Datasets:
modelId stringlengths 4 122 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 74.7M | likes int64 0 9.67k | library_name stringlengths 2 84 ⌀ | tags sequence | pipeline_tag stringlengths 5 30 ⌀ | createdAt timestamp[us, tz=UTC] | card stringlengths 1 901k | embedding sequence |
|---|---|---|---|---|---|---|---|---|---|---|
jonatasgrosman/wav2vec2-large-xlsr-53-english | jonatasgrosman | 2023-03-25T10:56:55 | 74,653,058 | 317 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"en",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"dataset:mozilla-foundation/common_vo... | automatic-speech-recognition | 2022-03-02T23:29:05 | ---
language: en
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- en
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 ... | [
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0.01539389509707... |
bert-base-uncased | null | 2023-06-30T01:42:19 | 55,618,661 | 1,219 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"coreml",
"onnx",
"safetensors",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | fill-mask | 2022-03-02T23:29:04 | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](http... | [
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0.14166030287742... |
openai/clip-vit-large-patch14 | openai | 2023-09-15T15:49:35 | 31,237,964 | 734 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"clip",
"zero-shot-image-classification",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"endpoints_compatible",
"has_space",
"region:us"
] | zero-shot-image-classification | 2022-03-02T23:29:05 | ---
tags:
- vision
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found ... | [
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0.120856180787... |
distilbert-base-uncased-finetuned-sst-2-english | null | 2023-10-26T16:14:11 | 30,654,240 | 355 | transformers | [
"transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | text-classification | 2022-03-02T23:29:04 | ---
language: en
license: apache-2.0
datasets:
- sst2
- glue
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: validation
metrics:
... | [
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gpt2 | null | 2023-06-30T02:19:43 | 21,850,807 | 1,516 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"doi:10.57967/hf/0039",
"license:mit",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:04 | ---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better... | [
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timm/mobilenetv3_large_100.ra_in1k | timm | 2023-04-27T22:49:21 | 15,846,252 | 17 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.00476",
"arxiv:1905.02244",
"license:apache-2.0",
"has_space",
"region:us"
] | image-classification | 2022-12-16T05:38:07 | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for mobilenetv3_large_100.ra_in1k
A MobileNet-v3 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* RandAugment `RA` recipe. Inspi... | [
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distilgpt2 | null | 2023-04-29T12:24:21 | 14,735,848 | 275 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"exbert",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"license:apache-2.0",
"model-... | text-generation | 2022-03-02T23:29:04 | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- openwebtext
model-index:
- name: distilgpt2
results:
- task:
type: text-generation
name: Text Generation
dataset:
type: wikitext
name: WikiText-103
metrics:
- type: perplexity
name: Perplexity
... | [
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roberta-base | null | 2023-03-06T15:14:53 | 14,521,862 | 247 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:1806.02847",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | fill-mask | 2022-03-02T23:29:04 | ---
language: en
tags:
- exbert
license: mit
datasets:
- bookcorpus
- wikipedia
---
# RoBERTa base model
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com... | [
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stabilityai/stable-diffusion-xl-base-1.0 | stabilityai | 2023-10-30T16:03:47 | 10,649,877 | 3,677 | diffusers | ["diffusers","onnx","text-to-image","stable-diffusion","arxiv:2307.01952","arxiv:2211.01324","arxiv:(...TRUNCATED) | text-to-image | 2023-07-25T13:25:51 | "---\nlicense: openrail++\ntags:\n- text-to-image\n- stable-diffusion\n---\n# SD-XL 1.0-base Model C(...TRUNCATED) | [-0.39037227630615234,-0.807611346244812,0.49455687403678894,0.12964530289173126,-0.1020255982875824(...TRUNCATED) |
distilbert-base-uncased | null | 2023-08-18T14:59:41 | 10,387,599 | 308 | transformers | ["transformers","pytorch","tf","jax","rust","safetensors","distilbert","fill-mask","exbert","en","da(...TRUNCATED) | fill-mask | 2022-03-02T23:29:04 | "---\nlanguage: en\ntags:\n- exbert\nlicense: apache-2.0\ndatasets:\n- bookcorpus\n- wikipedia\n---\(...TRUNCATED) | [-0.05776984989643097,-0.6613970994949341,0.2539806365966797,0.28165116906166077,-0.55666583776474,0(...TRUNCATED) |
Dataset Card for Hugging Face Hub Model Cards with Embeddings
This dataset consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
This dataset is the same as the Hugging Face Hub Model Cards dataset but with the addition of embeddings for each model card. The embeddings are generated using the jinaai/jina-embeddings-v2-base-en model.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
- build a recommender system for model cards
- build a search engine for model cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with model cards. In particular. it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
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