Welcome Gemma 2 - Google's new open LLM
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123
Tom Aarsen
tomaarsen
AI & ML interests
NLP: text embeddings, information retrieval, named entity recognition, few-shot text classification
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sentence-transformers/all-mpnet-base-v2
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tomaarsen's activity
posted
an
update
10 days ago
Post
4743
I just released Sentence Transformers v3.3.0 & it's huge! 4.5x speedup for CPU with OpenVINO int8 static quantization, training with prompts for a free perf. boost, PEFT integration, evaluation on NanoBEIR, and more! Details:
1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new
2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the
3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.
4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.
Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0
1. We integrate Post-Training Static Quantization using OpenVINO, a very efficient solution for CPUs that processes 4.78x as many texts per second on average, while only hurting performance by 0.36% on average. There's a new
export_static_quantized_openvino_model method to quantize a model.2. We add the option to train with prompts, e.g. strings like "query: ", "search_document: " or "Represent this sentence for searching relevant passages: ". It's as simple as using the
prompts argument in SentenceTransformerTrainingArguments. Our experiments show that you can easily reach 0.66% to 0.90% relative performance improvement on NDCG@10 at no extra cost by adding "query: " before each training query and "document: " before each training answer.3. Sentence Transformers now supports training PEFT adapters via 7 new methods for adding new adapters or loading pre-trained ones. You can also directly load a trained adapter with SentenceTransformer as if it's a normal model. Very useful for e.g. 1) training multiple adapters on 1 base model, 2) training bigger models than otherwise possible, or 3) cheaply hosting multiple models by switching multiple adapters on 1 base model.
4. We added easy evaluation on NanoBEIR, a subset of BEIR a.k.a. the MTEB Retrieval benchmark. It contains 13 datasets with 50 queries and up to 10k documents each. Evaluation is fast, and can easily be done during training to track your model's performance on general-purpose information retrieval tasks.
Additionally, we also deprecate Python 3.8, add better compatibility with Transformers v4.46.0, and more. Read the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.3.0
posted
an
update
about 1 month ago
Post
6312
📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference.
2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as
SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with
from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed.2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Full release notes: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.2.0
Documentation on Speeding up Inference: https://sbert.net/docs/sentence_transformer/usage/efficiency.html
- 1 reply
reacted to
louisbrulenaudet's
post with ❤️
about 2 months ago
Post
2081
My biggest release of the year: a series of 7 specialized embedding models for information retrieval within tax documents, is now available for free on Hugging Face 🤗
These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.
Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by @intfloat ), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.
A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to @julien-c , @clem 🤗, @thomwolf and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to @tomaarsen for his invaluable advice on training embedding models and Loss functions ❤️
Models are available on my personal HF page, into the Lemone-embed collection: louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
These new models aim to offer an open source alternative for in-domain semantic search from large text corpora and will improve RAG systems and context addition for large language models.
Trained on more than 43 million tax tokens derived from semi-synthetic and raw-synthetic data, enriched by various methods (in particular MSFT's evol-instruct by @intfloat ), and corrected by humans, this project is the fruit of hundreds of hours of work and is the culmination of a global effort to open up legal technologies that has only just begun.
A big thank you to Microsoft for Startups for giving me access to state-of-the-art infrastructure to train these models, and to @julien-c , @clem 🤗, @thomwolf and the whole HF team for the inference endpoint API and the generous provision of Meta LLama-3.1-70B. Special thanks also to @tomaarsen for his invaluable advice on training embedding models and Loss functions ❤️
Models are available on my personal HF page, into the Lemone-embed collection: louisbrulenaudet/lemone-embed-66fdc24000df732b395df29b
- 1 reply
reacted to
MoritzLaurer's
post with ❤️
about 2 months ago
Post
4032
#phdone - I defended my PhD yesterday! A key lesson: it is amazing how open science and open source can empower beginners with limited resources:
I first learned about instruction-based classifiers like BERT-NLI 3-4 years ago, through the @HuggingFace ZeroShotClassificationPipeline. Digging deeper into this, it was surprisingly easy to find new datasets, newer base models, and reusable fine-tuning scripts on the HF Hub to create my own zeroshot models - although I didn't know much about fine-tuning at the time.
Thanks to the community effect of the Hub, my models were downloaded hundreds of thousands of times after a few months. Seeing my research being useful for people motivated me to improve and upload newer models. Leaving my contact details in the model cards led to academic cooperation and consulting contracts (and eventually my job at HF).
That's the power of open science & open source: learning, sharing, improving, collaborating.
I mean every word in my thesis acknowledgments (screenshot). I'm very grateful to my supervisors @vanatteveldt @CasAndreu @KasperWelbers for their guidance; to @profAndreaRenda and @CEPS_thinktank for enabling me to work part-time during the first year; to @huggingface for creating awesome tools and an awesome platform; and to many others who are not active on social media.
Links to the full thesis and the collection of my most recent models are below.
PS: If someone happens to speak Latin, let me know if my diploma contains some hidden Illuminati code or something :D
I first learned about instruction-based classifiers like BERT-NLI 3-4 years ago, through the @HuggingFace ZeroShotClassificationPipeline. Digging deeper into this, it was surprisingly easy to find new datasets, newer base models, and reusable fine-tuning scripts on the HF Hub to create my own zeroshot models - although I didn't know much about fine-tuning at the time.
Thanks to the community effect of the Hub, my models were downloaded hundreds of thousands of times after a few months. Seeing my research being useful for people motivated me to improve and upload newer models. Leaving my contact details in the model cards led to academic cooperation and consulting contracts (and eventually my job at HF).
That's the power of open science & open source: learning, sharing, improving, collaborating.
I mean every word in my thesis acknowledgments (screenshot). I'm very grateful to my supervisors @vanatteveldt @CasAndreu @KasperWelbers for their guidance; to @profAndreaRenda and @CEPS_thinktank for enabling me to work part-time during the first year; to @huggingface for creating awesome tools and an awesome platform; and to many others who are not active on social media.
Links to the full thesis and the collection of my most recent models are below.
PS: If someone happens to speak Latin, let me know if my diploma contains some hidden Illuminati code or something :D
- 4 replies
posted
an
update
2 months ago
Post
1981
I've just shipped the Sentence Transformers v3.1.1 patch release, fixing the hard negatives mining utility for some models. This utility is extremely useful to get more performance out of your embedding training data.
⛏ Hard negatives are texts that are rather similar to some anchor text (e.g. a query), but are not the correct match. They're difficult for a model to distinguish from the correct answer, often resulting in a stronger model after training.
🔓 Beyond that, this release removes the numpy<2 restriction from v3.1.0. This was previously required for Windows as not all third-party libraries were updated to support numpy v2. With Sentence Transformers, you can now choose v1 or v2 of numpy.
Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.1.1
I'm looking forward to releasing v3.2, I have some exciting things planned 🚀
⛏ Hard negatives are texts that are rather similar to some anchor text (e.g. a query), but are not the correct match. They're difficult for a model to distinguish from the correct answer, often resulting in a stronger model after training.
mine_hard_negatives docs: https://sbert.net/docs/package_reference/util.html#sentence_transformers.util.mine_hard_negatives🔓 Beyond that, this release removes the numpy<2 restriction from v3.1.0. This was previously required for Windows as not all third-party libraries were updated to support numpy v2. With Sentence Transformers, you can now choose v1 or v2 of numpy.
Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.1.1
I'm looking forward to releasing v3.2, I have some exciting things planned 🚀
posted
an
update
2 months ago
Post
2023
🎉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.
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.
replied to
their
post
2 months ago
Glad to hear it! Feel free to send over feedback if you have any, it's always quite valuable for new features/docs.
posted
an
update
2 months ago
Post
3699
🚀 Sentence Transformers v3.1 is out! Featuring a hard negatives mining utility to get better models out of your data, a new strong loss function, training with streaming datasets, custom modules, bug fixes, small additions and docs changes. Here's the details:
⛏ Hard Negatives Mining Utility: Hard negatives are texts that are rather similar to some anchor text (e.g. a question), but are not the correct match. They're difficult for a model to distinguish from the correct answer, often resulting in a stronger model after training.
📉 New loss function: This loss function works very well for symmetric tasks (e.g. clustering, classification, finding similar texts/paraphrases) and a bit less so for asymmetric tasks (e.g. question-answer retrieval).
💾 Streaming datasets: You can now train with the datasets.IterableDataset, which doesn't require downloading the full dataset to disk before training. As simple as "streaming=True" in your "datasets.load_dataset".
🧩 Custom Modules: Model authors can now customize a lot more of the components that make up Sentence Transformer models, allowing for a lot more flexibility (e.g. multi-modal, model-specific quirks, etc.)
✨ New arguments to several methods: encode_multi_process gets a progress bar, push_to_hub can now be done to different branches, and CrossEncoders can be downloaded to specific cache directories.
🐛 Bug fixes: Too many to name here, check out the release notes!
📝 Documentation: A particular focus on clarifying the batch samplers in the Package Reference this release.
Check out the full release notes here ⭐: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.1.0
I'm very excited to hear your feedback, and I'm looking forward to the future changes that I have planned, such as ONNX inference! I'm also open to suggestions for new features: feel free to send me your ideas.
⛏ Hard Negatives Mining Utility: Hard negatives are texts that are rather similar to some anchor text (e.g. a question), but are not the correct match. They're difficult for a model to distinguish from the correct answer, often resulting in a stronger model after training.
📉 New loss function: This loss function works very well for symmetric tasks (e.g. clustering, classification, finding similar texts/paraphrases) and a bit less so for asymmetric tasks (e.g. question-answer retrieval).
💾 Streaming datasets: You can now train with the datasets.IterableDataset, which doesn't require downloading the full dataset to disk before training. As simple as "streaming=True" in your "datasets.load_dataset".
🧩 Custom Modules: Model authors can now customize a lot more of the components that make up Sentence Transformer models, allowing for a lot more flexibility (e.g. multi-modal, model-specific quirks, etc.)
✨ New arguments to several methods: encode_multi_process gets a progress bar, push_to_hub can now be done to different branches, and CrossEncoders can be downloaded to specific cache directories.
🐛 Bug fixes: Too many to name here, check out the release notes!
📝 Documentation: A particular focus on clarifying the batch samplers in the Package Reference this release.
Check out the full release notes here ⭐: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.1.0
I'm very excited to hear your feedback, and I'm looking forward to the future changes that I have planned, such as ONNX inference! I'm also open to suggestions for new features: feel free to send me your ideas.
-
- 2 replies
reacted to
MonsterMMORPG's
post with 🚀❤️🔥
4 months ago
Post
5199
FLUX Local & Cloud Tutorial With SwarmUI - FLUX: The Groundbreaking Open Source txt2img Model Outperforms Midjourney & Others - FLUX: The Anticipated Successor to SD3
🔗 Comprehensive Tutorial Video Link ▶️ https://youtu.be/bupRePUOA18
FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. This statement is not hyperbole; you'll witness its capabilities in the tutorial. This guide will demonstrate how to effortlessly install and utilize FLUX models on your personal computer and cloud platforms like Massed Compute, RunPod, and a complimentary Kaggle account.
🔗 FLUX Setup Guide (publicly accessible) ⤵️
▶️ https://www.patreon.com/posts/106135985
🔗 FLUX Models One-Click Robust Automatic Downloader Scripts ⤵️
▶️ https://www.patreon.com/posts/109289967
🔗 Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) ⤵️
▶️ https://youtu.be/HKX8_F1Er_w
🔗 Cloud-based SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵️
▶️ https://youtu.be/XFUZof6Skkw
🔗 SECourses Discord Server for Comprehensive Support ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 SECourses Reddit Community ⤵️
▶️ https://www.reddit.com/r/SECourses/
🔗 SECourses GitHub Repository ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
🔗 Official FLUX 1 Launch Announcement Blog Post ⤵️
▶️ https://blackforestlabs.ai/announcing-black-forest-labs/
Video Segments
0:00 Introduction to the state-of-the-art open source txt2img model FLUX
5:01 Process for integrating FLUX model into SwarmUI
....
🔗 Comprehensive Tutorial Video Link ▶️ https://youtu.be/bupRePUOA18
FLUX represents a milestone in open source txt2img technology, delivering superior quality and more accurate prompt adherence than #Midjourney, Adobe Firefly, Leonardo Ai, Playground Ai, Stable Diffusion, SDXL, SD3, and Dall E3. #FLUX, a creation of Black Forest Labs, boasts a team largely comprised of #StableDiffusion's original developers, and its output quality is truly remarkable. This statement is not hyperbole; you'll witness its capabilities in the tutorial. This guide will demonstrate how to effortlessly install and utilize FLUX models on your personal computer and cloud platforms like Massed Compute, RunPod, and a complimentary Kaggle account.
🔗 FLUX Setup Guide (publicly accessible) ⤵️
▶️ https://www.patreon.com/posts/106135985
🔗 FLUX Models One-Click Robust Automatic Downloader Scripts ⤵️
▶️ https://www.patreon.com/posts/109289967
🔗 Primary Windows SwarmUI Tutorial (Essential for Usage Instructions) ⤵️
▶️ https://youtu.be/HKX8_F1Er_w
🔗 Cloud-based SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵️
▶️ https://youtu.be/XFUZof6Skkw
🔗 SECourses Discord Server for Comprehensive Support ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 SECourses Reddit Community ⤵️
▶️ https://www.reddit.com/r/SECourses/
🔗 SECourses GitHub Repository ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
🔗 Official FLUX 1 Launch Announcement Blog Post ⤵️
▶️ https://blackforestlabs.ai/announcing-black-forest-labs/
Video Segments
0:00 Introduction to the state-of-the-art open source txt2img model FLUX
5:01 Process for integrating FLUX model into SwarmUI
....
reacted to
samjulien's
post with 🔥
4 months ago
Post
1893
🔥 Today, Writer dropped Palmyra-Med-70b and Palmyra-Fin-70b, two new domain-specific models that are setting a new standard for medical and financial model performance.
TL;DR
Palmyra-Med-70b
🔢 8k and 32k versions available
🚀 MMLU performance of ~86%, outperforming other top models
👨⚕️ Great for diagnosing, planning treatments, medical research, insurance coding and billing
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Med-70B
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b
Palmyra-Fin-70b
🚀 Passed the CFA Level III exam with a 73% score — the first model to do so
💸 Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
📈 Outperformed other top models on a long-fin-eval test of real-world use cases
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Fin-70B-32K
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k
Try them out and let us know what you think!
TL;DR
Palmyra-Med-70b
🔢 8k and 32k versions available
🚀 MMLU performance of ~86%, outperforming other top models
👨⚕️ Great for diagnosing, planning treatments, medical research, insurance coding and billing
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Med-70B
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-med-70b
Palmyra-Fin-70b
🚀 Passed the CFA Level III exam with a 73% score — the first model to do so
💸 Skilled at complex tasks like investment research, financial analysis, and sentiment analysis
📈 Outperformed other top models on a long-fin-eval test of real-world use cases
📃 Open-model license for non-commercial use cases
🤗 Available on Hugging Face: Writer/Palmyra-Fin-70B-32K
💾 Live on NVIDIA NIM: https://build.nvidia.com/writer/palmyra-fin-70b-32k
Try them out and let us know what you think!
-
- 2 replies
reacted to
nthakur's
post with 🔥
4 months ago
Post
3253
🦢 The SWIM-IR dataset contains 29 million text-retrieval training pairs across 27 diverse languages. It is one of the largest synthetic multilingual datasets generated using PaLM 2 on Wikipedia! 🔥🔥
SWIM-IR dataset contains three subsets :
- Cross-lingual:
- Monolingual:
- Indic Cross-lingual:
Check it out:
nthakur/swim-ir-dataset-662ddaecfc20896bf14dd9b7
SWIM-IR dataset contains three subsets :
- Cross-lingual:
nthakur/swim-ir-cross-lingual- Monolingual:
nthakur/swim-ir-monolingual- Indic Cross-lingual:
nthakur/indic-swim-ir-cross-lingualCheck it out:
nthakur/swim-ir-dataset-662ddaecfc20896bf14dd9b7
reacted to
louisbrulenaudet's
post with ❤️
5 months ago
Post
2110
Introducing the first two projects on the HFforLegal community: the 'Laws' dataset and the associated search tool based on
@nreimers
and
@tomaarsen
's Sentence Transformers library 🤗
The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions 🌍
Link to the dataset : HFforLegal/laws
Link to the space: HFforLegal/laws-retrieval
We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split.
The objective of these two tools is to centralize in a single format a set of rules from different countries and legal systems in order to facilitate NLP in the field of comparative law, enabling more accurate and comprehensive legal analysis across different jurisdictions 🌍
Link to the dataset : HFforLegal/laws
Link to the space: HFforLegal/laws-retrieval
We need your contributions to enrich this new knowledge base, and you will find in the 'Laws' dataset all the information you need to format your data and submit them to the appropriate split.
posted
an
update
5 months ago
Post
3894
@Omartificial-Intelligence-Space
has trained and released 6 Arabic embedding models for semantic similarity. 4 of them outperform all previous models on the STS17 Arabic-Arabic task!
📚 Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6️⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like intfloat/multilingual-e5-large, sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and sentence-transformers/LaBSE.
Check them out here:
- Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
- Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
- Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all: Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on mteb/leaderboard.
📚 Trained on a large dataset of 558k Arabic triplets translated from the AllNLI triplet dataset: Omartificial-Intelligence-Space/Arabic-NLi-Triplet
6️⃣ 6 different base models: AraBERT, MarBERT, LaBSE, MiniLM, paraphrase-multilingual-mpnet-base, mpnet-base, ranging from 109M to 471M parameters.
🪆 Trained with a Matryoshka loss, allowing you to truncate embeddings with minimal performance loss: smaller embeddings are faster to compare.
📈 Outperforms all commonly used multilingual models like intfloat/multilingual-e5-large, sentence-transformers/paraphrase-multilingual-mpnet-base-v2, and sentence-transformers/LaBSE.
Check them out here:
- Omartificial-Intelligence-Space/Arabic-mpnet-base-all-nli-triplet
- Omartificial-Intelligence-Space/Arabic-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabic-labse-Matryoshka
- Omartificial-Intelligence-Space/Marbert-all-nli-triplet-Matryoshka
- Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
Or the collection with all: Omartificial-Intelligence-Space/arabic-matryoshka-embedding-models-666f764d3b570f44d7f77d4e
My personal favourite is likely Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka: a very efficient 135M parameters & scores #1 on mteb/leaderboard.
- 1 reply
reacted to
reach-vb's
post with 🔥
5 months ago
Post
5259
Yet another rewarding week in Open Source AI:
1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
google/gemma-2-release-667d6600fd5220e7b967f315
2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
CAMB-AI/MARS5-TTS
3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
facebook/llm-compiler-667c5b05557fe99a9edd25cb
4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
arcee-ai/Arcee-Spark
5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)
6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
fal/AuraSR
7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx
And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!
What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥
1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there, according to LYMSYS.
google/gemma-2-release-667d6600fd5220e7b967f315
2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning.
CAMB-AI/MARS5-TTS
3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning.
facebook/llm-compiler-667c5b05557fe99a9edd25cb
4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench.
arcee-ai/Arcee-Spark
5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline)
6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking)
fal/AuraSR
7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size
https://huggingface.co/nyu-visionx
And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT!
What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥
- 1 reply
reacted to
louisbrulenaudet's
post with 🔥
5 months ago
Post
2933
Announcing the creation of the "HF for Legal" organization, an open-source community dedicated to demystifying language models for legal professionals 🤗
Whether you're a practicing attorney, a legal scholar, or a technologist interested in legal applications of AI, HF for Legal may be your hub for exploration, learning, and free innovation ⚗️
On the occasion of this launch, you'll be able to find several notebooks I've been developing over the last few months for TSDAE pre-training of embedding models, the generation of indexes for semantic search, based on the formidable work of @tomaarsen and @nreimers , adapted to the field of French law, or the addition of information retrieval tasks to the MTEB.
Join us in our mission to make AI more accessible and understandable for the legal world, ensuring that the power of language models can be harnessed effectively and ethically.
Link to the org: https://huggingface.co/HFforLegal
Special thanks to @clem for encouraging me to start this organization. Let's hope we can bring together all the enthusiasts who work in this field.
Let's code and share together! 🚀🔗
Whether you're a practicing attorney, a legal scholar, or a technologist interested in legal applications of AI, HF for Legal may be your hub for exploration, learning, and free innovation ⚗️
On the occasion of this launch, you'll be able to find several notebooks I've been developing over the last few months for TSDAE pre-training of embedding models, the generation of indexes for semantic search, based on the formidable work of @tomaarsen and @nreimers , adapted to the field of French law, or the addition of information retrieval tasks to the MTEB.
Join us in our mission to make AI more accessible and understandable for the legal world, ensuring that the power of language models can be harnessed effectively and ethically.
Link to the org: https://huggingface.co/HFforLegal
Special thanks to @clem for encouraging me to start this organization. Let's hope we can bring together all the enthusiasts who work in this field.
Let's code and share together! 🚀🔗
reacted to
lbourdois's
post with ❤️
5 months ago
Post
The most widely used French NER models on HF (
Jean-Baptiste/camembert-ner and
cmarkea/distilcamembert-base-ner) are trained on a single dataset (WikiNER) which on the one hand contains leaks and therefore distorts the true results of these models, and on the other hand overspecializes them in a particular domain (= texts from Wikipedia). They are also only available in a base version (110M parameters).
That's why I've trained new NER models in French both on more data (x3), as well as in base and large versions (336M). They are available in 3 entities (PER, ORG, LOC) or 4 entities (PER, ORG, LOC, MISC):
- CATIE-AQ/NERmembert-base-4entities
- CATIE-AQ/NERmembert-large-4entities
- CATIE-AQ/NERmembert-base-3entities
- CATIE-AQ/NERmembert-large-3entities
Datasets without leaks are also available:
- CATIE-AQ/frenchNER_4entities
- CATIE-AQ/frenchNER_3entities
That's why I've trained new NER models in French both on more data (x3), as well as in base and large versions (336M). They are available in 3 entities (PER, ORG, LOC) or 4 entities (PER, ORG, LOC, MISC):
- CATIE-AQ/NERmembert-base-4entities
- CATIE-AQ/NERmembert-large-4entities
- CATIE-AQ/NERmembert-base-3entities
- CATIE-AQ/NERmembert-large-3entities
Datasets without leaks are also available:
- CATIE-AQ/frenchNER_4entities
- CATIE-AQ/frenchNER_3entities
reacted to
dvilasuero's
post with 🤗🚀
5 months ago
Post
7941
Today is a huge day in Argilla’s history. We couldn’t be more excited to share this with the community: we’re joining Hugging Face!
We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.
Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets
After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.
To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.
As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.
Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.
Would love to answer any questions you have so feel free to add them below!
We’re embracing a larger mission, becoming part of a brilliant and kind team and a shared vision about the future of AI.
Over the past year, we’ve been collaborating with Hugging Face on countless projects: launching partner of Docker Spaces, empowering the community to clean Alpaca translations into Spanish and other languages, launching argilla/notus-7b-v1 building on Zephyr’s learnings, the Data is Better Together initiative with hundreds of community contributors, or releasing argilla/OpenHermesPreferences, one of the largest open preference tuning datasets
After more than 2,000 Slack messages and over 60 people collaborating for over a year, it already felt like we were part of the same team, pushing in the same direction. After a week of the smoothest transition you can imagine, we’re now the same team.
To those of you who’ve been following us, this won’t be a huge surprise, but it will be a big deal in the coming months. This acquisition means we’ll double down on empowering the community to build and collaborate on high quality datasets, we’ll bring full support for multimodal datasets, and we’ll be in a better place to collaborate with the Open Source AI community. For enterprises, this means that the Enterprise Hub will unlock highly requested features like single sign-on and integration with Inference Endpoints.
As a founder, I am proud of the Argilla team. We're now part of something bigger and a larger team but with the same values, culture, and goals. Grateful to have shared this journey with my beloved co-founders Paco and Amélie.
Finally, huge thanks to the Chief Llama Officer @osanseviero for sparking this and being such a great partner during the acquisition process.
Would love to answer any questions you have so feel free to add them below!
- 28 replies