nemotron-70B that we've heard so much compares to the original Llama-3.1-70B. 🤔🔎
Try it out here 👉 open-llm-leaderboard/comparator
> The Open LLM Leaderboard, most comprehensive suite for comparing Open LLMs on many benchmarks, just released a comparator tool that lets you dig into the detail of differences between any models.
Here's me checking how the new Llama-3.1-Nemotron-70B that we've heard so much compares to the original Llama-3.1-70B. 🤔🔎
Try it out here 👉 open-llm-leaderboard/comparator
- 2 replies
posted
an
update
25 days ago
Post
663
The Synthetic Data Generator now directly integrates with Argilla, so you can generate and curate your own high-quality datasets from pure natural language!
Up next -> include dataset generation for text classification.
Other suggestions? Let us know.
Space: argilla/synthetic-data-generator
Up next -> include dataset generation for text classification.
Other suggestions? Let us know.
Space: argilla/synthetic-data-generator
posted
an
update
25 days ago
Post
2491
Don't use an LLM when you can use a much cheaper model.
The problem is that no one tells you how to actually do it.
Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!
If you want a small model to perform well on your problem, you need to fine-tune it.
And to fine-tune it, you need data.
The good news is that you don't need a lot of data but instead high-quality data for your specific problem.
In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.
https://www.youtube.com/watch?v=BEe7shiG3rY
The problem is that no one tells you how to actually do it.
Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!
If you want a small model to perform well on your problem, you need to fine-tune it.
And to fine-tune it, you need data.
The good news is that you don't need a lot of data but instead high-quality data for your specific problem.
In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.
https://www.youtube.com/watch?v=BEe7shiG3rY
posted
an
update
about 1 month ago
Post
1208
Thursday 10 October 17:00 CEST, I will show a good way to get started with a text classification project on the Hugging Face Hub with Argilla and Setfit.
Signup here: https://lu.ma/31mecp34
Signup here: https://lu.ma/31mecp34
reacted to
their
post with 🔥🤗
about 1 month ago
Post
1128
Why is argilla/FinePersonas-v0.1 great for synthetic data generation? It can be used to synthesise realistic and diverse data of the customer personas your company is interested in!
Dataset: argilla/FinePersonas-v0.1
Example usage: https://distilabel.argilla.io/dev/sections/pipeline_samples/examples/fine_personas_social_network/
Dataset: argilla/FinePersonas-v0.1
Example usage: https://distilabel.argilla.io/dev/sections/pipeline_samples/examples/fine_personas_social_network/
- 1 reply
posted
an
update
about 1 month ago
Post
1128
Why is argilla/FinePersonas-v0.1 great for synthetic data generation? It can be used to synthesise realistic and diverse data of the customer personas your company is interested in!
Dataset: argilla/FinePersonas-v0.1
Example usage: https://distilabel.argilla.io/dev/sections/pipeline_samples/examples/fine_personas_social_network/
Dataset: argilla/FinePersonas-v0.1
Example usage: https://distilabel.argilla.io/dev/sections/pipeline_samples/examples/fine_personas_social_network/
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- 1 reply
posted
an
update
about 1 month ago
Post
847
We've got a number of great community meetups coming up again where we'll be discussing the basics of getting started and using Argilla for TextCat, TokenCat/NER and RAG. We will walk you through common scenario's and everything you might need to know to get your projects started.
First meetup that is coming up: Setting up a text classification project using Argilla and SetFit!
Deploy Argilla on Spaces
Vibe check your dataset
Configure and create an Argilla dataset
Add records
Add zero-shot suggestions
Evaluate model suggestions in Argilla
Train a SetFit model
Hope to see all of you guys there and looking forward to your questions and AI use cases. Don't be shy about bringing your own issues and questions to the table. We would love to answer them.
Sign up here: https://lu.ma/31mecp34
First meetup that is coming up: Setting up a text classification project using Argilla and SetFit!
Deploy Argilla on Spaces
Vibe check your dataset
Configure and create an Argilla dataset
Add records
Add zero-shot suggestions
Evaluate model suggestions in Argilla
Train a SetFit model
Hope to see all of you guys there and looking forward to your questions and AI use cases. Don't be shy about bringing your own issues and questions to the table. We would love to answer them.
Sign up here: https://lu.ma/31mecp34
reacted to
Jaward's
post with 🔥
about 1 month ago
Post
1933
This is supercool!!
LlaVA-3D: adds 3D-awareness to LVMs without compromising 2D understanding capabilities.
Method: they developed a unified architecture that maps 2D clip patch features to their corresponding positions in 3D space - enabling joint 2D and 3D vision-language instruction tuning.
Project: https://zcmax.github.io/projects/LLaVA-3D/
LlaVA-3D: adds 3D-awareness to LVMs without compromising 2D understanding capabilities.
Method: they developed a unified architecture that maps 2D clip patch features to their corresponding positions in 3D space - enabling joint 2D and 3D vision-language instruction tuning.
Project: https://zcmax.github.io/projects/LLaVA-3D/