license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Cross Lingual Cross Domain You can **try out the model** at [SGNLP](https://sgnlp.aisingapore.net/cross-lingual-cross-domain).<br /> If you want to find out more information, please contact us at [SGNLP-AISingapore](sg-nlp@aisingapore.org). | 1fb8d4635cd0a4f9fcac24a886048eaf |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Model Details **Model Name:** Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language - **Description:** It is an implementation of Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model paper. - **Paper:** Unsupervised domain adaptation of a pretrained cross-lingual language model. ... | c6faf8e65633b32e088f16c0e6fd9489 |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Install Python package SGnlp is an initiative by AI Singapore's NLP Hub. They aim to bridge the gap between research and industry, promote translational research, and encourage adoption of NLP techniques in the industry. <br><br> Various NLP models, other than cross lingual cross domain are available in the python pa... | c182bcc7c0fa7955289de581282e69b7 |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Examples For more full code guide, please refer to this [documentation](https://sgnlp.aisingapore.net/docs/model/ufd.html). <br> Alternatively, you can also try out the [demo](https://sgnlp.aisingapore.net/cross-lingual-cross-domain) for Cross Lingual Cross Domain. Example of Undersupervised Feature Decomposition (UF... | a69f76e51f35a9d6803d665fb84faa40 |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Model predict ('books_de_dvd' model example) instance = """Wolverine is BACK Der Film ist im Grunde wie alle Teile der X-Men für Comic-Fans auf jeden Fall ein muss. Hugh Jackman spielt seine Rolle wie immer so gut was ich von den ein oder anderen Darsteller leider nicht sagen kann. Story un... | 5ecf3cc5c771db5f651b89b4681d815c |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Training Results - For UFD - **Training Time: (Unsupervised training)** ~3 hours for 30 epochs on a single V100 GPU - **Training Time: (Supervised training)** ~3 hours for 60 epochs on a single V100 GPU | 080dae8d6c029d49b1224e5d3dea5f74 |
gpl-3.0 | ['ufd', 'text-classification', 'undersupervised-feature-decomposition'] | false | Model Parameters - **Model Weights:** [refer to documentation for details](https://sgnlp.aisingapore.net/docs/model/ufd.html) - **Model Config:** [refer to documentation for details](https://sgnlp.aisingapore.net/docs/model/ufd.html) - **Model Inputs:** Raw text. - **Model Outputs:** Array of logits with the size of n... | 525520cb7d01e2ebc1d374247628fbb1 |
apache-2.0 | ['translation'] | false | opus-mt-en-chk * source languages: en * target languages: chk * OPUS readme: [en-chk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-chk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 81ebb39272cded32f560fde720a2022d |
creativeml-openrail-m | ['text-to-image'] | false | collage Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/... | a0e2d0d581b06f3e3bc823834b736711 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_small_tf_512.in1k An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | 2f2aead0fac2e8951e8dc0a268e1a8d3 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 69.1 - GMACs: 67.3 - Activations (M): 383.8 - Image size: 512 x 512 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 677dd754921a19ddabda98554859c574 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_small_tf_512.in1k', pretrained=True) model = mo... | 9c576acf110661ff52ab33b757e53a0a |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_small_tf_512.in1k', pretrained=True,... | 39fe04b2ca82deae548c41e1cf5c655a |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_small_tf_512.in1k', pretrained=True, n... | cf43bb07ce5141a7cdd82939f5027851 |
mit | [] | false | luinv2 on Stable Diffusion This is the `<luin-waifu>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also tra... | 3c4cfba4cd6612573e8a39c347fd8f6d |
apache-2.0 | ['generated_from_trainer'] | false | Dansk-wav2vec2-stt This model is a fine-tuned version of [Siyam/Dansk-wav2vec21](https://huggingface.co/Siyam/Dansk-wav2vec21) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7500 - Wer: 0.3929 | d12ade87ffc2b697d9c9b0fdf43708d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0298 | 4.26 | 400 | 0.8420 | 0.4579 | | 0.0479 | 8.51 | 800 | 0.8713 | 0.4461 | | 0.0387 | 12.77 | 1200 | 0.8307 | 0.4404 | |... | 8c409ac7544c808f2063724a9e9db587 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e4 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8121 - Ro... | 82f382a4888d01825e89ecc0379ff294 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 398 | 0.8673 | 53.562 | 34.4013 | 36.5393 | 50.7868 | 14... | e8e33fd0abc81e1a61fbcfa6c5f7d4ab |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-large-subjqa-vanilla-electronics-qg` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: electronics) via [`lmqg`](https://github.com/asahi41... | eb8a0168223b5f8142cb6b686c598231 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-large](https://huggingface.co/t5-large) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (electronics) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question... | 8998fdce61bf171697f7e8844c22baa6 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-large-subjqa-v... | a53cc9e9609c3bd62dfac9610540f805 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-large-subjqa-vanilla-electronics-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | Score | Type | Dataset ... | d79807cfb6129a6257fda7bdd47c04d3 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: electronics - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-large - max_length: 512 - max_length_output: 32 - epoch: 1 - bat... | 223d8f18254a098a275a2e196124a9e7 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Anything V3 Welcome to Anything V3 - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags to generate images. e.g. **_1girl, white hair, golden eyes, beau... | b88f92bf6ec4d71ee633226b60a959fb |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 71775ce0c564214516c67ae8041a6aec |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Examples Below are some examples of images generated using this model: **Anime Girl:**  ``` 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden Steps: 50, Sampler: DDIM, CFG... | 7c2b326fd712c2f4a088293271346e9e |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | This is a mixed model made by IWillRemember, obtained from Discord. All credits goes to them who provided the models, thank you. Quote from IWillRemember: "...it's an amazingly accurate mix and it does almost everything really well if the right tags are used, the art style is really soft, photorealism, classicism, gho... | 39861dc600435dfb2c363ced333e8842 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_100v3_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one100v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.4863 - Precision: 0.2056 - Recall: 0.0896 - F1: 0.1248 - Accura... | 4e74f1e62c523fab4f391303c46f8ec7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 26 | 0.6246 | 0.1111 | 0.0003 | 0.0005 | 0.7773 | | No log | 2.0 |... | 0ceacc10f7f166273f422c38cb14ad95 |
apache-2.0 | ['generated_from_trainer'] | false | distil-I-upper This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6060 - Rmse: 0.7785 - Mse: 0.6060 - Mae: 0.6007 | 2cbf2a5f05370c6477a7fa12eb31783d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.7219 | 1.0 | 492 | 0.6818 | 0.8257 | 0.6818 | 0.5909 | | 0.5932 | 2.0 | 984 | 0.6419 | 0.8012 | 0.6419 ... | 951bc1de716a5aab268df9008c77739f |
apache-2.0 | ['translation'] | false | gmq-eng * source group: North Germanic languages * target group: English * OPUS readme: [gmq-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md) * model: transformer * source language(s): dan fao isl nno nob nob_Hebr non_Latn swe * target language(s): eng * model: transfo... | 14754d0d545543cecd4bdf2dcba9e465 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: gmq-eng - source_languages: gmq - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq', 'en'] - s... | dcfc9d7500655976570620b002b35157 |
apache-2.0 | ['generated_from_trainer'] | false | gpt-neo-125M-DOD-LOW This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0427 | c1868336ff0bbbf803ac9172cb6ac6b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 261 | 6.4768 | | 6.8863 | 2.0 | 522 | 6.1056 | | 6.8863 | 3.0 | 783 | 6.0427 | | a9523eb2fd3c55ddab971ae3010d7531 |
mit | ['computer vision', 'GAN'] | false | Face Frontalization is a generative computer vision task in which the model takes a photo of a person's head taken at an angle between -90 and 90 degrees, and produces an image of what that person's frontal (i.e. 0 degree) view of the face might look like. The present model was first released in [this repository](http... | 9bb7b6bdd81f64f3ef8eb236e6a6f628 |
mit | ['computer vision', 'GAN'] | false | Model description The Face Frontalization model is the Generator part of a [GAN](https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf) that was trained in a supervised fashion on profile-frontal image pairs. The Discriminator was based on a fairly standard [DCGAN](https://arxiv.or... | 45759e1498d5cad542481b2647345871 |
mit | ['computer vision', 'GAN'] | false | Intended uses & limitations The present Face Frontalization model was not intended to represent the state of the art for this machine learning task. Instead, the goals were: (a) to demonstrate the benefits of using a GAN for supervised machine learning tasks (whereas the original GAN is an unsupervised generative al... | 914a8eadc2bfc10fff3c75b8d99043b2 |
mit | ['computer vision', 'GAN'] | false | How to use The Face Frontalization model is a saved Pytorch model that can be loaded provided the included *network* package is present in the directory. It takes in 3-channel color images resized to 128x128 pixels in the form of [N, 3, 128, 128] tensors (where N is the size of the batch). Ideally, the input images s... | f3eb6b9094d62ce9c7dc05b45d31bc5c |
mit | ['computer vision', 'GAN'] | false | (as required by the frontalization model) preprocess = transforms.Compose((transforms.ToPILImage(), transforms.Resize(size = (128, 128)), transforms.ToTensor())) input_tensor = torch.unsqueeze(preprocess(image), 0) | 7fcb0528416764ddb54c1cf98e5b6160 |
mit | ['computer vision', 'GAN'] | false | and this will need to get fixed before the output is displayed) generated_image = saved_model(Variable(input_tensor.type('torch.FloatTensor'))) generated_image = generated_image.detach().squeeze().permute(1, 2, 0).numpy() generated_image = (generated_image + 1.0) / 2.0 return generated_image i... | 8fa79df528faeebc8d46137387d9ea39 |
mit | ['computer vision', 'GAN'] | false | Limitations and bias As mentioned in the **Intended uses** section, the present model's performance is not intended to compete with the state of the art. Additionally, as the training data had a disproportionately high number of images of caucasian and asian males in their 20s, the model does not perform as well when... | 56ba831bf5849e1d10ecf4d92435cbc6 |
mit | ['computer vision', 'GAN'] | false | Training data The present model was trained on [the CMU Multi-PIE Face Database that is available commercially](https://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html). The input images were closely cropped to include the face of a person photographed at an angle between -90 and 90 degrees. The target... | 9b8c421a34fbf999006e716725693696 |
mit | ['computer vision', 'GAN'] | false | Training procedure The training of the model was performed in a similar manner to that of a regular unsupervised [GAN](https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf), except that in addition to the binary cross entropy loss for the Discriminator, a pixelwise loss function w... | 4171741b6c88d0d255fe79251052938f |
mit | ['computer vision', 'GAN'] | false | Evaluation results GANs are notoriously difficult to train, with the losses for the Discriminator and the Generator often failing to converge even when producing what looks to be a highly realistic result to a human eye. The pixelwise loss for the test images also serves as a poor indicator of the model's performance... | eab9a2dc8ce0032b0a368ffee6f7c2c5 |
mit | ['roberta-base', 'roberta-base-epoch_25'] | false | RoBERTa, Intermediate Checkpoint - Epoch 25 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 480ed5ef3c73ae8f70afc4ab6ac9986e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-TT2-exam This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Precision: 0.9222 - Recall: 0.9369 - F1: 0.9295 - Accuracy: 0.9... | d671370e9bd7301c474189bae6d8187d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2509 | 1.0 | 879 | 0.0733 | 0.8855 | 0.9212 | 0.9030 | 0.9777 | | 0.0505 | 2.0 |... | b94b2f2b8a587edddb5e323eed68aa7e |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> import torch >>> from transformers import AutoModel, AutoTokenizer >>> >>> model_id = "rmihaylov/roberta-base-sentiment-bg" >>> model = AutoModel.from_pretrained(model_id, trust_remote_code=True) >>> tokenizer = AutoTokenizer.from_pretrained(model_i... | 4873ad4a1d7cdef38018153b5733a1e2 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Stable-Dreamfusion A pytorch implementation of the text-to-3D model **Dreamfusion**, powered by the [Stable Diffusion](https://github.com/CompVis/stable-diffusion) text-to-2D model. The original paper's project page: [_DreamFusion: Text-to-3D using 2D Diffusion_](https://dreamfusion3d.github.io/). Colab notebook fo... | 6d92a950f0b3259fef07f0ac0a47fa0b |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Important Notice This project is a **work-in-progress**, and contains lots of differences from the paper. Also, many features are still not implemented now. **The current generation quality cannot match the results from the original paper, and many prompts still fail badly!** | de3d0dc023c272a7d2a8a02459bc2b53 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Notable differences from the paper * Since the Imagen model is not publicly available, we use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) to replace it (implementation from [diffusers](https://github.com/huggingface/diffusers)). Different from Imagen, Stable-Diffusion is a latent diffusion model, w... | 88a6e9fbfc131aacefc62b55547dcdf5 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Install ```bash git clone https://github.com/ashawkey/stable-dreamfusion.git cd stable-dreamfusion ``` **Important**: To download the Stable Diffusion model checkpoint, you should provide your [access token](https://huggingface.co/settings/tokens). You could choose either of the following ways: * Run `huggingface-cl... | 15b96f12982b12f492d3c68cfaa4e6a3 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | dreamfields (CLIP) setting python main.py --text "a hamburger" --workspace trial_clip -O --guidance clip python main.py --text "a hamburger" --workspace trial_clip -O --test --gui --guidance clip ``` | 99dfc580ab92fc1d3d09add8e66128f3 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Code organization & Advanced tips This is a simple description of the most important implementation details. If you are interested in improving this repo, this might be a starting point. Any contribution would be greatly appreciated! * The SDS loss is located at `./nerf/sd.py > StableDiffusion > train_step`: ```pyt... | 65284ef093a06747c1a205e989f21d76 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | 3. the SDS loss, since UNet part is ignored and cannot simply audodiff, we manually set the grad for latents. w = self.alphas[t] ** 0.5 * (1 - self.alphas[t]) grad = w * (noise_pred - noise) latents.backward(gradient=grad, retain_graph=True) ``` * Other regularizations are in `./nerf/utils.py > Trainer > train_step`. ... | a3202fe198afcd88ba63ae6a4ba182a0 |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | faster training, but slower rendering ``` Training is faster if only sample 128 points uniformly per ray (5h --> 2.5h). More testing is needed... * Shading & normal evaluation: `./nerf/network*.py > NeRFNetwork > forward`. Current implementation harms training and is disabled. * light direc... | a54f8e21945e0b17aebb94f58f4bcdcb |
mit | ['stable-diffusion', 'dreamfusion', 'text2mesh'] | false | Acknowledgement * The amazing original work: [_DreamFusion: Text-to-3D using 2D Diffusion_](https://dreamfusion3d.github.io/). ``` @article{poole2022dreamfusion, author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben}, title = {DreamFusion: Text-to-3D using 2D Diffusio... | e8433f9b84c4a477f0a9b08620963a78 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8076 - Matthews Correlation: 0.5513 | 7e02f397ef6dd1a3dc1b3076d2eb9980 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5264 | 1.0 | 535 | 0.5380 | 0.4135 | | 0.3486 | 2.0 | 1070 | 0.5007 | 0.4923 | | 0.2... | bc7f03aa5630dea7611b9cb9f984dcdd |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'dreambooth'] | false | SD-1.5-TheRock Dreambooth model trained by Azuremis with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook This is a stable diffusion model fine-tuned via dreambooth to produce images of The Rock (Dwayne Jo... | 7663391231203118a1a76bb09c8876a8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4275, 'end_learning_ra... | b1b2bf847a6114ecaf3ebea680465804 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2026 - Validation Loss: 0.0726 - Train Precision: 0.8945 -... | baa12a6aafd636a207e77d8b6c448d20 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.2026 | 0.0726 | 0.8945 | 0.9220 | 0.9081 | 0.9793 | 0 ... | 5e55fee70a67083034c2abb0ad4dda60 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_large_tf_512.in1k An official MaxViT image classification model. Trained in tensorflow on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | fe939eb8fca6ad449576bb3f716456b8 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 212.3 - GMACs: 244.8 - Activations (M): 942.1 - Image size: 512 x 512 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | a89f38140829b49bdac24b493000f06e |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxvit_large_tf_512.in1k', pretrained=True) model = mo... | 3b7a8d2e79123329aaeabc1c1411e29f |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_large_tf_512.in1k', pretrained=True,... | d6d8e309b82d7670531ab5d56deec5ce |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxvit_large_tf_512.in1k', pretrained=True, n... | 36296f607b0e8f893f516149c819595f |
apache-2.0 | ['generated_from_keras_callback'] | false | caotianyu1996/bert_finetuned_ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0247 - Validation Loss: 0.0593 - Epoch: 2 | ab17df2e90fc95c593803876c1111e33 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1275 | 0.0574 | 0 | | 0.0414 | 0.0569 | 1 | | 0.0247 | 0.0593 | 2 | | f9f29eef6663fc4287089168774411d3 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'ug'] | false | XLS-R-300M Uyghur CV8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UG dataset. It achieves the following results on the evaluation set: - Loss: 0.2026 - Wer: 0.3248 | 40ef9bb2368df0049db3e260f95a15da |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'ug'] | false | Training procedure The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 9400 steps (100 epochs). | 1cfe3c3752c74ee1cdaa2b8b4cd26af4 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'ug'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 7859af31936a43723c87d096f5819264 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'ug'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3036 | 5.32 | 500 | 3.2628 | 1.0 | | 2.9734 | 10.63 | 1000 | 2.5677 | 0.9980 | | 1.3466 | 15.95 | 1500 | 0.4455 | 0.6306 | |... | d92cca9d442e0348c99d0cb5557b93d0 |
mit | ['generated_from_trainer'] | false | Klassifizierung-Gewerke This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0964 - F1: 0.9822 | e19b00de95dc3d2f80dafa9d1c985db1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.6216 | 1.0 | 91 | 0.1944 | 0.9415 | | 0.1465 | 2.0 | 182 | 0.1180 | 0.9695 | | 0.0651 | 3.0 | 273 | 0.0964 | 0.9822 | ... | a38027683ced7d714a4bf2791bc7617c |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_cola This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.3784 - Matthews Correl... | 4770a0fff9cf63f0e49eb48f457d31d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6286 | 1.0 | 67 | 0.4351 | 0.4469 | | 0.3982 | 2.0 | 134 | 0.4004 | 0.4858 | | 0.2... | 8045a976dc47be846d5648fcb8d4d2e4 |
creativeml-openrail-m | ['text-to-image'] | false | model by estelleflores  This is a Stable Diffusion 2 model fine-tuned to the CRIsimsEstelle concept taught to Stable Diffusion with Dreambooth.  It can be used by modifying the `instance_prompt`: **3d render in \<cri-sims> style... | 6313a5487b3513aa0d718760dc66d012 |
apache-2.0 | ['generated_from_keras_callback'] | false | AmitBHuji/mt5-small-finetuned-mt5-simplification-1epoch This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.2240 - Epoch: 7 | 940393c1fe79c7012e38396cb2a7b949 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 1192, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay... | 93c6e1ac81e0ce58785f1a06eb2fde89 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 15.1244 | 0 | | 9.6794 | 1 | | 7.9758 | 2 | | 7.1858 | 3 | | 6.6506 | 4 | | 6.5284 | 5 | | 6.2093 | 6 | | 6.2240 | 7 | | f392c7e359c7ed7233e45eb68f2f1a27 |
mit | [] | false | Anime girl on Stable Diffusion This is the `<anime-girl>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also... | d5cdd6fd9dc1fc31053d14ffc639fc2c |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/csmsc_tts_train_fastspeech2_raw_phn_pypinyin_g2p_phone_train.loss.ave` ♻️ Imported from https://zenodo.org/record/4031953/ This model was trained by kan-bayashi using csmsc/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 27e7bd6b7189325c368357684adebd79 |
apache-2.0 | ['generated_from_keras_callback'] | false | train_basic_M_V3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: | 55f790ba553dac520240bea1162b17d5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | ae5fd46df338285d5aa0beb25a77a752 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | pszemraj/pegasus-x-large-book-summary <a href="https://colab.research.google.com/gist/pszemraj/6c326c0649233ab017d63adc36958d1a/pegasus-x-large-booksum-demo.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> Get SparkNotes-esque summaries of arbitrary text! Due ... | 55968dd369a689a6e9fc54a1b098e295 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Epochs 5 & 6 The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: _ADAN_ using lucidrains' `adan-pytorch` with default betas - lr_... | 39295523f61a838acd421a8b3739f833 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'summary', 'booksum', 'long-document', 'long-form'] | false | Epochs 7 & 8 - epochs 5 & 6 were trained with 12288 tokens input - this fixes that with 2 epochs at 16384 tokens input The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16... | 2bd299e67ee9d2753f046e8fde9f898d |
apache-2.0 | ['generated_from_keras_callback'] | false | veb/twitch-distilbert-base-cased-finetuned This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.5140 - Validation Loss: 5.4524 - Epoch: 0 | 588d0c0142b0eb26aa53dc194df4a01d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 02658e706920d8575cfc8b6097b4546c |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4102 - Wer: 0.3165 | 22003e1b803c50c1aaea7e59c8eb4dfb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9393 | 3.67 | 400 | 0.6784 | 0.7123 | | 0.4104 | 7.34 | 800 | 0.4521 | 0.4865 | | 0.1929 | 11.01 | 1200 | 0.4470 | 0.4802 | |... | 64ebfdbbb7060302fbc883cd6df2e7a6 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_vp-100k_s509 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 979ac5538b25475dc175349f3a99618e |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on ADE20k semantic segmentation (base-IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/... | b6d042913990a5358dddcf558784c4df |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on ADE20k semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-ade-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-ade-semantic") url = "http://images.cocodataset.org/val2017/... | a83becc384c7e9d37a7b63170a06a412 |
mit | ['SlovakBERT'] | false | SlovakBERT (base-sized model)
SlovakBERT pretrained model on Slovak language using a masked language modeling (MLM) objective. This model is case-sensitive: it makes a difference between slovensko and Slovensko.
| 5de9f5a9ddb830ace711e9c1381a69e5 |
mit | ['SlovakBERT'] | false | Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
**IMPORTANT**: The model was not trained on the “ and ” (direct quote) character -> so before tokenizing the text, it is advised to replace all “ and ” (direct quote mar... | 28073e5d3e8b62dce4880f786f4a496b |
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