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 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | wav2vec 2.0 with CTC trained on CommonVoice German (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (German Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](http... | 988e073a0b93d40b9858c2678acc549d |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (char) that transforms words into chars and trained with the train transcriptions (train.tsv) of CommonVoice (DE). - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53-german](https... | cd5f19a34a8e80b9046b8c2780485251 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in German) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-de", savedir="pretrained_models/asr-wav2vec2-commonvoice-de") asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-de/examp... | 560aad178d3427128ef8abb8be80fdd8 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/CommonVoi... | 940fe0e121c6dbd71f93733e880bbf41 |
mit | ['generated_from_trainer'] | false | gpt2.CEBaB_confounding.uniform.absa.5-class.seed_43 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.3820 - Accuracy: 0.8654 - Macro-f1: 0.8612 - Weighted-macro-f1: 0.8650 | eec6771bca7e6ea1f7b305acae5d30b2 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.2721 | 676ede3d90ede2d850df643ef77690ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0432 | 1.0 | 8235 | 1.0841 | | 0.7773 | 2.0 | 16470 | 1.0675 | | 0.5593 | 3.0 | 24705 | 1.2721 | | 2730d525b323af31adc1092ba35955b5 |
apache-2.0 | ['text encoder', 'stable diffusion', 'v1.4'] | false | This repository hosts the TFLite version of `text encoder` part of [KerasCV Stable Diffusion](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Stable Diffusion consists of `text encoder`, `diffusion model`, `decoder`, and some glue codes to handl inputs and outputs of each part. ... | e90a85c336fe3e81f9f754ada333f2c6 |
mit | [] | false | Simple CNN-based Artist Classifier This repo contains a simple CNN-based Keras model which classifies images into one of 10 selected artists/painters. - The purpose of this model was for a quick prototyping - Data has been web-crawled using `https://github.com/YoongiKim/AutoCrawler` - 10 popular artists/painters wer... | 997b1dcb7cf2c5b96a4bcc0c5c58a04d |
mit | [] | false | How to use ```python import tensorflow as tf from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("jkang/drawing-artist-classifier") image_file = 'monet.jpg' img = tf.io.read_file(image_file) img = tf.io.decode_jpeg(img, channels=3) last_layer_activation, predictions = model(img[tf.newaxis... | 9d3fff32bdbdab61cca04d3b20f2e195 |
mit | [] | false | Intended uses & limitations You can use this model freely for predicting artists or trends of a given image. Please keep in mind that this model is not intended for production, but for research and quick prototyping. Web-crawled image data might not have a balanced amount of drawings that sufficiently represent the ar... | e0bb1c1888ecccfd20c3f1950bc4dbd1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Farhan11/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: - Train Loss: 0.0272 - Validation Loss: 0.0522 - Epoch: 2 | a564725f54f1ecf8df5a5980229bb59e |
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': 2e-05, 'decay_steps': 2634, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 32d9ae996eb4b852140007b0d7cbcceb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1799 | 0.0605 | 0 | | 0.0479 | 0.0561 | 1 | | 0.0272 | 0.0522 | 2 | | 7f4abdc4b8af00aec7dd4a84f57169f0 |
apache-2.0 | [] | false | HumBert HumBert (Humanitarian Bert) is a [XLM-Roberta](https://huggingface.co/xlm-roberta-base) model trained on humanitarian texts - approximately 50 million textual examples (roughly 2 billion tokens) from public humanitarian reports, law cases and news articles. Data were collected from three main sources: [Relie... | 7c0612c5b2d0201d954fb7f72a9c4634 |
apache-2.0 | [] | false | Intended uses To the best of our knowledge, HumBert is the first language model adapted on humanitarian topics, which often use a very specific language, making adaptation to downstream tasks (such as dister responses text classification) more effective. This model is primarily aimed at being fine-tuned on tasks such... | 4a620c8256f9188451ddf4ed0c2b1793 |
apache-2.0 | [] | false | Usage Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained('nlp-thedeep/humbert') model = AutoModelForMaskedLM.from_pretrained("nlp-thedeep/humbert") | 0fe719dfe67ade7b02d0749b9f2e5ba0 |
mit | ['generated_from_trainer'] | false | data2vec-text-base-finetuned-stsb This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5530 - Pearson: 0.8732 - Spearmanr: 0.8717 | 5c16b3bb900fa92a81bb4418f66ba630 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.725353773731373e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9577a6051af77c28ca6becc58a14308e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 180 | 1.0650 | 0.8102 | 0.8380 | | No log | 2.0 | 360 | 0.6211 | 0.8524 | 0.8497 | | 0.9312 ... | c170861e0381459a3b09b4c65f4ceb3b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Pl This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6919 - Wer: 39.4696 | ffd6fcbfdc24899e097957d26ff2d098 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precis... | db3a890677c829a797d09d2d00783738 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3647 | 1.05 | 500 | 0.6999 | 43.0430 | | 0.2752 | 3.04 | 1000 | 0.6002 | 39.3275 | | 0.2513 | 5.04 | 1500 | 0.5911 | 37.864... | 73ce702ce7dc989cf28be7d874ec6d4f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | 8ee2d58519ca4e6245ab4a1e12cc8899 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 179 | 3.7898 | 1.2975 | 20.8384 | | 8b3c1b375cc9aa12e2e83749568e249e |
creativeml-openrail-m | [] | false | Asou (artist) Style [Hypernetwork] Hypernetwork trained on art by artist [Asou](https://asou.fanbox.cc/). [](https://www.buymeacoffee.com/stricky) | 41b8cc5b4b6d4865f8b1376f254f1ff4 |
creativeml-openrail-m | [] | false | Settings ``` Model: NAI Layer structure: (1, 2, 1) Activation function: relu Layer normalization: False Use dropout: False Raw dataset size: 133 images Final dataset size: 532 images Size: 512x512 Create flipped copies: True Split oversized images: True Captions: DeepBooru Learning rate: 0.000005 -> 13000 steps Re... | d994f3f0a9df50c8323bf3543ad7c901 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_qnli_128 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9590 - Accuracy: 0.6194 | 90fd07f3be16a680db0e19df324dcfbd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1033 | 1.0 | 819 | 0.9703 | 0.6147 | | 1.0208 | 2.0 | 1638 | 0.9590 | 0.6194 | | 0.9764 | 3.0 | 2457 | 0.9706 | 0.... | 5869ff0edce90ec6c84268ee13c0094e |
mit | ['generated_from_trainer'] | false | mBART_tokenizer_custom This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the ํ๊ตญ์ด ๋ฐฉ์ธ ๋ฐํ(๊ฒฝ์๋) dataset from AI Hub. It achieves the following results on the evaluation set: - Loss: 0.0045 - Bleu: 78.9009 - Gen Len: 20.5578 | f4baa16c4637a5c945e90542a112c677 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.048 | 1.0 | 12500 | 0.0259 | 93.2721 | 22.2035 | | 0.0251 | 2.0 | 25000 | 0.0106 | 87.1169 | 21.7826 | | 0.0105 ... | d36de21e01ab04b3ff376908fae83ee4 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-TRAC-DS This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0206 - Accuracy: 0.6814 - Precision: 0.6561 - Recall: 0.6528 - F1: 0.6543 | 2205bae14fc97d80290825789575a73c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 43 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 719170d990fe4ca4f7555db334b5cdda |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.9928 | 0.5 | 612 | 0.9026 | 0.6201 | 0.5845 | 0.5812 | 0.5809 | | 0.8756 | 1.0 |... | bed3b1fa46aeeb5d5510c3cf148f79fe |
apache-2.0 | ['microsoft/deberta-v3-large'] | false | Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) | 08e5ddb21d509d674f3f62a1a6ab8dbf |
apache-2.0 | ['microsoft/deberta-v3-large'] | false | Performance
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on MNLI mismatched set: 90.49
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html | 0362db406d4a8d0fe306c8e6b6b84198 |
apache-2.0 | ['microsoft/deberta-v3-large'] | false | Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-deberta-v3-large') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving... | c9a84ac5d5334a2cdd10ef380834a6f9 |
apache-2.0 | ['text-generation', 'poetry'] | false | GPT2-Medium-Arabic-Poetry Fine-tuned [aubmindlab/aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) on the [Arabic Poetry Dataset (6th - 21st century)](https://www.kaggle.com/fahd09/arabic-poetry-dataset-478-2017) using 41,922 lines of poetry as the train split and 9,007 (by poets not in the train spli... | 8d2db2d486022567ce2f9be9c72b937e |
apache-2.0 | ['text-generation', 'poetry'] | false | Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed set_seed(42) model_name = "elgeish/gpt2-medium-arabic-poetry" model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "ูููููุฉ ุงูุฃููู ูุฑุฃุช ูู ุนูููู" input_i... | 1426024ec94d2e9d6bf4353651ae6677 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Fongbe) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="aioxlabs/dvoice-fongbe", savedir="pretrained_models/asr-wav2vec2-dvoice-fon") asr_model.transcribe_file('./the_path_to_your_audio_file') ``` | b904cd9c7fb952bc104f0e82eefab392 |
mit | [] | false | Dullboy Caricature on Stable Diffusion This is the `<dullboy-cari>` 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... | 6733c6e8cfebf0cb2f1952100c38eba2 |
apache-2.0 | ['generated_from_trainer'] | false | mrpc_bert-base-uncased_144 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4933 - Accuracy: 0.8480 - F1: 0.8935 - Combined Score: 0.8708 | 84e12ce4c62130c58990f0a4258c784d |
apache-2.0 | ['doe2vec', 'exploratory-landscape-analysis', 'autoencoders'] | false | Model description DoE2Vec model that can transform any design of experiments (function landscape) to a feature vector. For different input dimensions or sample size you require a different model. Each model name is build up like doe2vec-d{dimension\}-m{sample size}-ls{latent size}-{AE or VAE}-kl{Kl loss weight} ... | 625d200e17d6a5c93e47e5055a7752d2 |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on ้็ฉบๆๅบซ texts for POS-tagging and dependency-parsing, derived from [deberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-small-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universa... | 73d0f8fc523c1089f4191d0fcce584cf |
cc-by-sa-4.0 | ['japanese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-small-japanese-luw-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-small-japanese-luw-upos") s="ๅฝๅขใฎ้ทใใใณใใซใๆใใใจ้ชๅฝใงใใฃใใ"... | e521f4695f49c97fdc472d255ca362eb |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4414 - Wer: 0.3578 | 227221db51b4735b3ca9f7d1066948b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 | c6f17f0f22de90674f93ec46c748f7ac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.095 | 1.0 | 500 | 0.4785 | 0.3873 | | 0.09 | 2.01 | 1000 | 0.5840 | 0.4203 | | 0.1059 | 3.01 | 1500 | 0.5674 | 0.4073 | |... | 4e62401cd7460ed082a7866c3910e854 |
apache-2.0 | ['generated_from_trainer'] | false | finetune-sentiment-analysis-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4558 - Accuracy: 0.8867 - F1: 0.8944 | 6192cc1ae5d01a79d92a63d53d91e6cf |
creativeml-openrail-m | ['text-to-image'] | false | HassanBlend1.5 I am hassan, I created HassansBlend, the latest version currently is 1.5.1.2 I continue to iterate and improve on this model over time. Feel free to check out our discord or rentry page for more examples with prompts and outputs generated. This blend is finetuned over SD1.5 with thousands of images inc... | 7bdf2d9e40f4ba85568a4a5328ceb97e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4812 - Wer: 0.3557 | 43be3bb3aaf64b2eaeeca6ae312fe171 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4668 | 4.0 | 500 | 1.3753 | 0.9895 | | 0.6126 | 8.0 | 1000 | 0.4809 | 0.4350 | | 0.2281 | 12.0 | 1500 | 0.4407 | 0.4033 | |... | 7c00df9061568485385ac570d1fe813f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large V2 Malayalam This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the ICFOSS Malayalam Speech Corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.0617 - Wer: 44.1379 - Cer: 9.6895 | c81b79e711cc13a16ac2bf76e39c6018 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - 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... | ea7d89929d73e595b28f9a9c4143483a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.1071 | 0.13 | 500 | 0.1274 | 62.9885 | 15.0225 | | 0.0693 | 0.26 | 1000 | 0.1052 | 57.4713 | 13.0696 | | 0.054 |... | e30fae49fdfca9c78cbd530b40d9e9a5 |
gpl-3.0 | [] | false | Breast Estrogen Receptor (ER) GAN v1 Model Card This model card describes a model associated with a manuscript that is currently under review. Links to the manuscript will be provided once publicly available. | c5810c8544cbd3e209f4c5e86dcb94bd |
gpl-3.0 | [] | false | Model Details - **Developed by:** James Dolezal - **Model type:** Generative adversarial network - **Language(s):** English - **License:** GPL-3.0 - **Model Description:** This is a StyleGAN2 model that can generate synthetic H&E pathologic images of breast cancer. The GAN is conditioned on estrogen receptor (ER) stat... | 0a60b2fb4a2988f5697dd1d682dd81ce |
gpl-3.0 | [] | false | Examples This model is a [StyleGAN2](https://github.com/NVlabs/stylegan3) model and can be used with any StyleGAN-compatible scripts and tools. The [GitHub repository](https://github.com/jamesdolezal/histologic-sheep) associated with his model includes detailed information on how to interface with the GAN, generate im... | d9651aa65c9c303d3e927cc0b9404219 |
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 breast cancer. Excluded uses are described below. | 4fe942be9a8bb5c9618b8dcdb02dc052 |
gpl-3.0 | [] | false | Misuse and Out-of-Scope Use Output from this model should not be used in a clinical setting or be provided to 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 outside the context of ... | 5e2cdc60532fb4aab55a81fd3cdf3821 |
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 1,048 slides, with 228 ER-negative tumor and 820 ER-positive tumors. **Training Procedure** Each whole-slide image was sectioned into s... | 0bd5040c6dd4242a4b6fb211b7b7dbd7 |
gpl-3.0 | [] | false | vips-resize). During training, images are randomly flipped and rotated (90, 180, 270). Training is otherwise identical to the official StyleGAN2 implementation. Additional training information: - **Hardware:** 4 x A100 GPUs - **Batch size:** 32 - **R1 gamma:** 1.6384 - **Training time:** 10,000 kimg | 6b7f6f977878846f45069efcce10d080 |
mit | ['generated_from_keras_callback'] | false | FineTune_Vit5_LR0_00001_time2 This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6803 - Validation Loss: 0.7039 - Train Rouge1: 48.5820 - Train Rouge2: 26.3291 - Train Rougel:... | 158c88eb0b9e910c1f096781f2bcf694 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 0.6803 | 0.7039 | 48.5820 | 26.3291 ... | 29679c417600905119a291f0f2faee83 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql-with-cols This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset using a (slightly modified) training script by [Manuel Romero](https://huggingface.co/mrm8488). It achieves the following results on the evaluation set: - Loss: 0.0282 - Rouge2... | 48c9b195ad64600ebd7a05b1220a8d13 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.0557 | 1.0 | 4049 | 0.0384 | 0.9004 | 0.8038 | 0.84... | 727da214e016c6673f4a1a86f9c7eeb3 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/bart-base-subjqa-grocery-qg` This model is fine-tuned version of [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: grocery) via [`lmqg`](https://github.com/asahi417/... | 885f68cc1f6c52cb2f6d3269735d4433 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (grocery) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/... | 10ab831ebe5ab02ea96386659657a761 |
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", "lmqg/bart-base-subjqa-grocery-qg"... | 0b53df8f411c9d06c14548086d061974 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-subjqa-grocery-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | Score | Type | Dataset |... | d7aebfe87999c7289b5cd540dee00126 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: grocery - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: lmqg/bart-base-squad - max_length: 512 - max_length_output: 32 - epoch: 2 ... | b3ba127e1ff661eef7d4a230f86a330f |
apache-2.0 | ['generated_from_trainer'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8068 - Matthews Correlation: 0.5959 | 501e19e5db4548d05e3e0f336de12309 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4838 | 1.0 | 1069 | 0.5996 | 0.4637 | | 0.3543 | 2.0 | 2138 | 0.6670 | 0.5778 | | 0.1... | 8348896b678dbdf7531b0eb2a2edcbd4 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-zh_TW This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2408 - Rouge1: 15.8831 - Rouge2: 7.1676 - Rougel: 15.5523 - Rougelsum: 15.4954 | 371a7760fed09bed1994697e799bc022 |
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: 7 | 6b02d1c68d2272747027206644b65ce9 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 7.5388 | 1.0 | 838 | 3.5888 | 12.6081 | 5.3611 | 12.3495 | 12.2926 | | 4.0043 | 2.0 |... | c5fc775165abd6fd5aa2287e49abc164 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-tgl-eng-netspeak-trial6 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tl-en](https://huggingface.co/Helsinki-NLP/opus-mt-tl-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3343 - Bleu: 28.3769 | 7ac58c4649e6642a110de7ca915982ed |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 4.5424 | 1.0 | 57 | 3.8496 | 5.8759 | | 3.6414 | 2.0 | 114 | 3.5073 | 9.0838 | | 3.1777 | 3.0 | 171 | 3.2660 | 10.009... | 7a59138a627bee9eedc50c3d24364ab4 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the `mazapan` concept trained by kokuma on the `kokuma/figuritas-de-mazapan` dataset. This is a Stable Diffusion model fine-tuned on the `mazapan` concept with DreamBooth for the food theme.\ This model was created as part of the DreamBooth Hackathon ๐ฅ. Visit the [organisation page](https://huggi... | 8ef1d68720fc32da8658d37f4459e8c7 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | Prompts - **a cute X, mazapan**: `a cute bunny, mazapan` - **a cute X made of mazapan**: `a cute robot made of mazapan` - **a photograph of a cute X, mazapan**: `a photograph of a cute dog, mazapan` | a688d4db65806312a865317da659a919 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | Examples | a cute dog, mazapan | a cute sparrow, mazapan | a cute bear, mazapan | | -- | -- | -- | |  |  |  | | a cute koala, mazapan | a cute robot made of ... | 2922c50b6d3183379fd255545b30023f |
apache-2.0 | ['bert'] | false | Chinese BERT with Whole Word Masking Fix MLM Parameters Init parameters by https://huggingface.co/hfl/chinese-roberta-wwm-ext-large miss mlm parameters issue https://github.com/ymcui/Chinese-BERT-wwm/issues/98 Only train MLM parameters and freeze other parameters More info in github https://github.com/genggui001/c... | a7f28140ea9082c9780bf37175c51894 |
apache-2.0 | ['generated_from_trainer'] | false | mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__g_mt5_base_L2 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.0127 - Rouge2: 0.0 - Rougel: 0.012... | 797e5897384b359bba163f4dee4d6f29 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 131773 | nan | 0.0127 | 0.0 | 0.0128 | 0.0129 | 6.329... | b3811ea693a07b3b6181e448d487cc04 |
mit | [] | false | lex on Stable Diffusion This is the `<lex>` 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 train your ow... | cb6b82571e8b96815dcdc569a6b32604 |
other | ['stable-diffusion', 'text-to-image', 'core-ml'] | false | Stable Diffusion v2 Model Card This model was generated by Hugging Face using [Appleโs repository](https://github.com/apple/ml-stable-diffusion) which has [ASCL](https://github.com/apple/ml-stable-diffusion/blob/main/LICENSE.md). This model card focuses on the model associated with the Stable Diffusion v2 model, ava... | e9217e559e50c819cfea62e84d725d40 |
apache-2.0 | ['generated_from_keras_callback'] | false | test2 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: 0.2510 - Epoch: 0 | 465c87c053b4d9135dc7fdbe4c4e7828 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 7d14e9c4b3a35c33e5b3682eebe1439f |
creativeml-openrail-m | ['text-to-image'] | false | niamv1 Dreambooth model trained by gsingal with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/... | e49d53cd7323f7379df320cded28f964 |
apache-2.0 | ['generated_from_trainer'] | false | t5_large_baseline This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 - Rouge1: 99.8958 - Rouge2: 99.8696 - Rougel: 99.8958 - Rougelsum: 99.8958 - Gen Len: 46.715 | 540226d616e5b4d38c0ae33d03e3176e |
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: 4 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 3.0 | c8c823b1fa778aa6ebeb9f26694405f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.9852 | 0.33 | 50 | 0.1098 | 55.1421 | 49.8248 | 54.4294 | 54.7377 | 19... | fe56b9359aaeebba28529e7de1451215 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-fira 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: 2.7687 | 9ccc6c47b4864ab276eead3b2de01a3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 200 | 2.9963 | | No log | 2.0 | 400 | 2.7457 | | 3.0576 | 3.0 | 600 | 2.7687 | | 82cefd9c920c56f0c8ed5bfd7ac7a766 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 6528d70774a4ed7375f830ab1ea41936 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 09b7e475aec2171e1fb4875485f201a7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens') | 9d1d32f8f3c88bf9262701d92326ba31 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens) | 109803e4edcc43051486b66df4262188 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | S2T-SMALL-COVOST2-EN-ET-ST `s2t-small-covost2-en-et-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/spe... | 48895f37d0eb29d337304cfd0b901b78 |
mit | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | Intended uses & limitations This model can be used for end-to-end English speech to Estonian text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints. | e1ab5bc06d3c09f1d813b285486ddcd1 |
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