license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['image-classification'] | false | Params(M) | GFLOPs | Top1 Acc(%) | Download |
| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-... | 8aeffd7ccbd65ee2e46c860bf0b9379d |
apache-2.0 | ['image-classification'] | false | BibTeX entry and citation info
```bibtex
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
``` | f03bdd978a0bc0af8698377ce23befee |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.uniform.sa.5-class.seed_44 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7783 - Accuracy: 0.6712 - Macro-f1: 0.6220 - Weighte... | b742a4c2f00d8e8ab68a4347cca9793f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Rotoscopee This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from the following rotoscope animations : **_A Scanner Darkly_** movie, **_Undone_** tv series, **_Tehran Taboo_** movie. Use the token **_rotoscopee_** in your prompts to use the style. _Download the ckpt file from "files... | 947b99ed12aa53ef436f5c6c49f2fcd3 |
mit | [] | false | alien avatar on Stable Diffusion This is the `<alien-avatar>` 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 ... | 91ee7b556b9322e7188326dd79af1cbe |
mit | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the squad dataset. It achieves the following results on the evaluation set: - exact_match: 77.57805108798486 - f1: 85.73943867549627 - Loss: 1.0744 | 5f7aa8de642a4b4060430fd1c0abf733 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | f17a12a9cf4fc8d58effb25f90c214cc |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 026370acb27066b1bfa932dc5045d713 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | No log | 1.0 | 405 | 2.5110 | 23.4004 | 8.9397 | 20.9541 | 21.5922 | | 2a21cd03b0331befe61fd47316f19255 |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-base-finetuned-en-to-no-test This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: nan - Bleu: 0.8116 - Gen Len: 18.1543 | c5862f11734bfe966d75cad33b3e75d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP | 3f0ac2d89e7f7e4fd853daba7dce4c66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.0 | 1.0 | 788 | nan | 0.8116 | 18.1543 | | 0.0 | 2.0 | 1576 | nan | 0.8116 | 18.1543 | | 0.0 | 3.0... | d7054d991a82ad6e754b098a55e17e78 |
apache-2.0 | ['generated_from_trainer'] | false | Usage ```python from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-albanian-ner") model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-albanian-... | 61b18e421fc9c1a4d788f4d4595140ea |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-de-to-en-swd This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9422 - Bleu: 9.2293 - Gen Len: 17.3454 | 102ac8d4db446661018ec2a03984a9b5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 272 | 2.1658 | 3.8987 | 17.6419 | | 2.6679 | 2.0 | 544 | 2.0659 | 6.4465 | 17.4758 | | 2.6679 | 3.0... | 32686c21db8b2a5b5aa72b65f437f0f0 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-010099-full This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 1.9934 - Bleu: 21.2377 - Gen Len: 44.0745 | 019dd8e1be03e9792e9c99186995f490 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 | 73ca14c738549381b80f088109856985 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples 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: - Loss: 0.3081 - Accuracy: 0.8767 - F1: 0.8771 | 34bc7edd5b69506f463097e16e92c3d9 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-EL2 (Deep-Narrow version) T5-Efficient-LARGE-EL2 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint an... | 91e4f76f4f56998415f44e4676071fb0 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-el2** - is of model type **Large** with the following variations: - **el** is **2** It has **460.84** million parameters and thus requires *ca.* **1843.34 MB** of memory in full precision (*fp32*) or **921.67 MB** of memory in half precision (... | 6239b3cf029d40d525a2dfed7f9e4b0d |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-base-uncased-finetuned-imdb 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: | 2b177aba40d56693cf59e5efa750ca0e |
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... | 0bcbc05d13eeec80cb4e1ba7ae30107f |
mit | [] | false | yinit on Stable Diffusion This is the `yinit-dropcap` 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... | ce77eba7d5d91a6825f2c02cc8b71ae6 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP | 8eb9e291a8243f1f5b1e4c9b553e74a7 |
mit | ['audio', 'music', 'generation', 'tensorflow'] | false | Model provided by: marcop Pretrained musika_misc model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). | 371645797d5922bb94011127f66bdc5f |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-100k_s564 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 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 21947d082b4b3494870dfd4bd6d6d371 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | 7efd6d5a747576b46c47bd9a9e9af1ad |
mit | [] | false | model by s3rgio27 This your the Stable Diffusion model fine-tuned the elvis concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **elvis** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/gi... | 825bdc70ea79d5159b131308914f440a |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-base-uncased'] | false | How to use ```python >>> from transformers import pipeline >>> nlp = pipeline('fill-mask', model='zanelim/singbert') >>> nlp("kopi c siew [MASK]") [{'sequence': '[CLS] kopi c siew dai [SEP]', 'score': 0.5092713236808777, 'token': 18765, 'token_str': 'dai'}, {'sequence': '[CLS] kopi c siew mai [SEP]', 'score... | f460796bb827bcc68ac55f58acee5b0e |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-base-uncased'] | false | Limitations and bias This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias. | 774a6d51d2ebc03164c6611ac56ad36f |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-base-uncased'] | false | Training data Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew) corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`. | af6e326141a1e8c4ec395352c2e1a461 |
mit | ['singapore', 'sg', 'singlish', 'malaysia', 'ms', 'manglish', 'bert-base-uncased'] | false | pre-trained-models) vocab and checkpoints (pre-trained weights). Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab. Pre-training was further finetuned on training data with the following hyperparameters ... | f87d543b41fa5795618ed6aca8dbbb0b |
apache-2.0 | ['generated_from_keras_callback'] | false | kp9z2/distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.8579 - Validation Loss: 3.6750 - Epoch: 0 | a77a9557473957c4a365844e46e85857 |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_Climber_Finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1011 | 281b4ecf1892a70c13975d7f2a27549d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1002 | 1.0 | 2133 | 0.1022 | | 0.0822 | 2.0 | 4266 | 0.0941 | | 0.0769 | 3.0 | 6399 | 0.1011 | | 0189d7258a40f8acce55662b312c3ed0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 178 | 1.9530 | 9.1314 | 1.226 | 9.1213 | 9.1047 | 14.4473 | ... | c4062554b9807fc0f01459bf8cb1cbb7 |
apache-2.0 | ['generated_from_keras_callback'] | false | mordred501/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: 0.0250 - Validation Loss: 0.0601 - Epoch: 2 | 9a41f5ac4a11767b5cf8daac6f4fbfdf |
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': 1017, 'end_learning_ra... | 365bf86524c80805b73087e13f4f5fd9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1328 | 0.0573 | 0 | | 0.0411 | 0.0583 | 1 | | 0.0250 | 0.0601 | 2 | | aeb3676089ccd1c9e0f08f57a7b05c0f |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_400k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 3, Step 400k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 9e7b351a4750fe85bacd2446468c5461 |
apache-2.0 | ['multiberts', 'multiberts-seed_3', 'multiberts-seed_3-step_400k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_3-step_400k') model = TFBertModel.from_pretrained("google/multibe... | bfd989f0149ee62fe87d669408096877 |
apache-2.0 | ['generated_from_trainer'] | false | multi-classifier 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: - Loss: 0.3114 - Accuracy: 0.8676 | ec72446dcd74925d5132c3da00ae617e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3925 | 1.0 | 616 | 0.3155 | 0.8612 | | 0.2955 | 2.0 | 1232 | 0.3114 | 0.8676 | | e2c6dccece4a2abea6c828723e26fd91 |
apache-2.0 | ['summarization', 'translation'] | false | Model Card for T5 Base  | 7384c5e86920bf8c7e6ab713c98b3373 |
apache-2.0 | ['summarization', 'translation'] | false | Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT... | f1a7952baf3c5e60e83b93cc8f2806b9 |
apache-2.0 | ['summarization', 'translation'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import T5Tokenizer, T5Model tokenizer = T5Tokenizer.from_pretrained("t5-base") model = T5Model.from_pretrained("t5-base") input_ids = tokenizer( "Studi... | 12d59f4f69583cc59f9f18483253cfe5 |
mit | ['generated_from_trainer'] | false | roberta-base-NER-favsbot This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.4812 - Precision: 0.6940 - Recall: 0.7056 - F1: 0.6997 - Accuracy: 0.8454 | 9414d0cdffd7b993139c71f70c567634 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 4 | 2.1885 | 0.1787 | 0.3722 | 0.2414 | 0.2998 | | No log | 2.0 |... | d1cf3890298a2e303c29f43fa98f7e63 |
apache-2.0 | [] | false | bert-base-en-zh-hi-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly t... | 339e6fa21b088e53e99a20cb2ed1b256 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-zh-hi-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-zh-hi-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 49d8cc7b4cd46522f35c6b31eb5b2249 |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test-04 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0713 - Wer: 0.5078 | cd1694c55fe226fc23d39bfbccafc353 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | d321eece316f871b00609b416a517ef5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.2131 | 2.09 | 400 | 0.7100 | 0.6832 | | 0.6539 | 4.19 | 800 | 0.8307 | 0.6602 | | 0.5081 | 6.28 | 1200 | 0.7120 | 0.6297 | |... | 255fb512977a75e62f52eb1debbf4278 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | About the model The model has been trained on a dataset containing [264519 sentences with UK English spelling](https://www.englishvoice.ai/p/uk-to-us/ "264519 sentences with UK English spelling"), along with their US English equivalent. The purpose of the model is to rewrite sentences from UK English to US English. ... | fb85acab08bef745b0eb79e3fc99c6d5 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Generation examples | Input | Output | | :------------ | :------------ | | My favourite colour is yellow. | My favorite color is yellow. | | I saw a bloke in yellow trainers at the underground station. | I saw a guy in yellow sneakers at the subway station. | | You could have got hurt! | You could have gotten hurt! |... | 7843fafa4fc2bc0dcee5e452820840ae |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Sample code Sample Python code: ```python import torch from transformers import T5ForConditionalGeneration,T5Tokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-uk-to-us-english") tokenizer = T5Tokenizer.from_pretr... | d74a77969d329f6ece4518f8c643ce20 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for the fgreeneruins concept trained on the CCMat/db-forest-ruins dataset. This is a Stable Diffusion model fine-tuned on the fgreeneruins concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of fgreeneruins ruins** This model was created as part of the DreamBooth Ha... | d6d770709eb22b77bc89d3f912603eac |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Description This is a Stable Diffusion model fine-tuned on `ruins` images for the landscape theme.<br> Concept: **fgreeneruins** : forest ruins, greenery ruins<br> Pretrained Model: [nitrosocke/elden-ring-diffusion](https://huggingface.co/nitrosocke/elden-ring-diffusion)<br> Learning rate: 2e-6<br> | b505fd1ffc4ef973acab32d9ab379e88 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Samples Prompt: "high quality photo of Venice in fruins ruins"  <br> Prompt: "high quality photo of Rome in fgreeneruins ruins with the Colosseum in the background"  <br> Prompt: "fgreeneruins... | 3c947501154263e8f36f7cc6498f8b7d |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s246 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 145de13a309c932ee32a4871eb20623f |
mit | [] | false | painted_student on Stable Diffusion This is the `<painted_student>` 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. Yo... | c305f2fd3bacd83d27a6d9371e43043f |
apache-2.0 | ['classification', 'generated_from_trainer'] | false | test-trainer This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5655 - Accuracy: 0.8554 - F1: 0.9008 | f8370e2b3915fd46cf77bde618864bf8 |
apache-2.0 | ['classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3654 | 0.8358 | 0.8843 | | 0.5335 | 2.0 | 918 | 0.4351 | 0.8505 | 0.8982 | | 0.3401 |... | 5b41e53b2d3712c1e4aed8e3389b73b3 |
apache-2.0 | ['automatic-speech-recognition', 'multilingual_librispeech', 'generated_from_trainer'] | false | wav2vec2-300m-mls-german-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MULTILINGUAL_LIBRISPEECH - GERMAN 10h dataset. It achieves the following results on the evaluation set: - Loss: 0.2398 - Wer: 0.1520 | 372e5a0c5cb30d2659f757eb42a53a3c |
apache-2.0 | ['automatic-speech-recognition', 'multilingual_librispeech', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200.0 - mixed_precisio... | 4d13c1b62e33b5631ca818e8cf0d2828 |
apache-2.0 | ['automatic-speech-recognition', 'multilingual_librispeech', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 3.0132 | 7.25 | 500 | 2.9393 | 1.0 | | 2.9241 | 14.49 | 1000 | 2.8734 | 1.0 | | 1.0766 | 21.74 | 1500 | 0.2773 | ... | 42f2cc85715c8314fca16c00ede0f7eb |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/azuki). Dataset is available [here](https://huggingface.co/datasets/huggingnft/azuki). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repositor... | 39ba227c0142fe0dd7937d8940b97791 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 100k (uncased) Seed 4 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | b45ca107e4b65fd757fc38a1885ea65e |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-100k') model = BertModel.from_pretrained("multiberts-seed-4-100k") text = "Replace me by any text you'd like.... | 9b7fd80233b992ffe43360b232a0243d |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | Training data The model is initialized from the [gpt2](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentence... | 8c644cea73fd86bd26047a5613c5a647 |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | Training procedure Each natural language statement that against commonsense is concatenated with its reference reason with `<|continue|>` as a separator, then the model finetuned using CLM objective. The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum s... | 2684c67cfae98473c588e513a1fb2646 |
mit | ['exbert', 'commonsense', 'semeval2020', 'comve'] | false | BibTeX entry and citation info ```bibtex @article{fadel2020justers, title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation}, author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik}, year={2020} } ``` <a href="https://huggingface.co/exbert/?model... | 48f2045fdbf1ec3db87d92f85e9b59ad |
gpl-3.0 | [] | false | Thyroid BRAF-RAS Score (BRS) v1 Model Card This model card describes a model associated with the manuscript "Deep learning prediction of BRAF-RAS gene expression signature identifies noninvasive follicular thyroid neoplasms with papillary-like nuclear features", by Dolezal _et al_, available [here](https://www.nature.... | 1706252fe894f424b9dd3e3be488238b |
gpl-3.0 | [] | false | Model Details - **Developed by:** James Dolezal - **Model type:** Deep convolutional neural network image classifier - **Language(s):** English - **License:** GPL-3.0 - **Model Description:** This is a model that can predict, from H&E-stained pathologic images of thyroid neoplasms, the predicted BRAF-RAS Score (BRS). ... | 360acacb3fd31d903092df8669d32c1a |
gpl-3.0 | [] | false | Examples For direct use, the model can be loaded using Tensorflow/Keras: ``` import tensorflow as tf model = tf.keras.models.load_model('/path/') ``` or loaded with [Slideflow](https://github.com/jamesdolezal/slideflow) version 1.1+ with the following syntax: ``` import slideflow as sf model = sf.model.load('/path/... | 5d75a1a733360cd1fba6675c52597091 |
gpl-3.0 | [] | false | to a tf.data.Dataset dataset = dataset.map(normalizer.tf_to_tf) ``` Alternatively, the model can be used to generate predictions for whole-slide images processed through Slideflow in an end-to-end [Project](https://slideflow.dev/project_setup.html). To use the model to generate predictions on data processed with Slid... | 6fc6a27c99e0829dfcd758c4753e51be |
gpl-3.0 | [] | false | Direct Use This model is intended for research purposes only. Possible research areas and tasks include - Applications in educational settings. - Research on pathology classification models for thyroid neoplasms. Excluded uses are described below. | f17a1113a156e2d73e6c02c2638c74ae |
gpl-3.0 | [] | false | Misuse and Out-of-Scope Use This model should not be used in a clinical setting to generate predictions that will be used to inform patients, physicians, or any other health care members directly involved in their health care outside the context of an approved research protocol. Using the model in a clinical setting o... | 71f7b6f25f321c9cda2c13132e92c302 |
gpl-3.0 | [] | false | Bias This model was trained on The Cancer Genome Atlas (TCGA), which contains patient data from communities and cultures which may not reflect the general population. This datasets is comprised of images from multiple institutions, which may introduce a potential source of bias from site-specific batch effects ([Howar... | 71b75ea60c4cf9d70e2a82fe921ea3c4 |
gpl-3.0 | [] | false | Training **Training Data** The following dataset was used to train the model: - The Cancer Genome Atlas (TCGA), THCA cohort (see next section) This model was trained on a total of 369 slides, with 116 BRAF-like tumors and 271 RAS-like tumors. **Training Procedure** Each whole-slide image was sectioned into smalle... | 7bbf21e6f3022d960c4df2f493c9ed00 |
gpl-3.0 | [] | false | vips-resize). During training, - Images are stain-normalized with a modified Reinhard normalizer ("Reinhard-Fast"), which excludes the brightness standardization step, available [here](https://github.com/jamesdolezal/slideflow/blob/master/slideflow/norm/tensorflow/reinhard.py) - Images are randomly flipped and rotated... | ba7479d4cd33c29a6db153218cdb4eb1 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8564 - Bleu: 52.8938 | 16973b27d82fef6c49be1b0bbaf415f6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1555 - Precision: 0.9681 - Recall: 0.9670 - F1: 0.9675 - Accuracy: 0.9687 | 921145eaef02e8b95d16321130abd8d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 253 | 0.1972 | 0.9467 | 0.9408 | 0.9437 | 0.9511 | | 0.3572 | 2.0 |... | c9719e6f26097e0c26f1f6b817a65577 |
mit | [] | false | blue-zombiee on Stable Diffusion This is the `<blue-zombie>` 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 a... | 918068de927784aa2b2c77dddc2183f9 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_rmlp_base_rw_224.sw_in12k A timm specific MaxViT (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman. | a4197a1401670655a2ac45df50e67808 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 124.5 - GMACs: 23.2 - Activations (M): 92.6 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - Swin Transformer V2: Scaling Up Capacity and... | 72fdf7a1dfb266bede14ab1cbd3000bd |
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_rmlp_base_rw_224.sw_in12k', pretrained=True) mo... | 0d99f4b61344e8b5b2fdb57feb40e9a7 |
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_rmlp_base_rw_224.sw_in12k', pretrain... | eb529ae8a66f4eb1e7465859826b83db |
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_rmlp_base_rw_224.sw_in12k', pretrained=Tru... | 731e341168caa451fec2af51b7410553 |
mit | [] | false | Sims 2 Portrait on Stable Diffusion This is the `<sims2-portrait>` 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... | 88fd08eec142ec807ee4e28e7abaa5e8 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-8front-1body-8rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4376 - Wer: 0.1693 - Mer: 0.1635 - Wil: 0.2492 - Wip: 0.7508 - Hits: 55925 - S... | 2d46660e14977eac5060d908d66c99c1 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6093 ... | c0b5bdc5fdf8d69197e187fa0f74faef |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-1b0000 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.7760 - Bleu: 1.1101 - Gen Len: 99.5898 | d812d559c408fb56866560f7fa0f2de5 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 1, Step 500k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different ... | 11cbcb0a471023e24642b60afe5eb8f5 |
apache-2.0 | ['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_500k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_500k') model = TFBertModel.from_pretrained("google/multibe... | 7431972dd699f038dc44b845e6bd5309 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Gamindocar-2000-700 Dreambooth model trained by gabrieleai with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fa... | ac580ae688c403d56e18a4a72b3005fd |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | AltCLIP | 名称 Name | 任务 Task | 语言 Language(s) | 模型 Model | Github | |:------------------:|:----------:|:-------------------:|:--------:|:------:| | AltCLIP | text-image representation| 中英文 Chinese&English | CLIP | [FlagAI](https://github.com/FlagAI-Open/FlagAI) | | 57ba44c9c8fbe7e30fb0a4d760c6ad2f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | 简介 Brief Introduction 我们提出了一个简单高效的方法去训练更加优秀的双语CLIP模型。命名为AltCLIP。AltCLIP基于 [Stable Diffusiosn](https://github.com/CompVis/stable-diffusion) 训练，训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LIAON](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus) AltCLIP模型可以为本项目中的A... | 7b3f8266d8642dfd4cefd95778dab303 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'bilingual', 'en', 'English', 'zh', 'Chinese'] | false | 训练 Training 训练共有两个阶段。 在平行知识蒸馏阶段，我们只是使用平行语料文本来进行蒸馏（平行语料相对于图文对更容易获取且数量更大）。在双语对比学习阶段，我们使用少量的中-英 图像-文本对（一共约2百万）来训练我们的文本编码器以更好地适应图像编码器。 There are two phases of training. In the parallel knowledge distillation phase, we only use parallel corpus texts for distillation (parallel corpus is easier to obtain and larger in numb... | d4c3ddd94905b3539d68ae5ae3169179 |
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