license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | List of sentences for comparison sentences_1 = ["This is a sentence for testing miCSE.", "This is using mutual information Contrastive Sentence Embeddings model."] sentences_2 = ["This is testing miCSE.", "Similarity with miCSE"] | 5540e5451520ade378928a48bb2abcb2 |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Benchmark Model results on SentEval Benchmark: <details> <summary> Click to expand </summary> ```shell +-------+-------+-------+-------+-------+--------------+-----------------+--------+ | STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | S.Avg. | +-------+-------+-------+-------+-------+--------------+-----------------+--------+ | 71.71 | 83.09 | 75.46 | 83.13 | 80.22 | 79.70 | 73.62 | 78.13 | +-------+-------+-------+-------+-------+--------------+-----------------+--------+ ``` </details> | a69be4dd909f6b219e0f47a27cae50aa |
apache-2.0 | ['feature-extraction', 'sentence-similarity'] | false | Citations If you use this code in your research or want to refer to our work, please cite: ``` @article{Klein2022miCSEMI, title={miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings}, author={Tassilo Klein and Moin Nabi}, journal={ArXiv}, year={2022}, volume={abs/2211.04928} } ``` | 038cdffe214bbc49d406b3646064b3b5 |
creativeml-openrail-m | [] | false | **UPDATE 9/NOV/2022: added 2 additional versions trained from Trinart Characters base. The 5000 steps version is probably the better one for most people as it is much more editable than the 6000 steps version. 6000 steps may be good for merging with other models.** waifu diffusion 1.3 base model with dreambooth training on images of Yume from Hai to Gensou no Grimgar (Grimgar of Ashes and Fantasies) Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use "m_yumegirl" to activate For stronger effect add "1girl, red hair, single braid, brown eyes" to prompt | 86911d4154efe1a1664972f1034cf0a2 |
apache-2.0 | [] | false | Vision-and-Language Transformer (ViLT), fine-tuned on VSR random split Vision-and-Language Transformer (ViLT) model fine-tuned on random split of [Visual Spatial Reasoning (VSR)](https://arxiv.org/abs/2205.00363). ViLT was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). | 784d0d4790ce4e22b1d68ed3bfafe7c3 |
apache-2.0 | [] | false | How to use Here is how to use the model in PyTorch: ``` from transformers import ViltProcessor, ViltForImagesAndTextClassification import requests from PIL import Image image = Image.open(requests.get("https://camo.githubusercontent.com/ffcbeada14077b8e6d4b16817c91f78ba50aace210a1e4754418f1413d99797f/687474703a2f2f696d616765732e636f636f646174617365742e6f72672f747261696e323031372f3030303030303038303333362e6a7067", stream=True).raw) text = "The person is ahead of the cow." processor = ViltProcessor.from_pretrained("juletxara/vilt-vsr-random") model = ViltForImagesAndTextClassification.from_pretrained("juletxara/vilt-vsr-random") | ec1338d11bff19510e805785c2e80943 |
apache-2.0 | [] | false | BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } @article{liu2022visual, title={Visual Spatial Reasoning}, author={Liu, Fangyu and Emerson, Guy and Collier, Nigel}, journal={arXiv preprint arXiv:2205.00363}, year={2022} } ``` | 3a19e851b80e310955402f451cf27a43 |
mit | ['generated_from_trainer'] | false | camembert-base-mrpc This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4286 - Accuracy: 0.8505 - F1: 0.8928 - Combined Score: 0.8716 | 28f556d801cc069f2519b603366e3330 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | cv1.2 Dreambooth model trained by ukeeba 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-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | ba605fd9c04734b83a6ceae2d1568b8e |
odc-by | [] | false | Basically, generate the images by saying "dnd[RACE] person" I know some arent people, but it's what I've got to work with. ;) Make sure there are no spaces, or punctuation in the "dnd[RACE HERE]" section, so "a portrait of dndYuanTi person, intricate, elegant, highly detailed, digital painting, artstation, trending, Volumetric lighting" Here is a list of all of them (Autognome is VERY undertrained...): * dndAarakocra * dndAasimar * dndAirGenasi * dndAstralElf * dndAutognome * dndBugbear * dndCentaur * dndChangeling * dndDeepGnome * dndDragonborn * dndDwarf * dndEarthGenasi * dndEladrin * dndElf * dndFairy * dndFirbolg * dndFireGenasi * dndGenasi * dndGiff * dndGith * dndGnome * dndGoblin * dndGoliath * dndGrung * dndHadozee * dndHalfElf * dndHalfling * dndHalfOrc * dndHarengon * dndHobgoblin * dndHuman * dndKalashtar * dndKenku * dndKobold * dndLeonin * dndLizardfolk * dndLocathah * dndLoxodon * dndMinotaur * dndOrc * dndOwlin * dndPlasmoid * dndRebornLineage * dndSatyr * dndSeaElf * dndShadarKai * dndShifter * dndSimicHybrid * dndTabaxi * dndThriKreen * dndTiefling * dndTortle * dndTriton * dndVedalken * dndVerdan * dndWarforged * dndWaterGenasi * dndYuanTi | eab46641407abbcf92343a1877439b81 |
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: 0.4526 - Wer: 0.3411 | 100f866e42dc6403b9f32711d37a7072 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.7503 | 4.0 | 500 | 2.4125 | 1.0006 | | 0.9595 | 8.0 | 1000 | 0.4833 | 0.4776 | | 0.3018 | 12.0 | 1500 | 0.4333 | 0.4062 | | 0.1751 | 16.0 | 2000 | 0.4474 | 0.3697 | | 0.1288 | 20.0 | 2500 | 0.4445 | 0.3558 | | 0.1073 | 24.0 | 3000 | 0.4695 | 0.3464 | | 0.0816 | 28.0 | 3500 | 0.4526 | 0.3411 | | baeffd258e3773ce15499a918df6da1d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_pretrain_wnli This model is a fine-tuned version of [gokuls/distilbert_add_pre-training-complete](https://huggingface.co/gokuls/distilbert_add_pre-training-complete) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3435 - Accuracy: 0.5634 | 3fc357ad13793874cd35e53b4b875345 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3566 | 1.0 | 3 | 0.3453 | 0.5634 | | 0.347 | 2.0 | 6 | 0.3435 | 0.5634 | | 0.3501 | 3.0 | 9 | 0.3465 | 0.5775 | | 0.3482 | 4.0 | 12 | 0.3435 | 0.5634 | | 0.3484 | 5.0 | 15 | 0.3458 | 0.5634 | | 0.3481 | 6.0 | 18 | 0.3478 | 0.5070 | | 0.3493 | 7.0 | 21 | 0.3444 | 0.5634 | | 0.3477 | 8.0 | 24 | 0.3446 | 0.5634 | | 0.3473 | 9.0 | 27 | 0.3456 | 0.5634 | | cc42ae18e1c8acb0ad3638b985b1a86f |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-rte This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3557 | e717cb5d45b7d983628429195759ba0d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5903 | 1.6 | 500 | 2.1820 | | 2.4763 | 3.21 | 1000 | 2.4737 | | 2.3778 | 4.81 | 1500 | 2.2902 | | 2.2735 | 6.41 | 2000 | 2.3557 | | 2eed95f3435728344e46a9d0796af701 |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1010 - Train End Logits Accuracy: 0.9653 - Train Start Logits Accuracy: 0.9826 - Validation Loss: 0.0420 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | a6a7f2879cd818a386e8ffb9efaaf364 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1010 | 0.9653 | 0.9826 | 0.0420 | 1.0 | 1.0 | 0 | | 618e5d129d205f98bbcbe7054338059c |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_250v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni250v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.3679 - Precision: 0.4748 - Recall: 0.3732 - F1: 0.4179 - Accuracy: 0.8847 | 2f66e4e73880d99bea486ed032f0c5fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 91 | 0.4333 | 0.2856 | 0.1851 | 0.2246 | 0.8440 | | No log | 2.0 | 182 | 0.3466 | 0.3907 | 0.3038 | 0.3418 | 0.8794 | | No log | 3.0 | 273 | 0.3679 | 0.4748 | 0.3732 | 0.4179 | 0.8847 | | 52c4ca1f8784660513ec2d320aa8dfc6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - margret-stalizburg-v1-lora These are LoRA adaption weights for [andite/anything-v4.0](https://huggingface.co/andite/anything-v4.0). The weights were trained on the instance prompt "margret stalizburg" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: margret stalizburg     | 15c2794d634cac8052c951626ab35c86 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | wedadams_pyros_bj Dreambooth model trained by tftgregrge 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-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) .jpg) | b890bef9664d7df6f5d31fe815fb5f55 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | Model Description This is a RoBERTa model pre-trained on Thai Wikipedia texts. You can fine-tune `roberta-base-thai-spm` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-thai-spm-ud-head), and so on. | 6c684c2d81b570f737c8bf781bdeed54 |
apache-2.0 | ['thai', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-thai-spm") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-thai-spm") ``` | 947f625db2b8924f3f5d691cc6534499 |
mit | ['generated_from_keras_callback'] | false | Sushant45/Warsaw_Pact-clustered This model is a fine-tuned version of [nandysoham16/12-clustered_aug](https://huggingface.co/nandysoham16/12-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0812 - Train End Logits Accuracy: 0.9757 - Train Start Logits Accuracy: 0.9826 - Validation Loss: 0.1680 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | 0b03d31a833b37bfc23648a4f0d59645 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.0812 | 0.9757 | 0.9826 | 0.1680 | 1.0 | 1.0 | 0 | | 80fdeaee5c24fd158c239d989a64b7c7 |
mit | [] | false | [](https://github.com/AlexeyAB/darknet/actions?query=workflow%3A%22Darknet+Continuous+Integration%22) | 7f2cd47c403296a776a444862d18f2ac |
mit | [] | false | Model YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56.8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100. YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWIN-L Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutional-based detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551% in speed and 0.7% AP in accuracy, as well as YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy. | 6eb93f26a2f461a9263d6ce0f86f2a32 |
mit | [] | false | Citation ``` @misc{bochkovskiy2020yolov4, title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao}, year={2020}, eprint={2004.10934}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ``` @InProceedings{Wang_2021_CVPR, author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {13029-13038} } ``` | e9af6c70ac5342486a78b3332ed0bf05 |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | Model Description This is a DeBERTa(V2) model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [deberta-base-chinese](https://huggingface.co/KoichiYasuoka/deberta-base-chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). | a007b3ecdf798aa91711eb72bbe3bd6e |
cc-by-sa-4.0 | ['chinese', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-chinese-upos") ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/deberta-base-chinese-upos") ``` | 0ca572cd3156e8f87b08840db3372d5f |
mit | [] | false | Model description It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers) | b8c2ddb7d4d45f073e3c8da7c90b9b42 |
mit | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='cahya/bert-base-indonesian-1.5G') >>> unmasker("Ibu ku sedang bekerja [MASK] supermarket") [{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]', 'score': 0.7983310222625732, 'token': 1495}, {'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]', 'score': 0.090003103017807, 'token': 17}, {'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]', 'score': 0.025469014421105385, 'token': 1600}, {'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]', 'score': 0.017966199666261673, 'token': 1555}, {'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]', 'score': 0.016971781849861145, 'token': 1572}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel model_name='cahya/bert-base-indonesian-1.5G' tokenizer = BertTokenizer.from_pretrained(model_name) model = BertModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in Tensorflow: ```python from transformers import BertTokenizer, TFBertModel model_name='cahya/bert-base-indonesian-1.5G' tokenizer = BertTokenizer.from_pretrained(model_name) model = TFBertModel.from_pretrained(model_name) text = "Silakan diganti dengan text apa saja." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | 801a76db231ead9836ea6025890359c4 |
mit | [] | false | Training data This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of [indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018). The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form: ```[CLS] Sentence A [SEP] Sentence B [SEP]``` | 8980bd1c1392d1d9fca213ead6ba9fe2 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-230113-130735 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.4320 - Mae: 0.4213 - Rmse: 0.6133 - Abs Rel: 0.4298 - Log Mae: 0.1697 - Log Rmse: 0.2216 - Delta1: 0.3800 - Delta2: 0.6396 - Delta3: 0.8189 | dcf9ea0b03840e767e9a2e0157ecbd88 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.0073 | 1.0 | 72 | 0.4927 | 0.4684 | 0.6425 | 0.5680 | 0.1955 | 0.2515 | 0.3154 | 0.5289 | 0.7834 | | 0.4694 | 2.0 | 144 | 0.4560 | 0.4425 | 0.6285 | 0.4674 | 0.1818 | 0.2341 | 0.3395 | 0.6061 | 0.7873 | | 0.4632 | 3.0 | 216 | 0.4817 | 0.4646 | 0.6341 | 0.5412 | 0.1930 | 0.2453 | 0.3181 | 0.5368 | 0.7491 | | 0.4363 | 4.0 | 288 | 0.4589 | 0.4379 | 0.6228 | 0.4880 | 0.1793 | 0.2348 | 0.3588 | 0.6025 | 0.7952 | | 0.4636 | 5.0 | 360 | 0.4767 | 0.4545 | 0.6301 | 0.5367 | 0.1878 | 0.2430 | 0.3279 | 0.5716 | 0.7705 | | 0.4642 | 6.0 | 432 | 0.4437 | 0.4185 | 0.6200 | 0.4405 | 0.1689 | 0.2283 | 0.4071 | 0.6531 | 0.8091 | | 0.409 | 7.0 | 504 | 0.4787 | 0.4542 | 0.6291 | 0.5399 | 0.1873 | 0.2430 | 0.3345 | 0.5679 | 0.7648 | | 0.4081 | 8.0 | 576 | 0.4545 | 0.4359 | 0.6258 | 0.4554 | 0.1779 | 0.2311 | 0.3717 | 0.6035 | 0.7952 | | 0.4146 | 9.0 | 648 | 0.4726 | 0.4523 | 0.6293 | 0.5108 | 0.1870 | 0.2403 | 0.3394 | 0.5692 | 0.7571 | | 0.392 | 10.0 | 720 | 0.4643 | 0.4453 | 0.6249 | 0.5081 | 0.1831 | 0.2372 | 0.3380 | 0.5881 | 0.7917 | | 0.3722 | 11.0 | 792 | 0.4670 | 0.4475 | 0.6245 | 0.4957 | 0.1838 | 0.2355 | 0.3413 | 0.5739 | 0.7689 | | 0.4397 | 12.0 | 864 | 0.4548 | 0.4367 | 0.6262 | 0.4604 | 0.1780 | 0.2319 | 0.3664 | 0.6081 | 0.7903 | | 0.43 | 13.0 | 936 | 0.4281 | 0.4223 | 0.6230 | 0.3974 | 0.1691 | 0.2207 | 0.3975 | 0.6426 | 0.7943 | | 0.3976 | 14.0 | 1008 | 0.4592 | 0.4470 | 0.6249 | 0.4759 | 0.1827 | 0.2321 | 0.3482 | 0.5784 | 0.7507 | | 0.4251 | 15.0 | 1080 | 0.4515 | 0.4366 | 0.6205 | 0.4589 | 0.1773 | 0.2285 | 0.3689 | 0.5990 | 0.7785 | | 0.4007 | 16.0 | 1152 | 0.4859 | 0.4668 | 0.6347 | 0.5570 | 0.1939 | 0.2467 | 0.3156 | 0.5378 | 0.7265 | | 0.376 | 17.0 | 1224 | 0.4529 | 0.4331 | 0.6195 | 0.4421 | 0.1752 | 0.2260 | 0.3795 | 0.6016 | 0.7702 | | 0.4028 | 18.0 | 1296 | 0.5027 | 0.4775 | 0.6420 | 0.6169 | 0.1993 | 0.2569 | 0.3098 | 0.5228 | 0.7035 | | 0.3816 | 19.0 | 1368 | 0.4869 | 0.4634 | 0.6342 | 0.5565 | 0.1924 | 0.2473 | 0.3276 | 0.5448 | 0.7370 | | 0.4092 | 20.0 | 1440 | 0.4317 | 0.4155 | 0.6164 | 0.4083 | 0.1661 | 0.2218 | 0.4003 | 0.6569 | 0.8123 | | 0.3673 | 21.0 | 1512 | 0.4433 | 0.4326 | 0.6208 | 0.4295 | 0.1750 | 0.2244 | 0.3751 | 0.6068 | 0.7879 | | 0.3698 | 22.0 | 1584 | 0.4607 | 0.4322 | 0.6216 | 0.4981 | 0.1758 | 0.2354 | 0.3831 | 0.6163 | 0.7906 | | 0.3771 | 23.0 | 1656 | 0.4668 | 0.4478 | 0.6255 | 0.5075 | 0.1841 | 0.2373 | 0.3390 | 0.5819 | 0.7697 | | 0.4343 | 24.0 | 1728 | 0.4532 | 0.4331 | 0.6203 | 0.4722 | 0.1767 | 0.2312 | 0.3587 | 0.6166 | 0.8087 | | 0.4011 | 25.0 | 1800 | 0.4499 | 0.4327 | 0.6213 | 0.4519 | 0.1755 | 0.2279 | 0.3716 | 0.6152 | 0.7844 | | 0.3714 | 26.0 | 1872 | 0.4460 | 0.4254 | 0.6188 | 0.4495 | 0.1716 | 0.2278 | 0.3932 | 0.6352 | 0.7916 | | 0.3436 | 27.0 | 1944 | 0.4360 | 0.4182 | 0.6165 | 0.4192 | 0.1682 | 0.2224 | 0.3894 | 0.6524 | 0.8145 | | 0.3698 | 28.0 | 2016 | 0.4694 | 0.4536 | 0.6274 | 0.5040 | 0.1863 | 0.2369 | 0.3356 | 0.5667 | 0.7469 | | 0.365 | 29.0 | 2088 | 0.4288 | 0.4139 | 0.6156 | 0.4025 | 0.1655 | 0.2199 | 0.4028 | 0.6623 | 0.8109 | | 0.3723 | 30.0 | 2160 | 0.4337 | 0.4148 | 0.6141 | 0.4192 | 0.1661 | 0.2215 | 0.4044 | 0.6578 | 0.8073 | | 0.365 | 31.0 | 2232 | 0.4529 | 0.4309 | 0.6192 | 0.4751 | 0.1755 | 0.2314 | 0.3770 | 0.6115 | 0.7909 | | 0.3571 | 32.0 | 2304 | 0.4302 | 0.4151 | 0.6170 | 0.4134 | 0.1663 | 0.2227 | 0.4089 | 0.6611 | 0.8078 | | 0.3727 | 33.0 | 2376 | 0.4599 | 0.4352 | 0.6214 | 0.4937 | 0.1776 | 0.2348 | 0.3659 | 0.6120 | 0.7949 | | 0.3538 | 34.0 | 2448 | 0.4391 | 0.4257 | 0.6161 | 0.4404 | 0.1720 | 0.2248 | 0.3768 | 0.6317 | 0.8042 | | 0.3306 | 35.0 | 2520 | 0.4393 | 0.4223 | 0.6198 | 0.4328 | 0.1702 | 0.2262 | 0.3886 | 0.6493 | 0.8062 | | 0.3369 | 36.0 | 2592 | 0.4496 | 0.4316 | 0.6182 | 0.4642 | 0.1751 | 0.2289 | 0.3712 | 0.6124 | 0.8005 | | 0.3389 | 37.0 | 2664 | 0.4573 | 0.4376 | 0.6213 | 0.4897 | 0.1787 | 0.2338 | 0.3628 | 0.6014 | 0.7932 | | 0.3767 | 38.0 | 2736 | 0.4558 | 0.4366 | 0.6216 | 0.4840 | 0.1786 | 0.2334 | 0.3566 | 0.6064 | 0.7973 | | 0.3462 | 39.0 | 2808 | 0.4580 | 0.4380 | 0.6221 | 0.4815 | 0.1785 | 0.2328 | 0.3640 | 0.6020 | 0.7850 | | 0.3834 | 40.0 | 2880 | 0.4664 | 0.4459 | 0.6245 | 0.5155 | 0.1836 | 0.2385 | 0.3426 | 0.5782 | 0.7944 | | 0.3564 | 41.0 | 2952 | 0.4452 | 0.4271 | 0.6175 | 0.4563 | 0.1733 | 0.2282 | 0.3749 | 0.6269 | 0.8081 | | 0.3571 | 42.0 | 3024 | 0.4357 | 0.4189 | 0.6151 | 0.4360 | 0.1686 | 0.2243 | 0.3947 | 0.6482 | 0.8163 | | 0.345 | 43.0 | 3096 | 0.4285 | 0.4130 | 0.6114 | 0.4173 | 0.1653 | 0.2202 | 0.4034 | 0.6611 | 0.8223 | | 0.3163 | 44.0 | 3168 | 0.4473 | 0.4274 | 0.6176 | 0.4624 | 0.1732 | 0.2288 | 0.3790 | 0.6245 | 0.8095 | | 0.3331 | 45.0 | 3240 | 0.4392 | 0.4214 | 0.6139 | 0.4429 | 0.1699 | 0.2244 | 0.3887 | 0.6388 | 0.8081 | | 0.3574 | 46.0 | 3312 | 0.4487 | 0.4230 | 0.6156 | 0.4608 | 0.1710 | 0.2282 | 0.3860 | 0.6431 | 0.8063 | | 0.3703 | 47.0 | 3384 | 0.4342 | 0.4176 | 0.6179 | 0.4286 | 0.1678 | 0.2247 | 0.3918 | 0.6668 | 0.8098 | | 0.325 | 48.0 | 3456 | 0.4390 | 0.4238 | 0.6150 | 0.4500 | 0.1715 | 0.2256 | 0.3695 | 0.6334 | 0.8216 | | 0.3494 | 49.0 | 3528 | 0.4364 | 0.4182 | 0.6165 | 0.4348 | 0.1680 | 0.2248 | 0.4041 | 0.6539 | 0.8104 | | 0.3439 | 50.0 | 3600 | 0.4401 | 0.4252 | 0.6156 | 0.4414 | 0.1716 | 0.2243 | 0.3831 | 0.6260 | 0.8042 | | 0.3235 | 51.0 | 3672 | 0.4459 | 0.4258 | 0.6173 | 0.4607 | 0.1728 | 0.2287 | 0.3819 | 0.6272 | 0.8106 | | 0.3197 | 52.0 | 3744 | 0.4341 | 0.4205 | 0.6153 | 0.4291 | 0.1691 | 0.2226 | 0.3874 | 0.6429 | 0.8173 | | 0.3231 | 53.0 | 3816 | 0.4499 | 0.4297 | 0.6180 | 0.4654 | 0.1745 | 0.2290 | 0.3730 | 0.6166 | 0.8053 | | 0.3182 | 54.0 | 3888 | 0.4407 | 0.4242 | 0.6145 | 0.4501 | 0.1714 | 0.2252 | 0.3762 | 0.6366 | 0.8124 | | 0.334 | 55.0 | 3960 | 0.4518 | 0.4335 | 0.6176 | 0.4773 | 0.1768 | 0.2304 | 0.3591 | 0.6065 | 0.8111 | | 0.3198 | 56.0 | 4032 | 0.4505 | 0.4322 | 0.6173 | 0.4725 | 0.1760 | 0.2298 | 0.3637 | 0.6131 | 0.8025 | | 0.3165 | 57.0 | 4104 | 0.4378 | 0.4248 | 0.6174 | 0.4369 | 0.1720 | 0.2246 | 0.3729 | 0.6377 | 0.8137 | | 0.3269 | 58.0 | 4176 | 0.4372 | 0.4275 | 0.6156 | 0.4415 | 0.1730 | 0.2240 | 0.3675 | 0.6276 | 0.8095 | | 0.3224 | 59.0 | 4248 | 0.4359 | 0.4244 | 0.6149 | 0.4351 | 0.1711 | 0.2231 | 0.3721 | 0.6366 | 0.8090 | | 0.3104 | 60.0 | 4320 | 0.4317 | 0.4209 | 0.6146 | 0.4284 | 0.1696 | 0.2220 | 0.3799 | 0.6395 | 0.8179 | | 0.3248 | 61.0 | 4392 | 0.4323 | 0.4207 | 0.6138 | 0.4268 | 0.1694 | 0.2216 | 0.3864 | 0.6386 | 0.8148 | | 0.303 | 62.0 | 4464 | 0.4309 | 0.4189 | 0.6126 | 0.4264 | 0.1685 | 0.2213 | 0.3853 | 0.6453 | 0.8194 | | 0.3126 | 63.0 | 4536 | 0.4308 | 0.4206 | 0.6141 | 0.4229 | 0.1693 | 0.2208 | 0.3783 | 0.6447 | 0.8162 | | 0.3099 | 64.0 | 4608 | 0.4330 | 0.4239 | 0.6149 | 0.4298 | 0.1709 | 0.2218 | 0.3709 | 0.6323 | 0.8182 | | 0.3075 | 65.0 | 4680 | 0.4322 | 0.4222 | 0.6144 | 0.4276 | 0.1701 | 0.2217 | 0.3784 | 0.6374 | 0.8159 | | 0.3024 | 66.0 | 4752 | 0.4393 | 0.4269 | 0.6155 | 0.4456 | 0.1729 | 0.2249 | 0.3722 | 0.6245 | 0.8100 | | 0.3319 | 67.0 | 4824 | 0.4385 | 0.4273 | 0.6155 | 0.4402 | 0.1728 | 0.2238 | 0.3722 | 0.6244 | 0.8085 | | 0.3163 | 68.0 | 4896 | 0.4334 | 0.4215 | 0.6128 | 0.4305 | 0.1699 | 0.2216 | 0.3814 | 0.6379 | 0.8145 | | 0.3219 | 69.0 | 4968 | 0.4298 | 0.4197 | 0.6131 | 0.4215 | 0.1688 | 0.2203 | 0.3821 | 0.6453 | 0.8170 | | 0.3155 | 70.0 | 5040 | 0.4295 | 0.4199 | 0.6134 | 0.4219 | 0.1687 | 0.2204 | 0.3846 | 0.6453 | 0.8164 | | 0.3265 | 71.0 | 5112 | 0.4294 | 0.4194 | 0.6123 | 0.4232 | 0.1687 | 0.2203 | 0.3804 | 0.6468 | 0.8203 | | 0.3231 | 72.0 | 5184 | 0.4338 | 0.4231 | 0.6138 | 0.4333 | 0.1707 | 0.2222 | 0.3775 | 0.6340 | 0.8166 | | 0.3077 | 73.0 | 5256 | 0.4327 | 0.4221 | 0.6134 | 0.4315 | 0.1702 | 0.2219 | 0.3800 | 0.6361 | 0.8185 | | 0.3178 | 74.0 | 5328 | 0.4312 | 0.4203 | 0.6126 | 0.4278 | 0.1693 | 0.2212 | 0.3813 | 0.6417 | 0.8194 | | 0.3157 | 75.0 | 5400 | 0.4320 | 0.4213 | 0.6133 | 0.4298 | 0.1697 | 0.2216 | 0.3800 | 0.6396 | 0.8189 | | 16b05284aff4f90fd4b494a195d0a8ee |
apache-2.0 | ['generated_from_trainer'] | false | stance_detection This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) towards 26 US SPAC stock mergers on Twitter. It achieves the following results on the evaluation set: - Loss: 0.4906 - Accuracy: 0.8409 - F1w: 0.8574 - Acc0: 0.8293 - Acc1: 0.6 - Acc2: 0.7652 - Acc3: 0.8637 | 4441dc54e3b9bd3465ce730cb66f59c6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1w | Acc0 | Acc1 | Acc2 | Acc3 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:----:|:------:|:------:| | 0.7748 | 1.0 | 194 | 0.5172 | 0.8158 | 0.8297 | 0.8699 | 0.0 | 0.7429 | 0.8248 | | 0.5181 | 2.0 | 388 | 0.4692 | 0.8509 | 0.8587 | 0.8699 | 0.4 | 0.7429 | 0.8743 | | 0.3868 | 3.0 | 582 | 0.4906 | 0.8409 | 0.8574 | 0.8293 | 0.6 | 0.7652 | 0.8637 | | 193738740dee1bc1f0f6d7986c1061df |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | messy_sketch_art_style Dreambooth model trained by apurik-parv with [Shivamshri rao's DreamBooth implementation] Instance prompt:**meartsty** As the name implies the the model is trained on messy art style sketch /doodle images for 50000 steps. Simple prompts can replicate faithfully. complicated and contradicting prompts will add elements of noise to the image. Feel free to experiment with it. | fc8269cb46d6bb82a9d54821a51f8a4d |
apache-2.0 | ['generated_from_trainer'] | false | text-to-sparql-t5-base-2021-10-18_16-15 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1294 - Gen Len: 19.0 - Bertscorer-p: 0.5827 - Bertscorer-r: 0.0812 - Bertscorer-f1: 0.3202 - Sacrebleu-score: 5.9410 - Sacrebleu-precisions: [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601] - Bleu-bp: 0.0721 | bc897b980515a5d5cbb8979e5216e12b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| | nan | 1.0 | 4772 | 0.1294 | 19.0 | 0.5827 | 0.0812 | 0.3202 | 5.9410 | [92.24641734333713, 84.24354361048307, 78.78523204758982, 75.43428275229601] | 0.0721 | | 342c9f754b5c088fbbffd4b473441ba8 |
apache-2.0 | ['generated_from_trainer'] | false | t5_8_3e-5_datav2_min30_lp2_sample This model is a fine-tuned version of [KETI-AIR/ke-t5-large-ko](https://huggingface.co/KETI-AIR/ke-t5-large-ko) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.2375 - Rouge1: 24.1102 - Rouge2: 5.3137 - Rougel: 16.1086 - Bleu1: 18.6424 - Bleu2: 8.0483 - Bleu3: 2.7046 - Bleu4: 0.7308 - Gen Len: 36.4012 | 48b8398311266e9bc7d469f56ff17de7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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_ratio: 0.1 - num_epochs: 10.0 | 6f6d641c848d10479285b78216f89050 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:-------:|:------:|:------:|:------:|:-------:| | 4.1641 | 1.04 | 5000 | 6.8094 | 21.6187 | 4.959 | 14.8344 | 16.9553 | 7.4791 | 2.8017 | 1.1852 | 38.0426 | | 3.1804 | 2.08 | 10000 | 5.6664 | 22.2631 | 5.127 | 15.5533 | 16.881 | 7.515 | 2.8628 | 1.0614 | 33.7325 | | 2.779 | 3.12 | 15000 | 5.3350 | 22.5781 | 5.1137 | 15.7717 | 16.8632 | 7.3067 | 2.7117 | 0.9906 | 31.459 | | 2.4111 | 4.15 | 20000 | 5.2687 | 24.4915 | 6.003 | 16.8096 | 18.5998 | 8.54 | 3.4084 | 1.1511 | 32.7477 | | 2.2192 | 5.19 | 25000 | 5.3300 | 24.9661 | 6.0773 | 16.8486 | 19.0105 | 8.6794 | 3.4052 | 1.3281 | 32.9696 | | 1.9306 | 6.23 | 30000 | 5.4806 | 24.8662 | 5.9711 | 16.235 | 19.2093 | 8.7044 | 3.2412 | 1.0675 | 35.0973 | | 1.6696 | 7.27 | 35000 | 5.6865 | 24.3913 | 5.6936 | 16.4663 | 18.5884 | 8.3035 | 2.9593 | 1.0997 | 34.617 | | 1.4566 | 8.31 | 40000 | 5.8677 | 24.9166 | 5.8251 | 16.647 | 19.0703 | 8.5159 | 3.3477 | 1.1257 | 35.1763 | | 1.2808 | 9.35 | 45000 | 6.2375 | 24.1102 | 5.3137 | 16.1086 | 18.6424 | 8.0483 | 2.7046 | 0.7308 | 36.4012 | | e959f6e9389363f84a2e0d663c41c290 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-OnionOrNot 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: 0.2039 - Accuracy: 0.9224 - F1: 0.9218 | 7186973c15ac27407088fef4cf9e9dba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3334 | 1.0 | 300 | 0.2382 | 0.9024 | 0.9011 | | 0.1822 | 2.0 | 600 | 0.2039 | 0.9224 | 0.9218 | | 91f94898bf8eb1c8eb1a749ccf762d35 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | fastbooth-jsjessy-1400 Dreambooth model trained by eicu 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-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 44b387e16c9b6e6c709b50e07facef84 |
mit | [] | false | MSG on Stable Diffusion This is the `<MSG69>` 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 own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:                            | 13558a88218deb824aa7c6bfb06e6112 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-credit_cards-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3376 - Accuracy: 0.3186 | 629864f8214bbe89b6c8f3460571740d |
creativeml-openrail-m | ['text-to-image', 'art', 'digital art', 'stable diffusion'] | false | [](https://huggingface.co/spaces/MultiversexPeeps/duskfall-s-general-digital-art-model) | 37f76b1def2e5361172870030fabdeff |
creativeml-openrail-m | ['text-to-image', 'art', 'digital art', 'stable diffusion'] | false | Duskfall's General Digital Art Model Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Information on this model will be here: https://civitai.com/user/duskfallcrew If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk gendigi (use that on your prompt) | 2a8ea3997f2a98b2bb33434926498ff4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-j-phoneme-common-test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Wer: 0.0001 | d8321534e1ba71e11c1020c177916eae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP | 3d733b17fec203b6ee6368a6c33fe8ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1488 | 7.14 | 2000 | 0.0788 | 0.0919 | | 0.0308 | 14.28 | 4000 | 0.0155 | 0.0271 | | 0.0121 | 21.43 | 6000 | 0.0070 | 0.0103 | | 0.0067 | 28.57 | 8000 | 0.0059 | 0.0067 | | 0.0025 | 35.71 | 10000 | 0.0143 | 0.0180 | | 0.0001 | 42.85 | 12000 | 0.0000 | 0.0001 | | 0.0 | 50.0 | 14000 | 0.0000 | 0.0001 | | 8af7429a5f2aef955ede0d479adb771c |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Slovak CV11 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 sk dataset. It achieves the following results on the evaluation set: - Loss: 0.3982 - Wer: 23.1437 | 86a0bbc41eb4d7e1b285f883dbbe8095 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.001 | 14.29 | 1000 | 0.3982 | 23.1437 | | 0.0013 | 28.57 | 2000 | 0.4343 | 24.0362 | | 0.0001 | 42.86 | 3000 | 0.4565 | 23.3222 | | 0.0001 | 57.14 | 4000 | 0.4700 | 23.3936 | | 0.0001 | 71.43 | 5000 | 0.4753 | 23.4531 | | f1f353dcd2679d3b8e9e69d871b2ec20 |
apache-2.0 | ['translation'] | false | opus-mt-ng-en * source languages: ng * target languages: en * OPUS readme: [ng-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ng-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.eval.txt) | 6d99a487028fa55970fd4cb2f7cecbf5 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run tpkify-v1: [Open in Spaces](https://huggingface.co/spaces/akhaliq/tpkify-v1) thelastben fast-dreambooth sd1.5 model for turning things into toothpick art. use trigger tpkify. ex: a photo of a tpkify dog, sitting on the beach ex: oil painting of a tpkify corvette, by claude monet this v1 iteration was trained on 40 images for 3200 steps with 20% text encoder training 40 512x512 training .png images included in train_images40.zip | 5b020cfab4f88f5b6b5aa8d1651c869a |
apache-2.0 | ['generated_from_trainer'] | false | 20NG_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2209 - Accuracy: 0.6067 | bc6e6a4cbebe1bf0d71542e74fb3029d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - 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 | b1096e6ac244cfb2c5762fc167347955 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8602 | 0.07 | 50 | 2.5794 | 0.2133 | | 2.3635 | 0.14 | 100 | 2.0956 | 0.38 | | 2.1526 | 0.21 | 150 | 1.9011 | 0.4467 | | 1.9014 | 0.28 | 200 | 1.6340 | 0.5067 | | 1.6736 | 0.35 | 250 | 1.5457 | 0.5467 | | 1.5563 | 0.42 | 300 | 1.5041 | 0.5533 | | 1.4338 | 0.49 | 350 | 1.3933 | 0.5933 | | 1.3348 | 0.56 | 400 | 1.4123 | 0.54 | | 1.2879 | 0.64 | 450 | 1.3352 | 0.6333 | | 1.2864 | 0.71 | 500 | 1.3027 | 0.62 | | 1.2162 | 0.78 | 550 | 1.2734 | 0.6267 | | 1.1786 | 0.85 | 600 | 1.2695 | 0.5933 | | 1.1702 | 0.92 | 650 | 1.2379 | 0.5933 | | 1.2338 | 0.99 | 700 | 1.2209 | 0.6067 | | ccf374a6586e7f34ef4037bb7c504908 |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned Bert for French
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: French (fr)
- model type: finetuned Bert
| a1a7cc1145ab23062e947f740708d753 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-fr-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-fr-bert")
````
| e6c18d30506e157310e8ad3006cb5c00 |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion fine tuned on art by [Björn Hurri](https://www.artstation.com/bjornhurri) This model is fine tuned on some of his "shiny"-style paintings. I also have a version for his "matte" works. | 1c3be4d46b5ba4f0c8ba8aaa82877d6b |
cc0-1.0 | ['stable-diffusion', 'text-to-image'] | false | Samples For this model I made two checkpoints. The "hurrishiny monster x2" model is trained for twice as long as the regular checkpoint, meaning it should be more fine tuned on the style but also more rigid. The top 4 images are from the regular version, the rest are from the x2 version. I hope it gives you an idea of what kind of styles can be created with this model. <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/1700_1.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/1700_2.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/1700_3.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/1700_4.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/3400_1.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/3400_2.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/3400_3.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/3400_4.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/index1.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/index3.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/index5.png" width="256px"/> <img src="https://huggingface.co/Froddan/hurrishiny/resolve/main/index6.png" width="256px"/> | 63572db22518feb9c56bde085d4dd6fe |
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 the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0649 - Precision: 0.9330 - Recall: 0.9485 - F1: 0.9407 - Accuracy: 0.9854 | c7bc7a677c724e79fe8da1f15f967599 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0871 | 1.0 | 1756 | 0.0672 | 0.9209 | 0.9387 | 0.9297 | 0.9834 | | 0.0394 | 2.0 | 3512 | 0.0584 | 0.9311 | 0.9505 | 0.9407 | 0.9857 | | 0.0201 | 3.0 | 5268 | 0.0649 | 0.9330 | 0.9485 | 0.9407 | 0.9854 | | e60f72d216a2f90a71bc7f6459050d58 |
mit | ['generated_from_keras_callback'] | false | nandysoham/Cardinal__Catholicism_-clustered This model is a fine-tuned version of [nandysoham16/11-clustered_aug](https://huggingface.co/nandysoham16/11-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2081 - Train End Logits Accuracy: 0.9444 - Train Start Logits Accuracy: 0.9549 - Validation Loss: 1.0270 - Validation End Logits Accuracy: 0.75 - Validation Start Logits Accuracy: 0.75 - Epoch: 0 | c0d46db8ca5699f70b3c01d432ff7685 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2081 | 0.9444 | 0.9549 | 1.0270 | 0.75 | 0.75 | 0 | | b09b760a8265754d53d3fc2c40723c72 |
apache-2.0 | ['translation'] | false | opus-mt-ee-sv * source languages: ee * target languages: sv * OPUS readme: [ee-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ee-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/ee-sv/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ee-sv/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ee-sv/opus-2020-01-08.eval.txt) | 7a3d1686a8dd3769dc3d96c243077a6d |
apache-2.0 | [] | false | Model description ALBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Sentence Ordering Prediction (SOP): ALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ALBERT model as inputs. ALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers. This is the first version of the xlarge model. Version 2 is different from version 1 due to different dropout rates, additional training data, and longer training. It has better results in nearly all downstream tasks. This model has the following configuration: - 24 repeating layers - 128 embedding dimension - 2048 hidden dimension - 16 attention heads - 58M parameters | eff4bb373c7e8ece41d85ea22c437b86 |
apache-2.0 | [] | false | How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v1') >>> unmasker("Hello I'm a [MASK] model.") [ { "sequence":"[CLS] hello i'm a modeling model.[SEP]", "score":0.05816134437918663, "token":12807, "token_str":"â–modeling" }, { "sequence":"[CLS] hello i'm a modelling model.[SEP]", "score":0.03748830780386925, "token":23089, "token_str":"â–modelling" }, { "sequence":"[CLS] hello i'm a model model.[SEP]", "score":0.033725276589393616, "token":1061, "token_str":"â–model" }, { "sequence":"[CLS] hello i'm a runway model.[SEP]", "score":0.017313428223133087, "token":8014, "token_str":"â–runway" }, { "sequence":"[CLS] hello i'm a lingerie model.[SEP]", "score":0.014405295252799988, "token":29104, "token_str":"â–lingerie" } ] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AlbertTokenizer, AlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xlarge-v1') model = AlbertModel.from_pretrained("albert-xlarge-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import AlbertTokenizer, TFAlbertModel tokenizer = AlbertTokenizer.from_pretrained('albert-xlarge-v1') model = TFAlbertModel.from_pretrained("albert-xlarge-v1") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | 4ebe5ecd53f5393db4e95f8f6899bf60 |
apache-2.0 | [] | false | Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='albert-xlarge-v1') >>> unmasker("The man worked as a [MASK].") [ { "sequence":"[CLS] the man worked as a chauffeur.[SEP]", "score":0.029577180743217468, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the man worked as a janitor.[SEP]", "score":0.028865724802017212, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the man worked as a shoemaker.[SEP]", "score":0.02581118606030941, "token":29024, "token_str":"â–shoemaker" }, { "sequence":"[CLS] the man worked as a blacksmith.[SEP]", "score":0.01849772222340107, "token":21238, "token_str":"â–blacksmith" }, { "sequence":"[CLS] the man worked as a lawyer.[SEP]", "score":0.01820771023631096, "token":3672, "token_str":"â–lawyer" } ] >>> unmasker("The woman worked as a [MASK].") [ { "sequence":"[CLS] the woman worked as a receptionist.[SEP]", "score":0.04604868218302727, "token":25331, "token_str":"â–receptionist" }, { "sequence":"[CLS] the woman worked as a janitor.[SEP]", "score":0.028220869600772858, "token":29477, "token_str":"â–janitor" }, { "sequence":"[CLS] the woman worked as a paramedic.[SEP]", "score":0.0261906236410141, "token":23386, "token_str":"â–paramedic" }, { "sequence":"[CLS] the woman worked as a chauffeur.[SEP]", "score":0.024797942489385605, "token":28744, "token_str":"â–chauffeur" }, { "sequence":"[CLS] the woman worked as a waitress.[SEP]", "score":0.024124596267938614, "token":13678, "token_str":"â–waitress" } ] ``` This bias will also affect all fine-tuned versions of this model. | 03478e39951373e6c775afd91287c521 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Japanese This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 ja dataset. It achieves the following results on the evaluation set: - Loss: 0.3617 - Wer: 68.9459 | 8d4eefdbb8d4497e79c5fc91d8e525f1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1938 | 1.09 | 1000 | 0.2841 | 74.6631 | | 0.0466 | 3.06 | 2000 | 0.2996 | 72.0953 | | 0.005 | 5.04 | 3000 | 0.3376 | 70.4355 | | 0.0021 | 7.01 | 4000 | 0.3617 | 68.9459 | | 0.002 | 8.1 | 5000 | 0.3735 | 71.4711 | | a94fd221f82efd8130330f72175f0e53 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_f_fastspeech2 ``` | 674cc804b609e8e3940646de9b1a6f61 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS config <details><summary>expand</summary> ``` config: conf/tuning/train_fastspeech2.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/f/tts_train_fastspeech2_raw_phn_none ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min - - train - loss - min keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 1.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 8 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: 800 batch_size: 20 valid_batch_size: null batch_bins: 2500000 valid_batch_bins: null train_shape_file: - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/text_shape.phn - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/speech_shape valid_shape_file: - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/text_shape.phn - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/valid/speech_shape batch_type: numel valid_batch_type: null fold_length: - 150 - 204800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_f_phn/text - text - text - - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/train_f_phn/durations - durations - text_int - - dump/raw/train_f_phn/wav.scp - speech - sound valid_data_path_and_name_and_type: - - dump/raw/dev_f_phn/text - text - text - - exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/dev_f_phn/durations - durations - text_int - - dump/raw/dev_f_phn/wav.scp - speech - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 1.0 scheduler: noamlr scheduler_conf: model_size: 384 warmup_steps: 4000 token_list: - <blank> - <unk> - ',' - . - r - t - n - a0 - s - I0 - D - l - m - Y0 - v - h - E1 - k - a:1 - E:1 - G - f - j - T - a1 - p - c - au:1 - i:1 - O:1 - I:1 - E0 - I1 - r_0 - t_h - k_h - Y1 - ei1 - i0 - ou:1 - ei:1 - u:1 - O1 - N - l_0 - '91' - ai0 - au1 - ou0 - n_0 - ei0 - ai:1 - O0 - ou1 - ai1 - i1 - '9:1' - '90' - au0 - x - c_h - 9i:1 - C - p_h - u0 - Y:1 - J - 9i1 - u1 - 9i0 - N_0 - m_0 - J_0 - Yi0 - Oi1 - Yi1 - Oi0 - au:0 - '9:0' - E:0 - <sos/eos> odim: null model_conf: {} use_preprocessor: true token_type: phn bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null feats_extract: fbank feats_extract_conf: n_fft: 1024 hop_length: 256 win_length: null fs: 22050 fmin: 80 fmax: 7600 n_mels: 80 normalize: global_mvn normalize_conf: stats_file: exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/feats_stats.npz tts: fastspeech2 tts_conf: adim: 384 aheads: 2 elayers: 4 eunits: 1536 dlayers: 4 dunits: 1536 positionwise_layer_type: conv1d positionwise_conv_kernel_size: 3 duration_predictor_layers: 2 duration_predictor_chans: 256 duration_predictor_kernel_size: 3 postnet_layers: 5 postnet_filts: 5 postnet_chans: 256 use_masking: true use_scaled_pos_enc: true encoder_normalize_before: true decoder_normalize_before: true reduction_factor: 1 init_type: xavier_uniform init_enc_alpha: 1.0 init_dec_alpha: 1.0 transformer_enc_dropout_rate: 0.2 transformer_enc_positional_dropout_rate: 0.2 transformer_enc_attn_dropout_rate: 0.2 transformer_dec_dropout_rate: 0.2 transformer_dec_positional_dropout_rate: 0.2 transformer_dec_attn_dropout_rate: 0.2 pitch_predictor_layers: 5 pitch_predictor_chans: 256 pitch_predictor_kernel_size: 5 pitch_predictor_dropout: 0.5 pitch_embed_kernel_size: 1 pitch_embed_dropout: 0.0 stop_gradient_from_pitch_predictor: true energy_predictor_layers: 2 energy_predictor_chans: 256 energy_predictor_kernel_size: 3 energy_predictor_dropout: 0.5 energy_embed_kernel_size: 1 energy_embed_dropout: 0.0 stop_gradient_from_energy_predictor: false pitch_extract: dio pitch_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 f0max: 400 f0min: 80 reduction_factor: 1 pitch_normalize: global_mvn pitch_normalize_conf: stats_file: exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/pitch_stats.npz energy_extract: energy energy_extract_conf: fs: 22050 n_fft: 1024 hop_length: 256 win_length: null reduction_factor: 1 energy_normalize: global_mvn energy_normalize_conf: stats_file: exp/f/tts_train_tacotron2_raw_phn_none/decode_use_teacher_forcingtrue_train.loss.ave/stats/train/energy_stats.npz required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> | 1498215c309123d9d76afc81301fc1d7 |
mit | [] | false | Overview **Model Description:** roberta-large-faithcritic is the [RoBERTa large model](https://huggingface.co/roberta-large) fine-tuned on FaithCritic, a derivative of the [FaithDial](https://huggingface.co/datasets/McGill-NLP/FaithDial) dataset. The objective is to predict whether an utterance is faithful or not, given the source knowledge. The hyperparameters are provided in [hparams.yml](https://huggingface.co/McGill-NLP/roberta-large-faithcritic/blob/main/hparams.yaml). To know more about how to train a critic model, visit [our repo](https://github.com/McGill-NLP/FaithDial). | a40bb0222000e91f349aeb24c951d770 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/roberta-large-faithcritic") model = AutoModel.from_pretrained("McGill-NLP/roberta-large-faithcritic") knowledge = "A cardigan is a type of knitted garment (sweater) that has an open front." response = "The old version is the regular one, knitted garment that has open front and buttons!" input = tokenizer(knowledge, response) output = model(**input) ``` | 5d05127fd02c4857ee339bda33573ab1 |
mit | [] | false | Citation Information ```bibtex @article{dziri2022faithdial, title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue}, author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva}, journal={arXiv preprint, arXiv:2204.10757}, year={2022}, url={https://arxiv.org/abs/2204.10757} } ``` | 2e0d0b3ff6ea92a6d59faf53fbf4e376 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Model description This is a ported version of [S3PRL's Hubert for the SUPERB Keyword Spotting task](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream/speech_commands). The base model is [hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k), which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to [SUPERB: Speech processing Universal PERformance Benchmark](https://arxiv.org/abs/2105.01051) | 08a820864f0d185595a234d29b70cd01 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Task and dataset description Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0](https://www.tensorflow.org/datasets/catalog/speech_commands) for the task. The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the false positive. For the original model's training and evaluation instructions refer to the [S3PRL downstream task README](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream | 0bd46adde3af247ac820ef64f4450f72 |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | Usage examples You can use the model via the Audio Classification pipeline: ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("anton-l/superb_demo", "ks", split="test") classifier = pipeline("audio-classification", model="superb/hubert-large-superb-ks") labels = classifier(dataset[0]["file"], top_k=5) ``` Or use the model directly: ```python import torch from datasets import load_dataset from transformers import HubertForSequenceClassification, Wav2Vec2FeatureExtractor from torchaudio.sox_effects import apply_effects_file effects = [["channels", "1"], ["rate", "16000"], ["gain", "-3.0"]] def map_to_array(example): speech, _ = apply_effects_file(example["file"], effects) example["speech"] = speech.squeeze(0).numpy() return example | 48540e10cede3202691aeaf9a68b233c |
apache-2.0 | ['speech', 'audio', 'hubert', 'audio-classification'] | false | load a demo dataset and read audio files dataset = load_dataset("anton-l/superb_demo", "ks", split="test") dataset = dataset.map(map_to_array) model = HubertForSequenceClassification.from_pretrained("superb/hubert-large-superb-ks") feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-large-superb-ks") | 63803ddd1251cb02db5fa213e7426b08 |
creativeml-openrail-m | ['text-to-image'] | false | Sample images:   Based on StableDiffusion 1.5 model | 79b7f9d15403b92e799478eab64676ea |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.food_service_positive.absa.5-class.seed_42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.7699 - Accuracy: 0.8050 - Macro-f1: 0.8026 - Weighted-macro-f1: 0.8053 | f75eb557ad2a7b6a9e79639450a6cbea |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Kazakh (kk) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-09-25 01:39:09.527 | f0beed34e5538e18f07da9779ea66443 |
mit | [] | false | Chillpill on Stable Diffusion This is the `<Chillpill>` 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 own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 9f515e6eb870732b06e3146bdabaca29 |
mit | ['generated_from_trainer'] | false | rte_roberta-base_125_v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7551 - Accuracy: 0.6715 | ecaa645ec7981a1659bb3e09b346b48e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.8004 - Wer: 0.7139 | 7ffbc0eb06b69bafa7212780c0bc0587 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 10.0 - mixed_precision_training: Native AMP | d415aaae0abe2e2936aef5ad4255dac1 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.6683 | 1.45 | 500 | 1.7698 | 1.0041 | | 1.9548 | 2.91 | 1000 | 1.0890 | 0.8602 | | 1.9568 | 4.36 | 1500 | 1.0878 | 0.8680 | | 1.9497 | 5.81 | 2000 | 1.1501 | 0.8838 | | 1.8453 | 7.27 | 2500 | 1.0452 | 0.8418 | | 1.6952 | 8.72 | 3000 | 0.9153 | 0.7823 | | e7469ad18d581741bd2a03775d4f5fae |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr2e05-wd0.005-bs32 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2862 - Rmse: 0.5350 - Mse: 0.2862 - Mae: 0.4436 | b209ef8dd931372ce6847bd51993c31b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2774 | 1.0 | 623 | 0.2746 | 0.5240 | 0.2746 | 0.4160 | | 0.274 | 2.0 | 1246 | 0.2738 | 0.5233 | 0.2738 | 0.4166 | | 0.2724 | 3.0 | 1869 | 0.2862 | 0.5350 | 0.2862 | 0.4436 | | bdce93891f83052d42ba50aee9489241 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 80k (uncased) Seed 3 intermediate checkpoint 80k 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/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-3](https://hf.co/multberts-seed-3). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | ea7c6ad649667c54c7b63f43d7b0554b |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | 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-3-80k') model = BertModel.from_pretrained("multiberts-seed-3-80k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 26234078712580d7bf1d5842cb3d10a6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-banking77-classification This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3034 - Accuracy: 0.9321 - F1 Score: 0.9321 | d5812a8aeb5aeac15d3cd655e8d8289f |
mit | ['generated_from_trainer'] | false | Training and evaluation data The dataset used is [banking77](https://huggingface.co/datasets/banking77) The 77 labels are: |label|intent| |:---:|:----:| |0|activate_my_card| |1|age_limit| |2|apple_pay_or_google_pay| |3|atm_support| |4|automatic_top_up| |5|balance_not_updated_after_bank_transfer| |6|balance_not_updated_after_cheque_or_cash_deposit| |7|beneficiary_not_allowed| |8|cancel_transfer| |9|card_about_to_expire| |10|card_acceptance| |11|card_arrival| |12|card_delivery_estimate| |13|card_linking| |14|card_not_working| |15|card_payment_fee_charged| |16|card_payment_not_recognised| |17|card_payment_wrong_exchange_rate| |18|card_swallowed| |19|cash_withdrawal_charge| |20|cash_withdrawal_not_recognised| |21|change_pin| |22|compromised_card| |23|contactless_not_working| |24|country_support| |25|declined_card_payment| |26|declined_cash_withdrawal| |27|declined_transfer| |28|direct_debit_payment_not_recognised| |29|disposable_card_limits| |30|edit_personal_details| |31|exchange_charge| |32|exchange_rate| |33|exchange_via_app| |34|extra_charge_on_statement| |35|failed_transfer| |36|fiat_currency_support| |37|get_disposable_virtual_card| |38|get_physical_card| |39|getting_spare_card| |40|getting_virtual_card| |41|lost_or_stolen_card| |42|lost_or_stolen_phone| |43|order_physical_card| |44|passcode_forgotten| |45|pending_card_payment| |46|pending_cash_withdrawal| |47|pending_top_up| |48|pending_transfer| |49|pin_blocked| |50|receiving_money| |51|Refund_not_showing_up| |52|request_refund| |53|reverted_card_payment?| |54|supported_cards_and_currencies| |55|terminate_account| |56|top_up_by_bank_transfer_charge| |57|top_up_by_card_charge| |58|top_up_by_cash_or_cheque| |59|top_up_failed| |60|top_up_limits| |61|top_up_reverted| |62|topping_up_by_card| |63|transaction_charged_twice| |64|transfer_fee_charged| |65|transfer_into_account| |66|transfer_not_received_by_recipient| |67|transfer_timing| |68|unable_to_verify_identity| |69|verify_my_identity| |70|verify_source_of_funds| |71|verify_top_up| |72|virtual_card_not_working| |73|visa_or_mastercard| |74|why_verify_identity| |75|wrong_amount_of_cash_received| |76|wrong_exchange_rate_for_cash_withdrawal| | 66349781857a20340f467e9d0f48a2a7 |
mit | ['generated_from_trainer'] | false | Training procedure ``` from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification") pipe("Non riesco a pagare con la carta di credito") ``` | 265cdf0ececa6c172ff52a2ba86e9f65 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 | | 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 | | 1.4633 | 3.0 | 471 | 1.0385 | 0.8308 | 0.8153 | | 0.9234 | 4.0 | 628 | 0.7008 | 0.8789 | 0.8761 | | 0.6163 | 5.0 | 785 | 0.5029 | 0.9068 | 0.9063 | | 0.4282 | 6.0 | 942 | 0.4084 | 0.9123 | 0.9125 | | 0.3203 | 7.0 | 1099 | 0.3515 | 0.9253 | 0.9253 | | 0.245 | 8.0 | 1256 | 0.3295 | 0.9227 | 0.9225 | | 0.1863 | 9.0 | 1413 | 0.3092 | 0.9269 | 0.9269 | | 0.1518 | 10.0 | 1570 | 0.2901 | 0.9338 | 0.9338 | | 0.1179 | 11.0 | 1727 | 0.2938 | 0.9318 | 0.9319 | | 0.0969 | 12.0 | 1884 | 0.2906 | 0.9328 | 0.9328 | | 0.0805 | 13.0 | 2041 | 0.2963 | 0.9295 | 0.9295 | | 0.063 | 14.0 | 2198 | 0.2998 | 0.9289 | 0.9288 | | 0.0554 | 15.0 | 2355 | 0.2933 | 0.9351 | 0.9349 | | 0.046 | 16.0 | 2512 | 0.2960 | 0.9328 | 0.9326 | | 0.04 | 17.0 | 2669 | 0.3032 | 0.9318 | 0.9318 | | 0.035 | 18.0 | 2826 | 0.3061 | 0.9312 | 0.9312 | | 0.0317 | 19.0 | 2983 | 0.3030 | 0.9331 | 0.9330 | | 0.0315 | 20.0 | 3140 | 0.3034 | 0.9321 | 0.9321 | | 97896799c78769e3444a29c913613ccc |
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