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Tabular Regression
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.
Use Cases
Sales Prediction: a Use Case for Predicting a Continuous Target Variable
Here the objective is to predict a continuous variable based on a set of input variable(s).
https://huggingface.co/tasks/tabular-regression
Tabular regression is the task of predicting a numerical value given a set of attributes.
About Tabular Regression
About the Task
Tabular regression is the task of predicting a numerical value given a set of attributes/features. Tabular meaning that data is stored in a table (like an excel sheet), and each sample is contained in its own row. The features used to predict our target can be both numerical and categorical. However, including categorical features often requires additional preprocessing/feature engineering (a few models do accept categorical features directly, like CatBoost). An example of tabular regression would be predicting the weight of a fish given its' species and length.
Use Cases
Sales Prediction: a Use Case for Predicting a Continuous Target Variable
Here the objective is to predict a continuous variable based on a set of input variable(s).
https://huggingface.co/tasks/tabular-regression
Priority Support-Enterprise-ready version of the world’s leading AI platform
https://posts.aidevin.dev/posts/612
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https://posts.aidevin.dev/posts/612
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https://posts.aidevin.dev/posts/611
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https://posts.aidevin.dev/posts/611
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Use our optimized Docker containers (TGI) for maximum performance and security.
Give your organization the most advanced platform to build AI with enterprise-grade security, access controls, dedicated support and more.
https://posts.aidevin.dev/posts/610
Use our optimized Docker containers (TGI) for maximum performance and security.
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Increase scalability and performance with more compute options like ZeroGPU for Spaces.
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XLM-RoBERTa (large-sized model)
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.
Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.
RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
https://huggingface.co/FacebookAI/xlm-roberta-large
XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.
Disclaimer: The team releasing XLM-RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.
RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
https://huggingface.co/FacebookAI/xlm-roberta-large
huggingface.co
FacebookAI/xlm-roberta-large · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Amazon/Chronos-T5-Tiny
Chronos-T5 (Tiny)
Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
For details on Chronos models, training data and procedures, and experimental results, please refer to the paper Chronos: Learning the Language of Time Series.
Chronos-T5 (Tiny)
Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.
For details on Chronos models, training data and procedures, and experimental results, please refer to the paper Chronos: Learning the Language of Time Series.
Openai/clip-vit-large-patch14
Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
https://huggingface.co/openai/clip-vit-large-patch14
Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
https://huggingface.co/openai/clip-vit-large-patch14
huggingface.co
openai/clip-vit-large-patch14 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Sentence-transformers/all-MiniLM-L6-v2
all-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
all-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
huggingface.co
sentence-transformers/all-MiniLM-L6-v2 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.https://huggingface.co/google-bert/bert-base-uncased
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.https://huggingface.co/google-bert/bert-base-uncased
huggingface.co
google-bert/bert-base-uncased · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
fastText (Language Identification)
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in this paper. The official website can be found here.
This LID (Language IDentification) model is used to predict the language of the input text, and the hosted version (lid218e) was released as part of the NLLB project and can detect 217 languages. You can find older versions (ones that can identify 157 languages) on the official fastText website.
Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.https://huggingface.co/facebook/fasttext-language-identification
fastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. It was introduced in this paper. The official website can be found here.
This LID (Language IDentification) model is used to predict the language of the input text, and the hosted version (lid218e) was released as part of the NLLB project and can detect 217 languages. You can find older versions (ones that can identify 157 languages) on the official fastText website.
Model description
fastText is a library for efficient learning of word representations and sentence classification. fastText is designed to be simple to use for developers, domain experts, and students. It's dedicated to text classification and learning word representations, and was designed to allow for quick model iteration and refinement without specialized hardware. fastText models can be trained on more than a billion words on any multicore CPU in less than a few minutes.
It includes pre-trained models learned on Wikipedia and in over 157 different languages. fastText can be used as a command line, linked to a C++ application, or used as a library for use cases from experimentation and prototyping to production.https://huggingface.co/facebook/fasttext-language-identification
huggingface.co
facebook/fasttext-language-identification · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to Nvidia.
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Deep Residual Learning for Image Recognition by He et al.
Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models.
This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to Nvidia.
Audio Spectrogram Transformer (fine-tuned on AudioSet)
Audio Spectrogram Transformer (AST) model fine-tuned on AudioSet. It was introduced in the paper AST: Audio Spectrogram Transformer by Gong et al. and first released in this repository.
Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
The Audio Spectrogram Transformer is equivalent to ViT, but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.
Usage
You can use the raw model for classifying audio into one of the AudioSet classes. See the documentation for more info.
https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593
Audio Spectrogram Transformer (AST) model fine-tuned on AudioSet. It was introduced in the paper AST: Audio Spectrogram Transformer by Gong et al. and first released in this repository.
Disclaimer: The team releasing Audio Spectrogram Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
The Audio Spectrogram Transformer is equivalent to ViT, but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.
Usage
You can use the raw model for classifying audio into one of the AudioSet classes. See the documentation for more info.
https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593
huggingface.co
MIT/ast-finetuned-audioset-10-10-0.4593 · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Releasing HQQ Llama-3.1-70b 4-bit quantized version! Check it out at
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
.
Achieves 99% of the base model performance across various benchmarks! Details in the model card.https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
.
Achieves 99% of the base model performance across various benchmarks! Details in the model card.https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
huggingface.co
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hey HF. I just released a new reward modelling dataset:
Avelina/UltraSteer-v0
UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's
nvidia/Llama2-13B-SteerLM-RM
reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.
This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.
I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.https://huggingface.co/datasets/Avelina/UltraSteer-v0
Avelina/UltraSteer-v0
UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's
nvidia/Llama2-13B-SteerLM-RM
reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.
This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.
I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.https://huggingface.co/datasets/Avelina/UltraSteer-v0
huggingface.co
Avelina/UltraSteer-v0 · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.