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 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 32 - eval_batch_size: 32 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 50 - ema_inv_gam... | 017203d5f5a9ef2291efd32e07f5ab7e |
mit | ['generated_from_trainer'] | false | deberta-v3-large-cola This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.5335 - Matthews Correlation: 0.7193 | 2181be4e12f6f647b7b16a0f1c352c07 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - 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: 3.0 | a979534c6d453f056db964e2fde9d161 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Lt This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5724 - Wer: 35.8598 | f135cd2a360499febb9d6078439b71ac |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0237 | 6.0 | 1000 | 0.4745 | 37.9839 | | 0.0016 | 12.01 | 2000 | 0.5128 | 35.9749 | | 0.0008 | 18.01 | 3000 | 0.5458 | 35.784... | d4ead414b45711ef7aba4b421cbd9fa4 |
cc0-1.0 | ['MaltBERTa', 'MaCoCu'] | false | Model description **XLMR-MaltBERTa** is a large pre-trained language model trained on Maltese texts. It was created by continuing training from the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model. It was developed as part of the [MaCoCu](https://macocu.eu/) project. The main developer is [Rik van ... | f18ac7cb688c7206cf793c4a8ac285f1 |
cc0-1.0 | ['MaltBERTa', 'MaCoCu'] | false | How to use ```python from transformers import AutoTokenizer, AutoModel, TFAutoModel tokenizer = AutoTokenizer.from_pretrained("RVN/XLMR-MaltBERTa") model = AutoModel.from_pretrained("RVN/XLMR-MaltBERTa") | bc03a69cc234302ff38fc2a04fcdd9e8 |
cc0-1.0 | ['MaltBERTa', 'MaCoCu'] | false | Benchmark performance We tested the performance of MaltBERTa on the UPOS and XPOS benchmark of the [Universal Dependencies](https://universaldependencies.org/) project. Moreover, we test on a Google Translated version of the COPA data set (see our [Github repo](https://github.com/RikVN/COPA) for details). We compare ... | f41169875b02ab6bf3a010125740673b |
cc0-1.0 | ['MaltBERTa', 'MaCoCu'] | false | Acknowledgements Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). The authors received funding from the European Union’s Connecting Europe Facility 2014- 2020 - CEF Telecom, under Grant Agreement No.INEA/CEF/ICT/A2020/2278341 (MaCoCu). | df243aa312f57944002128b6239a963d |
cc0-1.0 | ['MaltBERTa', 'MaCoCu'] | false | Citation If you use this model, please cite the following paper: ```bibtex @inproceedings{non-etal-2022-macocu, title = "{M}a{C}o{C}u: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages", author = "Ba{\~n}{\'o}n, Marta and Espl{\`a}-Gomis, Miquel and... | 211fe4c3b5e09df6b8019a224c4c71db |
mit | ['roberta-base', 'roberta-base-epoch_62'] | false | RoBERTa, Intermediate Checkpoint - Epoch 62 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 ... | 879edbbd71c71b6aff0b4f02770cc3d4 |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-2 430M is a L24-D1024 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it. ctx_len = 768 n_layer = 24 n_embd = 1024 Final checkpoint: 20220615-1080... | 6bebc2b51fb19cdeecbcc309410bc5ea |
mit | ['text-classification', 'zero-shot-classification'] | false | Model description This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https... | 7f585359593351a2d1de19ecf236acfb |
mit | ['text-classification', 'zero-shot-classification'] | false | Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c") sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU" candidate_labels = ["politics", ... | 0baa9fc9f63f9f8ee7b7b4fdb85d9f11 |
mit | ['text-classification', 'zero-shot-classification'] | false | NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c" tokenizer = AutoTokenizer.from_pretrained(model_name)... | eebfd09842b09ca221e306c4e8ab5861 |
mit | ['text-classification', 'zero-shot-classification'] | false | Training data This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://a... | 179ba72c76e3403c9fd7709209cef8b7 |
mit | ['text-classification', 'zero-shot-classification'] | false | Training procedure DeBERTa-v3-small-mnli-fever-docnli-ling-2c was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=3, | dcc7fd090a780f401af6ca9e0f4f48ac |
mit | ['text-classification', 'zero-shot-classification'] | false | Eval results The model was evaluated using the binary test sets for MultiNLI and ANLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy. mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c ---------|----------|---------|----------|----------|---... | 1bd03671eb8ec23cc259a2874d0012d3 |
mit | [] | false | Model description This language-music model takes [BART-base](https://huggingface.co/facebook/bart-base) fine-tunes on 282,870 English text-music pairs, where all scores are represented in ABC notation. It was introduced in the paper [Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task]... | ffdda06bc2a4e81f755e04568dd0143c |
mit | [] | false | Intended uses & limitations You can use this model for text-conditional music generation. All scores generated by this model can be written on one stave (for vocal solo or instrumental solo) in standard classical notation, and are in a variety of styles, e.g., blues, classical, folk, jazz, pop, and world music. We re... | 580a94a7cf7ddebb5aab931cfbefacf7 |
mit | [] | false | How to use Here is how to use this model in PyTorch: ```python import torch from samplings import top_p_sampling, temperature_sampling from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained('sander-wood/text-to-music') model = AutoModelForSeq2SeqLM.from_pretrained('s... | 063ab0ea4714a9b101ebc839f653b95a |
mit | [] | false | X:1 L:1/8 M:6/8 K:D A | BEE BEE | Bdf edB | BAF FEF | DFA BAF | BEE BEE | Bdf edB | BAF DAF | FED E2 :: A | Bef gfe | faf edB | BAF FEF | DFA BAF | Bef gfe | faf edB | BAF DAF | FED E2 :| X:2 L:1/8 M:6/8 K:D A |: DED F2 A | d2 f ecA | G2 B F2 A | E2 F GFE | DED F2 A | d2 f ecA | Bgf edc |1 d3 d2 A :|2 d3 d2 a ||... | 6785e0d827ca27c4ea15ffa5883fca1a |
mit | [] | false | X:1 L:1/8 M:4/4 K:F "F" CFG |"F" A6 z G |"Fm7" A3 G"Bb7" A3 G |"F" A6 z G |"F7" A4"Eb7" G4 |"F" F6 z F | "Dm" A3 G"Dm/C" A3 G |"Bb" A2"Gm" B2"C7" G3 G |"F" F8- |"Dm7""G7" F6 z2 |"C" C4 C3 C | "C7" C2 B,2"F" C4 |"F" C4 C3 C |"Dm" D2 C2"Dm/C" D4 |"Bb" D4 D3 D |"Bb" D2 C2"C7" D4 |"F" C8- | "F" C4"Gm" z C"C7" FG |"F" A6 ... | a45c000e333ec90616da82da89ec0415 |
mit | [] | false | This is a Chinese folk song from the Jiangnan region. It was created during the Qianlong era (1735-1796) of the Qing dynasty. Over time, many regional variations were created, and the song gained popularity both in China and abroad. One version of the song describes a custom of giving jasmine flowers, popular in the ... | 07a9d8dfac1bff1266f27c2990673a03 |
mit | [] | false | X:1 L:1/8 Q:1/4=100 M:2/4 K:C "^Slow" DA A2 | GA c2- | c2 G2 | c2 GF | GA/G/ F2 | E2 DC | DA A2 | GA c2- | c2 GA | cd- d2 | cA c2- | c2 GA | cd- d2 | cA c2- | c2 GA | c2 A2 | c2 d2 | cA c2- | c2 c2 | A2 G2 | F2 AG | F2 ED | CA,/C/ D2- | D2 CD | F2 A2 | G2 ED | CG A2 | G2 FD | CA,/C/ D2- | D2 CD | F2 A2 | G2 ED | C... | 6f3f21ae618d29fd8012879742ede8e8 |
mit | [] | false | BibTeX entry and citation info ```bibtex @inproceedings{ wu2023exploring, title={Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task}, author={Shangda Wu and Maosong Sun}, booktitle={The AAAI-23 Workshop on Creative AI Across Modalities}, year={2023}, url={https://openreview.net/forum?... | a4b5fdf4f9f3eed4710c65e4bd1e61c3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4101 | e994a16716eb9e10394e7d9c9423d658 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2109 | 1.0 | 8235 | 1.2303 | | 0.9385 | 2.0 | 16470 | 1.2412 | | 0.7448 | 3.0 | 24705 | 1.4101 | | cc56b1a5cb3d3cd8051529591ba5ec89 |
apache-2.0 | ['translation'] | false | opus-mt-sn-sv * source languages: sn * target languages: sv * OPUS readme: [sn-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | e4c106ee82888fb7cd26b0ffadafdd5e |
other | ['generated_from_trainer'] | false | TextGen_Opt350M This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6053 | 08a2f38e105d44492c3e4159de20e175 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5886 | 1.0 | 2056 | 3.5856 | | 3.2797 | 2.0 | 4112 | 3.5879 | | 3.0513 | 3.0 | 6168 | 3.6053 | | ed854e0149756b84855a575429ed0d22 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.8-2 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: 3.7465 - Bleu: 1.3564 - Gen Len: 89.6103 | 1f1b755577a33ca0d5003718beff7a96 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-ur This model is a fine-tuned version of [anuragshas/wav2vec2-large-xls-r-300m-ur](https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-ur) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 2.0508 - Wer: 0.7328 | c2339ee4cc3541ac5c4e989030e4ddd6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - 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_sc... | c5a22a93a88e37d8e8bfca0cfc626632 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 0.0719 | 66.67 | 400 | 1.8510 | 0.7432 | | 0.0284 | 133.33 | 800 | 2.0088 | 0.7415 | | 0.014 | 200.0 | 1200 | 2.0508 | 0.732... | b84517e4f48719470abac2f64a24a72e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncasedv1-finetuned-twitter-sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.3985 - Accuracy: 0.8247 - F1: 0.8246 - Precision: 0.8251... | 7ca7a9a9afad7808038a9e60543d7bab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 500 | 0.4049 | 0.8181 | 0.8178 | 0.8236 | 0.7862 | | No log | 2.0 |... | d5665cadbee2a220ffd68df13f2a18e2 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-V2 Slovenian - Drishti Sharma This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2118 - Wer: 13.8338 | ff04758eae2bfe2af68dec0781471cac |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0118 | 3.04 | 1000 | 0.2118 | 13.8338 | | 8a170d66f08067efcc1a78b9604a5f7e |
mit | ['bart', 'pytorch'] | false | bart-large-japanese This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University. Both the encoder and decoder outputs are identical to the original Fairseq model. | 3359b4fa177c522ea89f7e8a95a24c76 |
mit | ['bart', 'pytorch'] | false | How to use the model The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-large-japanese/blob/main/tokenization_bart_japanese.py). Tokenizer requirements: * [Juman++](https://github.com/ku-nlp/jumanpp) * [zenhan](https://pypi.org/project/zenhan/) * [pyknp](https://pypi.... | 6901afff578ca996b534c9e7851b29cd |
mit | ['bart', 'pytorch'] | false | Simple FillMaskPipeline ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline model_name = "Formzu/bart-large-japanese" model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) masked_text = "天気が<mask>から散歩しましょう... | b496b34099cff048e63598acccc50e20 |
mit | ['bart', 'pytorch'] | false | Text Generation ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "Formzu/bart-large-japanese" model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) tokenizer = AutoToken... | f4fde716597df4f03f8294f0d220ca13 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large-V2 Nepali This model is a fine-tuned version of [DrishtiSharma/whisper-large-v2-hindi-3k-steps](https://huggingface.co/DrishtiSharma/whisper-large-v2-hindi-3k-steps) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.8851 - Wer: 9.7561 | ee50d9c0d427545d8042ad41804b039e |
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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 200 - mixed_precision... | fb223a19c8bbfe2184719f47ca36bdb5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0 | 200.0 | 200 | 0.8851 | 9.7561 | | 4532b32d8f1772d1bc34093640026d6b |
mit | [] | false | Bluebey on Stable Diffusion This is the `<bluebey>` 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... | 78bc4c819dd29ef65dd69489cda2ece7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Raiden_Shogun_DB Dreambooth model trained by Falon 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/fast-stabl... | 6a299e6c9c8544abe4d06fe3e058bdff |
apache-2.0 | ['t5-lm-adapt'] | false | lm-adapted-t511lm100k) includes the following improvements compared to the original [T5 model](https://huggingface.co/t5-large): - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be r... | b9fd9dbaad0ee3078a5c63e0a9b46cef |
apache-2.0 | ['image-classification', 'pytorch', 'onnx'] | false | Usage instructions ```python from PIL import Image from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize from torchvision.transforms.functional import InterpolationMode from holocron.models import model_from_hf_hub model = model_from_hf_hub("frgfm/repvgg_a2").eval() img = Ima... | 55cef41739d13f584b54106df578b807 |
apache-2.0 | ['generated_from_trainer'] | false | correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3343 - Pre... | a87fdbc46b70c6f4c6160c13e9e9bfd1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4801 | 0.0352 | 0.0591 | 0.0441 | 0.7521 | | No log | 2.0 |... | a9599e453691655de17a324c7e50054b |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | platzi-roberta-bryan This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.6294 - Accuracy: 0.8309 - F1: 0.8787 | ae384d519c1ce2c39a6ec48a67124981 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3798 | 1.09 | 500 | 0.6294 | 0.8309 | 0.8787 | | 0.3876 | 2.18 | 1000 | 0.6294 | 0.8309 | 0.8787 | | 1a13294161b206b1aa11fb952d6e4ee5 |
apache-2.0 | ['generated_from_keras_callback'] | false | annaeze/lab9_2 This model is a fine-tuned version of [annaeze/lab9_1](https://huggingface.co/annaeze/lab9_1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0642 - Validation Loss: 0.0854 - Epoch: 2 | d5318cd4da4c6c8587c8852f81dcddf6 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.3518 | 0.1309 | 0 | | 0.0959 | 0.1059 | 1 | | 0.0642 | 0.0854 | 2 | | fbb66e76d28c1dee14148f192f9edf84 |
other | [] | false | diff-svc一键包 原项目地址：https://github.com/openvpi/diff-svc vst插件：https://github.com/zhaohui8969/VST_NetProcess-/tree/master 代码修改：@ChrisPreston 模型训练：@ChrisPreston 音源：Aqua Ch. 湊あくあ https://www.youtube.com/@MinatoAqua カバー株式会社 模型使用协议（重要）： 1. 请勿用于商业目的 2. 请勿用于会影响主播本人的行为（比如冒充本人发表争议言论） 3. 请勿用于血腥、暴力、性相关、政治相关内容 ... | 4af19f078fc74d60953655eb44abc8ad |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor from optimum.onnxruntime import ORTModelForImageClassification from optimum.pipelines import pipeline feature_extractor = AutoFeature... | 378a124da6c2c6968d6d5931a556baf3 |
apache-2.0 | ['vision', 'image-classification'] | false | Loading already converted and optimized ORT checkpoint for inference model = ORTModelForImageClassification.from_pretrained("optimum/vit-base-patch16-224") onnx_img_classif = pipeline( "image-classification", model=model, feature_extractor=feature_extractor ) url = "http://images.cocodataset.org/val2017/000000039... | 9b47e5397c1e49a3e5ae65eb948be0e5 |
mit | [] | false | scrap-style on Stable Diffusion This is the `<style-scrap>` 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 al... | b501d9fe0fff29e750b4937dffe026f0 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_16_02_2022-01_30_30 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1748 - Precision: 0.3384 - R... | 03c282d4481e550a17f7ca1584f2b90f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3180 | 0.0985 | 0.1648 | 0.1233 | 0.8643 | | No log | 2.0 |... | a9831466e1c7fe4ed991cf80de622ced |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_base_tf_384.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. | b9ec66d2d198f23c89b4ebacf5fb760e |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 119.7 - GMACs: 73.8 - Activations (M): 332.9 - Image size: 384 x 384 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k | 164549b6aea31382275ee7cf6f51dc90 |
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_base_tf_384.in1k', pretrained=True) model = mod... | 306e40343b23026f9007568fd570036b |
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_base_tf_384.in1k', pretrained=True, ... | fd478b8a0e148f9f8d8c7163959b5bb4 |
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_base_tf_384.in1k', pretrained=True, nu... | 0453e92d5401d1945d01af25e5eb2402 |
apache-2.0 | ['translation'] | false | opus-mt-fr-pon * source languages: fr * target languages: pon * OPUS readme: [fr-pon](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-pon/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 3b6919940977253cf3227b840bed8fdb |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_base_tf_512.in21k_ft_in1k An official MaxViT image classification model. Pretrained in tensorflow on ImageNet-21k (21843 Google specific instance of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/m... | 89ac0a229f31505e3d8b344039f3bc1b |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 119.9 - GMACs: 138.0 - Activations (M): 704.0 - Image size: 512 x 512 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k - **Pretrain Datase... | 122d97a936ac4932811a7ca8a404a385 |
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_base_tf_512.in21k_ft_in1k', pretrained=True) mo... | 3b0ed86567e8269130f313f6915de987 |
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_base_tf_512.in21k_ft_in1k', pretrain... | 39f4c9271bd60daf53d0282b598e13aa |
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_base_tf_512.in21k_ft_in1k', pretrained=Tru... | 9abc28c30ea5ff8efdb46ba642fab192 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-nl_s160 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (zh-CN)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 3c752037b211875fc7e83ff621046381 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-browser-extension 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: 0.7068 - Accuracy: 0.8516 - F1: 0.8690 | 492fe4fa9a620cf40d0aa7259a0e918c |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | Fast Anime PromptGen This model was trained on a dataset of **80,000** safe anime prompts for 3 epochs. I fetched the prompts from the [Safebooru API endpoint](https://safebooru.donmai.us/posts/random.json), but only accepted unique prompts with **up_score ≥ 8** and without any [blacklisted tags](./blacklist.txt). I... | 5ab627782b9e195d097476b0d34f82e2 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | Text-to-image Examples Prefix *1girl* | [Generated *1girl* prompts](./anime_girl_settings.txt) | Model *Anything V4*  Prefix *1boy* | [Generated *1boy* prompts](./anime_boy_settings.txt) | Model *Anything V4*  | e0f4654a48af69f4334fc2abe5e1fc6e |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | Contrastive Search ``` pip install --upgrade transformers ``` ```python import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2') tokenizer.add_special_tokens({'pad_token': '[PAD]'}) model = GPT2LMHeadModel.from_pretrained('FredZhang7/anime-a... | 44538c57b76285bbbffb957a7e3e626e |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | generate 10 samples using contrastive search outs = nlp(prompt, max_length=76, num_return_sequences=10, do_sample=True, repetition_penalty=1.2, temperature=0.7, top_k=4, early_stopping=True) print('\nInput:\n' + 100 * '-') print('\033[96m' + prompt + '\033[0m') print('\nOutput:\n' + 100 * '-') for i in range(len(outs... | eb47de3067aaf7b675d5b6803a03ef17 |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | remove trailing commas and double spaces outs[i] = str(outs[i]['generated_text']).replace(' ', '').rstrip(',') print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n') ``` Output Example:  Please see [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2) ... | 3a168875cbd051ef0bba6b4bf032fc7a |
creativeml-openrail-m | ['stable-diffusion', 'anime', 'anything-v4', 'art', 'arxiv:2210.14140'] | false | Awesome Tips - If you feel like a generated anime character doesn't show emotions, try emoticons like `;o`, `:o`, `;p`, `:d`, `:p`, and `;d` in the prompt. I also use `happy smirk`, `happy smile`, `laughing closed eyes`, etc. to make the characters more lively and expressive. - Adding `absurdres`, instead of `highre... | c4f6e016029d6eb2565fdc0870cdb339 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset. It achieves the following results on the evaluation set: - Loss: 0.1252 - Wer: 4.9888 | 5d896aa441046f7fc3b65f273e49b40d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | c30545d5998ae2be84426233eb0e19a6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2346 | 0.2 | 500 | 0.1957 | 8.5131 | | 0.1252 | 0.4 | 1000 | 0.1448 | 5.7876 | | 0.2076 | 0.6 | 1500 | 0.1361 | 5.5786 | |... | 45c7a142a3ce86752e4716ad07f5a8ff |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss w... | 0fc626a8be0d913cd5d97e6ae4730d96 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | How to use ```python import torchaudio from speechbrain.pretrained import EncoderClassifier language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn-ce", savedir="tmp") | 5b8b7538e30810235fcd019ef2612bf9 |
apache-2.0 | ['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107'] | false | Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3") prediction = language_id.classify_batch(signal) print(prediction) (tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01, -3.2668e+01... | 7ff4a6502a8552c10d25f44579fde712 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | kornwtp/ConGen-Multilingual-MiniLM-L12 This is a [ConGen](https://github.com/KornWtp/ConGen) model: It maps sentences to a 384 dimensional dense vector space and can be used for tasks like semantic search. | a33859d5179937d350c831d9518d632c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage Using this model becomes easy when you have [ConGen](https://github.com/KornWtp/ConGen) installed: ``` pip install -U git+https://github.com/KornWtp/ConGen.git ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence",... | 7a32cf01279ad8778d09c2a224a20128 |
mit | ['deberta', 'deberta-v3', 'mdeberta', 'question-answering'] | false | Evaluation on SQuAD2.0 dev set ``` { "epoch": 3.0, "eval_HasAns_exact": 79.65587044534414, "eval_HasAns_f1": 85.91387795001529, "eval_HasAns_total": 5928, "eval_NoAns_exact": 82.10260723296888, "eval_NoAns_f1": 82.10260723296888, "eval_NoAns_total": 5945, "eval_best_exact": 80.880990482... | ce6db823822a6fa7eb5a8b51a8eb902b |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny Belarusian Repo to test model training This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the mozilla-foundation/common_voice_11_0 be dataset. It achieves the following results on the evaluation set: - Loss: 0.4388 - Wer: 46.5201 | 8e8afb62c62dc3f2e92b18797bb71cf4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 300 - mixed_precisi... | aaa6d12bd8ef4609d7b8bbf2cc889e8a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 2.5366 | 0.05 | 10 | 1.5402 | 94.5055 | | 1.3721 | 0.1 | 20 | 1.0021 | 75.8242 | | 0.9921 | 0.15 | 30 | 0.8322 | 75.091... | c957755bbc54dd0e557dc592c9ded8af |
cc | [] | false | FeiArt Handpainted CG Diffusion is a custom diffusion model trained by @FeiArt_AiArt. It can be used to create Handpainted CG style images. To use it，you can use [FeiArt_Handpainted CG Diffusion](https://colab.research.google.com/drive/1u9ompOlBZMgIZc_KZvxIa3V6UD4Ch3dT?usp=sharing) If you create a fun image with thi... | 25dac51ea24e8a5c3599f170e164e68c |
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 dataset. It achieves the following results on the evaluation set: - Loss: 1.1006 | 21acfaf984e21e0cfa7ae7e197c69a74 |
apache-2.0 | ['generated_from_trainer'] | false | base-mlm-imdb-target-imdb This model is a fine-tuned version of [muhtasham/base-mlm-imdb](https://huggingface.co/muhtasham/base-mlm-imdb) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4659 - Accuracy: 0.8918 - F1: 0.9428 | bc3af1afba9c7cdea6267219d38d3eee |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2453 | 0.64 | 500 | 0.1892 | 0.9334 | 0.9656 | | 0.1764 | 1.28 | 1000 | 0.1267 | 0.9581 | 0.9786 | | 0.117 |... | 63a1e49cf63ec0533dea96763918cad8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.6218 - Accuracy: 0.7775 | 9257b037359ee2bdf9e5cc2a716a20d0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3214 | 1.0 | 4374 | 0.6218 | 0.7775 | | 0.1833 | 2.0 | 8748 | 0.7939 | 0.7695 | | 0.1228 | 3.0 | 13122 | 0.8713 ... | 11e513634455a0f8cca1386f6b51df8f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion 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: 0.2182 - Accuracy: 0.9265 - F1: 0.9266 | 39b9fc5b236fc6ee90b26851f4841e15 |
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