license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
bsd-3-clause | ['pytorch-lightning', 'audio-to-audio'] | false | BibTeX entry and citation info ```bibtex @inproceedings{lee21nuwave, author={Junhyeok Lee and Seungu Han}, title={{NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling}}, year=2021, booktitle={Proc. Interspeech 2021}, pages={1634--1638}, doi={10.21437/Interspeech.2021-36} } ``` | f7a2083f576477f77ba200541a7dcfef |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-kitti-finetuned-diode-221214-123047 This model is a fine-tuned version of [vinvino02/glpn-kitti](https://huggingface.co/vinvino02/glpn-kitti) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3497 - Mae: 0.2847 - Rmse: 0.3977 - Abs Rel: 0.3477 - Log Mae: 0.1203 - Log Rmse: 0.1726 - Delta1: 0.5217 - Delta2: 0.8246 - Delta3: 0.9436 | bb5026d01e1722bb09dfea743268f016 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.15 - num_epochs: 25 - mixed_precision_training: Native AMP | ae5d3fdbae4568b773a7dddf9df324f6 |
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 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 0.6103 | 1.0 | 72 | 0.4449 | 0.3914 | 0.5513 | 0.4625 | 0.1615 | 0.2186 | 0.3918 | 0.6910 | 0.8549 | | 0.3762 | 2.0 | 144 | 0.4095 | 0.3583 | 0.4876 | 0.4281 | 0.1505 | 0.2015 | 0.4065 | 0.7121 | 0.8901 | | 0.341 | 3.0 | 216 | 0.3768 | 0.3046 | 0.4061 | 0.4016 | 0.1313 | 0.1840 | 0.4757 | 0.7938 | 0.9309 | | 0.291 | 4.0 | 288 | 0.3853 | 0.3227 | 0.4495 | 0.3724 | 0.1360 | 0.1869 | 0.4646 | 0.7680 | 0.9127 | | 0.2861 | 5.0 | 360 | 0.3786 | 0.3151 | 0.4257 | 0.4065 | 0.1344 | 0.1876 | 0.4597 | 0.7785 | 0.9329 | | 0.2539 | 6.0 | 432 | 0.3687 | 0.3158 | 0.4546 | 0.3329 | 0.1316 | 0.1821 | 0.4732 | 0.7869 | 0.9138 | | 0.2199 | 7.0 | 504 | 0.3705 | 0.3122 | 0.4479 | 0.3378 | 0.1312 | 0.1820 | 0.4784 | 0.7888 | 0.9189 | | 0.1728 | 8.0 | 576 | 0.3578 | 0.2895 | 0.4008 | 0.3675 | 0.1235 | 0.1766 | 0.5101 | 0.8178 | 0.9420 | | 0.1877 | 9.0 | 648 | 0.3589 | 0.2846 | 0.3846 | 0.3721 | 0.1235 | 0.1764 | 0.5144 | 0.8170 | 0.9403 | | 0.1541 | 10.0 | 720 | 0.3521 | 0.2831 | 0.3997 | 0.3283 | 0.1201 | 0.1712 | 0.5241 | 0.8260 | 0.9422 | | 0.1414 | 11.0 | 792 | 0.3460 | 0.2735 | 0.3772 | 0.3419 | 0.1173 | 0.1691 | 0.5409 | 0.8360 | 0.9469 | | 0.1643 | 12.0 | 864 | 0.3530 | 0.2878 | 0.4100 | 0.3313 | 0.1214 | 0.1736 | 0.5249 | 0.8214 | 0.9344 | | 0.1724 | 13.0 | 936 | 0.3606 | 0.2995 | 0.4249 | 0.3459 | 0.1255 | 0.1775 | 0.5057 | 0.8069 | 0.9323 | | 0.1514 | 14.0 | 1008 | 0.3477 | 0.2832 | 0.3881 | 0.3596 | 0.1206 | 0.1726 | 0.5174 | 0.8253 | 0.9437 | | 0.1535 | 15.0 | 1080 | 0.3535 | 0.2961 | 0.4242 | 0.3412 | 0.1231 | 0.1753 | 0.5186 | 0.8080 | 0.9332 | | 0.1233 | 16.0 | 1152 | 0.3508 | 0.2896 | 0.4104 | 0.3391 | 0.1213 | 0.1727 | 0.5225 | 0.8165 | 0.9398 | | 0.116 | 17.0 | 1224 | 0.3519 | 0.2874 | 0.3989 | 0.3533 | 0.1215 | 0.1731 | 0.5200 | 0.8179 | 0.9407 | | 0.1532 | 18.0 | 1296 | 0.3532 | 0.2965 | 0.4200 | 0.3459 | 0.1236 | 0.1747 | 0.5147 | 0.8035 | 0.9353 | | 0.1179 | 19.0 | 1368 | 0.3497 | 0.2828 | 0.3896 | 0.3557 | 0.1204 | 0.1728 | 0.5200 | 0.8260 | 0.9457 | | 0.1326 | 20.0 | 1440 | 0.3467 | 0.2787 | 0.3848 | 0.3475 | 0.1185 | 0.1704 | 0.5257 | 0.8330 | 0.9479 | | 0.1069 | 21.0 | 1512 | 0.3471 | 0.2807 | 0.3922 | 0.3418 | 0.1187 | 0.1707 | 0.5288 | 0.8297 | 0.9452 | | 0.1049 | 22.0 | 1584 | 0.3474 | 0.2864 | 0.4048 | 0.3387 | 0.1199 | 0.1717 | 0.5227 | 0.8251 | 0.9428 | | 0.103 | 23.0 | 1656 | 0.3483 | 0.2840 | 0.3991 | 0.3416 | 0.1196 | 0.1717 | 0.5254 | 0.8269 | 0.9431 | | 0.1184 | 24.0 | 1728 | 0.3473 | 0.2839 | 0.3960 | 0.3450 | 0.1198 | 0.1717 | 0.5223 | 0.8251 | 0.9443 | | 0.1258 | 25.0 | 1800 | 0.3497 | 0.2847 | 0.3977 | 0.3477 | 0.1203 | 0.1726 | 0.5217 | 0.8246 | 0.9436 | | 88f40a2416d8d25c70901c6957df5715 |
mit | [] | false | flash base + globalpointer 04/08/2022 10:53:34 - INFO - __main__ - ADDRESS = Score(f1=0.607703, precision=0.64939, recall=0.571046, tp=213, pred=328, gold=373) 04/08/2022 10:53:34 - INFO - __main__ - BOOK = Score(f1=0.8125, precision=0.873134, recall=0.75974, tp=117, pred=134, gold=154) 04/08/2022 10:53:34 - INFO - __main__ - COMPANY = Score(f1=0.818304, precision=0.832877, recall=0.804233, tp=304, pred=365, gold=378) 04/08/2022 10:53:34 - INFO - __main__ - GAME = Score(f1=0.854305, precision=0.834951, recall=0.874576, tp=258, pred=309, gold=295) 04/08/2022 10:53:34 - INFO - __main__ - GOVERNMENT = Score(f1=0.823529, precision=0.775, recall=0.878543, tp=217, pred=280, gold=247) 04/08/2022 10:53:34 - INFO - __main__ - MOVIE = Score(f1=0.810997, precision=0.842857, recall=0.781457, tp=118, pred=140, gold=151) 04/08/2022 10:53:34 - INFO - __main__ - NAME = Score(f1=0.874042, precision=0.890625, recall=0.858065, tp=399, pred=448, gold=465) 04/08/2022 10:53:34 - INFO - __main__ - ORGANIZATION = Score(f1=0.813986, precision=0.836207, recall=0.792916, tp=291, pred=348, gold=367) 04/08/2022 10:53:34 - INFO - __main__ - POSITION = Score(f1=0.78478, precision=0.808824, recall=0.762125, tp=330, pred=408, gold=433) 04/08/2022 10:53:34 - INFO - __main__ - SCENE = Score(f1=0.683805, precision=0.738889, recall=0.636364, tp=133, pred=180, gold=209) 04/08/2022 10:53:34 - INFO - __main__ - micro_f1 = Score(f1=0.79175, precision=0.809524, recall=0.77474, tp=2380, pred=2940, gold=3072) 04/08/2022 10:53:34 - INFO - __main__ - macro_f1 = Score(f1=0.788395, precision=0.808275, recall=0.771906, tp=0, pred=0, gold=0) 04/08/2022 10:53:34 - INFO - __main__ - mean_f1 = 0.790072 | c2b6dabec2729789f8bd67b3c16529ff |
mit | [] | false | flash base + softmax 04/08/2022 11:10:44 - INFO - __main__ - ADDRESS = Score(f1=0.568987, precision=0.522422, recall=0.624665, tp=233, pred=446, gold=373) 04/08/2022 11:10:44 - INFO - __main__ - BOOK = Score(f1=0.750789, precision=0.730061, recall=0.772727, tp=119, pred=163, gold=154) 04/08/2022 11:10:44 - INFO - __main__ - COMPANY = Score(f1=0.75528, precision=0.711944, recall=0.804233, tp=304, pred=427, gold=378) 04/08/2022 11:10:44 - INFO - __main__ - GAME = Score(f1=0.811502, precision=0.767372, recall=0.861017, tp=254, pred=331, gold=295) 04/08/2022 11:10:44 - INFO - __main__ - GOVERNMENT = Score(f1=0.738636, precision=0.69395, recall=0.789474, tp=195, pred=281, gold=247) 04/08/2022 11:10:44 - INFO - __main__ - MOVIE = Score(f1=0.74359, precision=0.720497, recall=0.768212, tp=116, pred=161, gold=151) 04/08/2022 11:10:44 - INFO - __main__ - NAME = Score(f1=0.831967, precision=0.794521, recall=0.873118, tp=406, pred=511, gold=465) 04/08/2022 11:10:44 - INFO - __main__ - ORGANIZATION = Score(f1=0.754054, precision=0.747989, recall=0.760218, tp=279, pred=373, gold=367) 04/08/2022 11:10:44 - INFO - __main__ - POSITION = Score(f1=0.742729, precision=0.720174, recall=0.766744, tp=332, pred=461, gold=433) 04/08/2022 11:10:44 - INFO - __main__ - SCENE = Score(f1=0.628842, precision=0.621495, recall=0.636364, tp=133, pred=214, gold=209) 04/08/2022 11:10:44 - INFO - __main__ - micro_f1 = Score(f1=0.736335, precision=0.703979, recall=0.77181, tp=2371, pred=3368, gold=3072) 04/08/2022 11:10:44 - INFO - __main__ - macro_f1 = Score(f1=0.732638, precision=0.703043, recall=0.765677, tp=0, pred=0, gold=0) 04/08/2022 11:10:44 - INFO - __main__ - mean_f1 = 0.734486 | 0a4418a50db64cdf6192061e88906b8c |
mit | [] | false | bert base + globalpointer 04/08/2022 11:22:48 - INFO - __main__ - ADDRESS = Score(f1=0.641558, precision=0.622166, recall=0.662198, tp=247, pred=397, gold=373) 04/08/2022 11:22:48 - INFO - __main__ - BOOK = Score(f1=0.813115, precision=0.821192, recall=0.805195, tp=124, pred=151, gold=154) 04/08/2022 11:22:48 - INFO - __main__ - COMPANY = Score(f1=0.823684, precision=0.819372, recall=0.828042, tp=313, pred=382, gold=378) 04/08/2022 11:22:48 - INFO - __main__ - GAME = Score(f1=0.841762, precision=0.811321, recall=0.874576, tp=258, pred=318, gold=295) 04/08/2022 11:22:48 - INFO - __main__ - GOVERNMENT = Score(f1=0.827324, precision=0.778571, recall=0.882591, tp=218, pred=280, gold=247) 04/08/2022 11:22:48 - INFO - __main__ - MOVIE = Score(f1=0.82392, precision=0.826667, recall=0.821192, tp=124, pred=150, gold=151) 04/08/2022 11:22:48 - INFO - __main__ - NAME = Score(f1=0.861345, precision=0.840164, recall=0.883621, tp=410, pred=488, gold=464) 04/08/2022 11:22:48 - INFO - __main__ - ORGANIZATION = Score(f1=0.804911, precision=0.806011, recall=0.803815, tp=295, pred=366, gold=367) 04/08/2022 11:22:48 - INFO - __main__ - POSITION = Score(f1=0.805046, precision=0.799544, recall=0.810624, tp=351, pred=439, gold=433) 04/08/2022 11:22:48 - INFO - __main__ - SCENE = Score(f1=0.702703, precision=0.722222, recall=0.684211, tp=143, pred=198, gold=209) 04/08/2022 11:22:48 - INFO - __main__ - micro_f1 = Score(f1=0.795833, precision=0.783528, recall=0.808531, tp=2483, pred=3169, gold=3071) 04/08/2022 11:22:48 - INFO - __main__ - macro_f1 = Score(f1=0.794537, precision=0.784723, recall=0.805606, tp=0, pred=0, gold=0) 04/08/2022 11:22:48 - INFO - __main__ - mean_f1 = 0.795185 ``` | deac7ee4f7a1a33a3313b9c07dd66848 |
mit | [] | false | cmeee + globalpointer ```python 04/08/2022 11:50:41 - INFO - __main__ - bod = Score(f1=0.639522, precision=0.642318, recall=0.63675, tp=3746, pred=5832, gold=5883) 04/08/2022 11:50:41 - INFO - __main__ - dep = Score(f1=0.473988, precision=0.650794, recall=0.372727, tp=41, pred=63, gold=110) 04/08/2022 11:50:41 - INFO - __main__ - dis = Score(f1=0.716959, precision=0.704479, recall=0.729889, tp=3602, pred=5113, gold=4935) 04/08/2022 11:50:41 - INFO - __main__ - dru = Score(f1=0.756328, precision=0.829329, recall=0.695139, tp=1001, pred=1207, gold=1440) 04/08/2022 11:50:41 - INFO - __main__ - equ = Score(f1=0.518703, precision=0.638037, recall=0.436975, tp=104, pred=163, gold=238) 04/08/2022 11:50:41 - INFO - __main__ - ite = Score(f1=0.322533, precision=0.503448, recall=0.23727, tp=219, pred=435, gold=923) 04/08/2022 11:50:41 - INFO - __main__ - mic = Score(f1=0.746967, precision=0.75614, recall=0.738014, tp=431, pred=570, gold=584) 04/08/2022 11:50:41 - INFO - __main__ - pro = Score(f1=0.611138, precision=0.614138, recall=0.608167, tp=1251, pred=2037, gold=2057) 04/08/2022 11:50:41 - INFO - __main__ - sym = Score(f1=0.47969, precision=0.495738, recall=0.464649, tp=1919, pred=3871, gold=4130) 04/08/2022 11:50:41 - INFO - __main__ - micro_f1 = Score(f1=0.622061, precision=0.638329, recall=0.606601, tp=12314, pred=19291, gold=20300) 04/08/2022 11:50:41 - INFO - __main__ - macro_f1 = Score(f1=0.585092, precision=0.648269, recall=0.54662, tp=0, pred=0, gold=0) 04/08/2022 11:50:41 - INFO - __main__ - mean_f1 = 0.603576 ``` | f02414443fe4b672076e6c9389814bbc |
mit | [] | false | usage ```python import torch from flash import FLASHForMaskedLM from transformers import BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_base_wwm_cluecorpussmall") model = FLASHForMaskedLM.from_pretrained("junnyu/flash_base_wwm_cluecorpussmall") model.eval() text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！" inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512, return_token_type_ids=False) | c3d7b06dff21126b96b01f8af5b5eb2a |
mit | [] | false | 这里必须是512，不然结果可能不对。 with torch.no_grad(): pt_outputs = model(**inputs).logits[0] pt_outputs_sentence = "pytorch: " for i, id in enumerate(tokenizer.encode(text)): if id == tokenizer.mask_token_id: val,idx = pt_outputs[i].softmax(-1).topk(k=5) tokens = tokenizer.convert_ids_to_tokens(idx) new_tokens = [] for v,t in zip(val.cpu(),tokens): new_tokens.append(f"{t}+{round(v.item(),4)}") pt_outputs_sentence += "[" + "||".join(new_tokens) + "]" else: pt_outputs_sentence += "".join( tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)) print(pt_outputs_sentence) | 125119a78613980c3dc7d9de16c13ab4 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | waifu-diffusion v1.3 - Diffusion for Weebs waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning. <img src=https://i.imgur.com/Y5Tmw1S.png width=75% height=75%> [Original Weights](https://huggingface.co/hakurei/waifu-diffusion-v1-3) | 72e8211da2223a4b8ffa5ade9e4ed1e5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Gradio & Colab We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run Waifu Diffusion: [](https://huggingface.co/spaces/hakurei/waifu-diffusion-demo) [](https://colab.research.google.com/drive/1_8wPN7dJO746QXsFnB09Uq2VGgSRFuYE | e1c51894bf662a12607645cadd64e89a |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Example Code ```python import torch from torch import autocast from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained( 'waifu-diffusion', torch_dtype=torch.float32 ).to('cuda') prompt = "1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt" with autocast("cuda"): image = pipe(prompt, guidance_scale=6)["sample"][0] image.save("test.png") ``` | c9c3f62328e89fa32a6d8ad72df27edf |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Team Members and Acknowledgements This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/). - [Anthony Mercurio](https://github.com/harubaru) - [Salt](https://github.com/sALTaccount/) - [Sta @ Bit192](https://twitter.com/naclbbr) In order to reach us, you can join our [Discord server](https://discord.gg/touhouai). [](https://discord.gg/touhouai) | 5ac06f8643fb749a0df4c88893080add |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/cyberkongz). Dataset is available [here](https://huggingface.co/datasets/huggingnft/cyberkongz). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [](https://github.com/AlekseyKorshuk/huggingnft) | fa2f3ff25c0fa5082776299784c8f039 |
mit | [] | false | Model Description A series of CLIP [ConvNeXt-Large](https://arxiv.org/abs/2201.03545) (w/ extra text depth, vision MLP head) models trained on LAION-2B (english), a subset of [LAION-5B](https://arxiv.org/abs/2210.08402), using [OpenCLIP](https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution Firsts: * First known ConvNeXt CLIP models trained at scale in the range of CLIP ViT-L/16, ViT-L14, and RN50x16 * First released model weights exploring increase of augmentation + regularization for image tower via adding (greater scale range of RRC, random erasing, stochastic depth) The models utilize: * the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-Large model (`convnext_large`) as the image tower * a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models * a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768). The models are trained at 256x256 (working on 384 variants) image resolution. At 256x256, the ConvNext-Large-D used roughly 1/2 the training FLOPs to achieve accuracy greater than previous L/14 model trained on LAION-2B. L/14 model is ~1.65x more GMAC, 1.45x more activations, and 1.22x more parameters. The ConvNeXt was trained with 26B samples-seen and L/14 with 34B. | Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) | | ----- | ------- | ---------- | ------------ | --------- | | [convnext_large_d.laion2b_s26b_b102k-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1), D(0.1) | 75.9 | | [convnext_large_d_320.laion2b_s29b_b131k-ft](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.6 | | [convnext_large_d_320.laion2b_s29b_b131k-ft-soup](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.9 | RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only, D = Dropout (prob) -- image tower head only LAION-A = LAION Aesthetic, an ~900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering. Model training done by Ross Wightman on the [stability.ai](https://stability.ai/) cluster. | cea961655b5440dd79bf877ae6141b2b |
mit | [] | false | Training Procedure All models were trained with a global batch size of 102400 for 128 checkpoint intervals of 203.7M samples for a total of ~26B samples seen over training. For 256x256 models, a slurm script w/ srun below was used on 16 8-GPU (A100 80GB) nodes (Stability). ``` /opt/slurm/sbin/srun --cpu_bind=v --accel-bind=gn python -m training.main \ --save-frequency 1 \ --name "convnext_large_256" \ --resume 'latest' \ --train-data="pipe:aws s3 cp s3://mybucket/path/{laion{00000..xxxxx}.tar -" \ --train-num-samples 203666042 \ --dataset-type webdataset \ --precision amp_bfloat16 \ --beta2 0.98 \ --warmup 10000 \ --batch-size=800 \ --epochs=128 \ --dataset-resampled \ --aug-cfg use_timm=True scale='(0.33, 1.0)' re_prob=0.35 \ --clip-grad-norm 5.0 \ --lr 1.667e-3 \ --workers=6 \ --model "convnext_large_d" \ --seed 0 \ --ddp-static-graph \ --local-loss \ --gather-with-grad \ --grad-checkpointing ``` | ad56b253031722abebd256b53ecc1057 |
mit | [] | false | Results The models achieve between 75.9 top-1 zero-shot accuracy on ImageNet-1k.  An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb | d31106a6f3c4951e1660e3afe96e1ce7 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | About the model The model has been trained on [a dataset containing 138927 article titles](https://www.englishvoice.ai/p/keywords-and-titles/ "a dataset containing 138927 article titles") along with their keywords. The purpose of the model is to generate suggestions of article headlines, given a keyword or multiple keywords. | 00c6a357838c9e6513744e62fc926894 |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Generation examples | Input | Output | | :------------ | :------------ | | weight loss | The Last Weight Loss Plan: Lose Weight, Feel Great, and Get in Shape <br/>How to Lose Weight Without Giving Up Your Favorite Foods <br/> I Lost Weight and Finally Feel Good About My Body | | property rental, property management | Property rental: The new way to make money <br/> We take the hassle out of property rental <br/> Is property management your new best friend? | | diabetic diet plan | A diabetic diet plan that actually works! <br/> Lose weight, feel great, and live better with our diabetic diet plan! <br/> Diet has never been so tasty: Our diabetic diet plan puts you to the test! | You can supply multiple keywords by separating them with commas. Higher temperature settings result in more creative headlines; we recommend testing first with the temperature set to 1.5. | b5c9483ba566677306f84ed5d97cb97c |
apache-2.0 | ['text2text-generation', 'paraphrase-generation'] | false | Sample code Python code for generating headlines: ```python import torch from transformers import T5ForConditionalGeneration,T5Tokenizer device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-keywords-to-headline") tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline") model = model.to(device) keywords = "weight loss, weight pills" text = "headline: " + keywords encoding = tokenizer.encode_plus(text, return_tensors = "pt") input_ids = encoding["input_ids"].to(device) attention_masks = encoding["attention_mask"].to(device) beam_outputs = model.generate( input_ids = input_ids, attention_mask = attention_masks, do_sample = True, num_return_sequences = 5, temperature = 0.95, early_stopping = True, top_k = 50, top_p = 0.95, ) for i in range(len(beam_outputs)): result = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(result) ``` Sample result: I Am Losing Weight and I Love It! New Weight Loss Pill Helps You Get the Body You Want! I Lost Weight By Taking Pills! The Truth About Weight Loss Pills! The Best Weight Loss Pills Money Can Buy! | e7c493cdf9cabf60a3abfeece056e795 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ur', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [HarrisDePerceptron/xls-r-300m-ur](https://huggingface.co/HarrisDePerceptron/xls-r-300m-ur) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 1.0517 - WER: 0.5151291512915129 - CER: 0.23689640940982254 | 0c6961faa38906fc179da938ee4ec8a6 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ur', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 3a116c21a635ea080a6506b0ec6f01e6 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'ur', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2991 | 1.96 | 100 | 0.9769 | 0.6627 | | 1.3415 | 3.92 | 200 | 0.9701 | 0.6594 | | 1.2998 | 5.88 | 300 | 0.9678 | 0.6668 | | 1.2881 | 7.84 | 400 | 0.9650 | 0.6613 | | 1.2369 | 9.8 | 500 | 0.9392 | 0.6502 | | 1.2293 | 11.76 | 600 | 0.9536 | 0.6480 | | 1.1709 | 13.73 | 700 | 0.9265 | 0.6402 | | 1.1492 | 15.69 | 800 | 0.9636 | 0.6506 | | 1.1044 | 17.65 | 900 | 0.9305 | 0.6351 | | 1.0704 | 19.61 | 1000 | 0.9329 | 0.6280 | | 1.0039 | 21.57 | 1100 | 0.9413 | 0.6295 | | 0.9756 | 23.53 | 1200 | 0.9718 | 0.6185 | | 0.9633 | 25.49 | 1300 | 0.9731 | 0.6133 | | 0.932 | 27.45 | 1400 | 0.9659 | 0.6199 | | 0.9252 | 29.41 | 1500 | 0.9766 | 0.6196 | | 0.9172 | 31.37 | 1600 | 1.0052 | 0.6199 | | 0.8733 | 33.33 | 1700 | 0.9955 | 0.6203 | | 0.868 | 35.29 | 1800 | 1.0069 | 0.6240 | | 0.8547 | 37.25 | 1900 | 0.9783 | 0.6258 | | 0.8451 | 39.22 | 2000 | 0.9845 | 0.6052 | | 0.8374 | 41.18 | 2100 | 0.9496 | 0.6137 | | 0.8153 | 43.14 | 2200 | 0.9756 | 0.6122 | | 0.8134 | 45.1 | 2300 | 0.9712 | 0.6096 | | 0.8019 | 47.06 | 2400 | 0.9565 | 0.5970 | | 0.7746 | 49.02 | 2500 | 0.9864 | 0.6096 | | 0.7664 | 50.98 | 2600 | 0.9988 | 0.6092 | | 0.7708 | 52.94 | 2700 | 1.0181 | 0.6255 | | 0.7468 | 54.9 | 2800 | 0.9918 | 0.6148 | | 0.7241 | 56.86 | 2900 | 1.0150 | 0.6018 | | 0.7165 | 58.82 | 3000 | 1.0439 | 0.6063 | | 0.7104 | 60.78 | 3100 | 1.0016 | 0.6037 | | 0.6954 | 62.75 | 3200 | 1.0117 | 0.5970 | | 0.6753 | 64.71 | 3300 | 1.0191 | 0.6037 | | 0.6803 | 66.67 | 3400 | 1.0190 | 0.6033 | | 0.661 | 68.63 | 3500 | 1.0284 | 0.6007 | | 0.6597 | 70.59 | 3600 | 1.0060 | 0.5967 | | 0.6398 | 72.55 | 3700 | 1.0372 | 0.6048 | | 0.6105 | 74.51 | 3800 | 1.0048 | 0.6044 | | 0.6164 | 76.47 | 3900 | 1.0398 | 0.6148 | | 0.6354 | 78.43 | 4000 | 1.0272 | 0.6133 | | 0.5952 | 80.39 | 4100 | 1.0364 | 0.6081 | | 0.5814 | 82.35 | 4200 | 1.0418 | 0.6092 | | 0.6079 | 84.31 | 4300 | 1.0277 | 0.5967 | | 0.5748 | 86.27 | 4400 | 1.0362 | 0.6041 | | 0.5624 | 88.24 | 4500 | 1.0427 | 0.6007 | | 0.5767 | 90.2 | 4600 | 1.0370 | 0.5919 | | 0.5793 | 92.16 | 4700 | 1.0442 | 0.6011 | | 0.547 | 94.12 | 4800 | 1.0516 | 0.5982 | | 0.5513 | 96.08 | 4900 | 1.0461 | 0.5989 | | 0.5429 | 98.04 | 5000 | 1.0504 | 0.5996 | | 0.5404 | 100.0 | 5100 | 1.0517 | 0.5967 | | 62ae1ad4539416a8ad299d0d49b9b369 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8976 - Mae: 0.4268 | 0efbb980d70e8232ff4b4523e7207053 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.092 | 1.0 | 235 | 0.9514 | 0.5122 | | 0.9509 | 2.0 | 470 | 0.8976 | 0.4268 | | 173e7dcf571a5ca0cacb0b1a6a3cf6c6 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-finetuned-squad-finetuned-squad This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the squad dataset. | 41a04cefb9349e85e9dcfba0d9cf3801 |
apache-2.0 | ['translation'] | false | opus-mt-en-fi * source languages: en * target languages: fi * OPUS readme: [en-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-fi/README.md) * dataset: opus+bt-news * model: transformer * pre-processing: normalization + SentencePiece * download original weights: [opus+bt-news-2020-03-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-fi/opus+bt-news-2020-03-21.zip) * test set translations: [opus+bt-news-2020-03-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fi/opus+bt-news-2020-03-21.test.txt) * test set scores: [opus+bt-news-2020-03-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-fi/opus+bt-news-2020-03-21.eval.txt) | 2f09039aeb86e5a44d273e1dfacb82f0 |
mit | ['pytorch', 'causal-lm'] | false | Lit-6B - A Large Fine-tuned Model For Fictional Storytelling Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text. | 7b664038d81da3106c523285566cf907 |
mit | ['pytorch', 'causal-lm'] | false | Model Description The model used for fine-tuning is [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax), which is a 6 billion parameter auto-regressive language model trained on [The Pile](https://pile.eleuther.ai/). | b2fd602bee90f575b0975fce3774c81d |
mit | ['pytorch', 'causal-lm'] | false | Training Data & Annotative Prompting The data used in fine-tuning has been gathered from various sources such as the [Gutenberg Project](https://www.gutenberg.org/). The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations. ``` [ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ] *** When a traveler in north central Massachusetts takes the wrong fork... ``` The annotations can be mixed and matched to help generate towards a specific style. | 3efef0b3a5a9e8da11cb2e0a0ff5d6fd |
mit | ['pytorch', 'causal-lm'] | false | Example Code ``` from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained('hakurei/lit-6B') tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-6B') prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ] *** When a traveler''' input_ids = tokenizer.encode(prompt, return_tensors='pt') output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id) generated_text = tokenizer.decode(output[0]) print(generated_text) ``` An example output from this code produces a result that will look similar to: ``` [ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ] *** When a traveler comes to an unknown region, his thoughts turn inevitably towards the old gods and legends which cluster around its appearance. It is not that he believes in them or suspects their reality—but merely because they are present somewhere else in creation just as truly as himself, and so belong of necessity in any landscape whose features cannot be altogether strange to him. Moreover, man has been prone from ancient times to brood over those things most connected with the places where he dwells. Thus the Olympian deities who ruled Hyper ``` | 7d70cedb40739637cc9e5db3f5e59466 |
mit | ['pytorch', 'causal-lm'] | false | Team members and Acknowledgements This project would not have been possible without the computational resources graciously provided by the [TPU Research Cloud](https://sites.research.google/trc/) - [Anthony Mercurio](https://github.com/harubaru) - Imperishable_NEET | 08c8763ddc5b42f5fc1aa87c12cbc306 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53_full_train This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the Swissdial dataset. It achieves the following results on the evaluation set: - Loss: 0.2811 - Wer: 0.2909 | 60d502fe9534c91e3ebab096cad83f66 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP | b584f2537cff6bcd3b0bbab26a4f36a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.7666 | 2.69 | 1000 | 0.4356 | 0.4954 | | 0.7868 | 5.39 | 2000 | 0.2693 | 0.3180 | | 0.6948 | 8.09 | 3000 | 0.2811 | 0.2909 | | 67bf433f05da53fef25c65417f8aba49 |
apache-2.0 | ['generated_from_trainer'] | false | DistilBERT-POWO_MGH_Lifecycle_Finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0728 | 25e4dede5617ba97f8480230e1e0e4e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0716 | 1.0 | 1625 | 0.0843 | | 0.0695 | 2.0 | 3250 | 0.0701 | | 0.0603 | 3.0 | 4875 | 0.0728 | | 99d39739b5cf76d8f3a959895db263e9 |
mit | ['generated_from_trainer'] | false | adr-ner This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0434 - Precision: 0.7305 - Recall: 0.6934 - F1: 0.7115 - Accuracy: 0.9941 | 956ff4330127ee3f7b20c9f7eeacc1c8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | a7654e198d1937557b3929fdac18c68c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 107 | 0.0630 | 0.0 | 0.0 | 0.0 | 0.9876 | | No log | 2.0 | 214 | 0.0308 | 0.4282 | 0.3467 | 0.3832 | 0.9900 | | No log | 3.0 | 321 | 0.0254 | 0.5544 | 0.5603 | 0.5573 | 0.9920 | | No log | 4.0 | 428 | 0.0280 | 0.6430 | 0.5751 | 0.6071 | 0.9929 | | 0.0465 | 5.0 | 535 | 0.0266 | 0.5348 | 0.7146 | 0.6118 | 0.9915 | | 0.0465 | 6.0 | 642 | 0.0423 | 0.7632 | 0.5793 | 0.6587 | 0.9939 | | 0.0465 | 7.0 | 749 | 0.0336 | 0.6957 | 0.6765 | 0.6860 | 0.9939 | | 0.0465 | 8.0 | 856 | 0.0370 | 0.6876 | 0.6702 | 0.6788 | 0.9936 | | 0.0465 | 9.0 | 963 | 0.0349 | 0.6555 | 0.7040 | 0.6789 | 0.9932 | | 0.0044 | 10.0 | 1070 | 0.0403 | 0.6910 | 0.6808 | 0.6858 | 0.9938 | | 0.0044 | 11.0 | 1177 | 0.0415 | 0.7140 | 0.6808 | 0.6970 | 0.9939 | | 0.0044 | 12.0 | 1284 | 0.0440 | 0.7349 | 0.6681 | 0.6999 | 0.9941 | | 0.0044 | 13.0 | 1391 | 0.0423 | 0.7097 | 0.6977 | 0.7036 | 0.9941 | | 0.0044 | 14.0 | 1498 | 0.0435 | 0.7174 | 0.6977 | 0.7074 | 0.9941 | | 0.0006 | 15.0 | 1605 | 0.0434 | 0.7305 | 0.6934 | 0.7115 | 0.9941 | | 638794870e3526c1633ba8930d234626 |
mit | [] | false | These are the midjourney styles that are pre-loaded in [Whatchamacallit](https://colab.research.google.com/github/aicrumb/whatchamacallit/blob/main/Whatchamacallit.ipynb) Using original textual inversion bins that are compatible with most webuis/notebooks that support text inversion loading. They can be easily converted to diffusers-style and in Whatchamacallit there is code to do that already if you need reference. \- midj-strong: <br> good at that weird surreal melty almost golden sort of style, looks like clip guided diffusion in my opinion \- midj-portrait: <br> a bit more subtle but still very cinematic and changes the image significantly but less so than midj-strong \- midj-anthro: <br> was finetuned on some anthropomorphic animals (not traditional furry style, but just animals standing like humans). good on other subjects though.  | 036bdb1d924257e63b7f3c24a1f7a251 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 25177 - mixed_precision_training: Native AMP | ea2ce2fac1393d7769c8c3b29a1428f5 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 1649999872}, 'generation': {'batch_size': 128, 'every_n_steps': 512, 'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 2048}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_hits_threshold': 0, 'num_samples': 2048, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'every_n_steps': 512, 'force_call_on': [25177], 'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': 'cf05a2b0558c03b08c78f07662c22989785b9520'}, 'path_or_name': 'kejian/mighty-mle'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'curious-mle', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1649999872, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | a54b9a0af99c3f4c7fab681701f3f127 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-sst2-target-glue-wnli This model is a fine-tuned version of [muhtasham/tiny-mlm-glue-sst2](https://huggingface.co/muhtasham/tiny-mlm-glue-sst2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2687 - Accuracy: 0.1127 | 609c41b1f303c97f188ae856be26e788 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6895 | 25.0 | 500 | 0.7649 | 0.2535 | | 0.6628 | 50.0 | 1000 | 1.1357 | 0.1268 | | 0.6042 | 75.0 | 1500 | 1.7250 | 0.0986 | | 0.5319 | 100.0 | 2000 | 2.2687 | 0.1127 | | 8e7cb0f981fad0e8ad4ded7d32f9e89b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/sentence-t5-large
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from the Tensorflow model [st5-large-1](https://tfhub.dev/google/sentence-t5/st5-large/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results.
The model uses only the encoder from a T5-large model. The weights are stored in FP16.
| d5f3aab4bd89afc13c90df6bff126017 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/sentence-t5-large')
embeddings = model.encode(sentences)
print(embeddings)
```
The model requires sentence-transformers version 2.2.0 or newer.
| 3bb12ad0ba7fd97828aea3efad50e2f9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-large)
| 61da753dd8d2a44f360c46fdbd66337d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Citing & Authors
If you find this model helpful, please cite the respective publication:
[Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)
| 626a8009e0463594aede13ed103cf569 |
mit | ['spacy', 'token-classification'] | false | English pipeline for part-of-speech and rhetorical tagging. | Feature | Description | | --- | --- | | **Name** | `en_docusco_spacy_fc_trf` | | **Version** | `1.1` | | **spaCy** | `>=3.4.3,<3.5.0` | | **Default Pipeline** | `transformer`, `tagger`, `ner` | | **Components** | `transformer`, `tagger`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | `MIT` | | **Author** | [David Brown](https://browndw.github.io/docuscope-docs/) | | 26442e3f3fdd323fe2f197a31514e5da |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (269 labels for 2 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `APPGE`, `AT`, `AT1`, `BCL21`, `BCL22`, `CC`, `CCB`, `CS`, `CS21`, `CS22`, `CS31`, `CS32`, `CS33`, `CS41`, `CS42`, `CS43`, `CS44`, `CSA`, `CSN`, `CST`, `CSW`, `CSW31`, `CSW32`, `CSW33`, `DA`, `DA1`, `DA2`, `DAR`, `DAT`, `DB`, `DB2`, `DD`, `DD1`, `DD2`, `DDQ`, `DDQGE`, `DDQV`, `DDQV31`, `DDQV32`, `DDQV33`, `EX`, `FO`, `FU`, `FW`, `GE`, `IF`, `II`, `II21`, `II22`, `II31`, `II32`, `II33`, `II41`, `II42`, `II43`, `II44`, `IO`, `IW`, `JJ`, `JJ21`, `JJ22`, `JJ31`, `JJ32`, `JJ33`, `JJR`, `JJT`, `JK`, `MC`, `MC1`, `MC2`, `MC221`, `MC222`, `MCMC`, `MD`, `MF`, `ND1`, `NN`, `NN1`, `NN121`, `NN122`, `NN131`, `NN132`, `NN133`, `NN141`, `NN142`, `NN143`, `NN144`, `NN2`, `NN21`, `NN22`, `NN221`, `NN222`, `NN231`, `NN232`, `NN233`, `NN31`, `NN33`, `NNA`, `NNB`, `NNL1`, `NNL2`, `NNO`, `NNO2`, `NNT1`, `NNT2`, `NNU`, `NNU1`, `NNU2`, `NNU21`, `NNU22`, `NP`, `NP1`, `NP2`, `NPD1`, `NPD2`, `NPM1`, `NPM2`, `PN`, `PN1`, `PN121`, `PN122`, `PN21`, `PN22`, `PNQO`, `PNQS`, `PNQS31`, `PNQS32`, `PNQS33`, `PNQV`, `PNX1`, `PPGE`, `PPH1`, `PPHO1`, `PPHO2`, `PPHS1`, `PPHS2`, `PPIO1`, `PPIO2`, `PPIS1`, `PPIS2`, `PPX1`, `PPX121`, `PPX122`, `PPX2`, `PPX221`, `PPX222`, `PPY`, `RA`, `RA21`, `RA22`, `REX`, `REX21`, `REX22`, `REX41`, `REX42`, `REX43`, `REX44`, `RG`, `RG21`, `RG22`, `RGQ`, `RGQV`, `RGQV31`, `RGQV32`, `RGQV33`, `RGR`, `RGT`, `RL`, `RL21`, `RL22`, `RP`, `RPK`, `RR`, `RR21`, `RR22`, `RR31`, `RR32`, `RR33`, `RR41`, `RR42`, `RR43`, `RR44`, `RR51`, `RR52`, `RR53`, `RR54`, `RR55`, `RRQ`, `RRQV`, `RRQV31`, `RRQV32`, `RRQV33`, `RRR`, `RRT`, `RT`, `RT21`, `RT22`, `RT31`, `RT32`, `RT33`, `RT41`, `RT42`, `RT43`, `RT44`, `TO`, `UH`, `UH21`, `UH22`, `UH31`, `UH32`, `UH33`, `VB0`, `VBDR`, `VBDZ`, `VBG`, `VBI`, `VBM`, `VBN`, `VBR`, `VBZ`, `VD0`, `VDD`, `VDG`, `VDI`, `VDN`, `VDZ`, `VH0`, `VHD`, `VHG`, `VHI`, `VHN`, `VHZ`, `VM`, `VM21`, `VM22`, `VMK`, `VV0`, `VVD`, `VVG`, `VVGK`, `VVI`, `VVN`, `VVNK`, `VVZ`, `XX`, `Y`, `ZZ1`, `ZZ2`, `ZZ221`, `ZZ222` | | **`ner`** | `ActorsAbstractions`, `ActorsFirstPerson`, `ActorsPeople`, `ActorsPublicEntities`, `CitationAuthority`, `CitationControversy`, `CitationNeutral`, `ConfidenceHedged`, `ConfidenceHigh`, `OrganizationNarrative`, `OrganizationReasoning`, `PlanningFuture`, `PlanningStrategy`, `SentimentNegative`, `SentimentPositive`, `SignpostingAcademicWritingMoves`, `SignpostingMetadiscourse`, `StanceEmphatic`, `StanceModerated` | </details> | 794a6d1c19d221f9ffb6303da6a546e5 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 98.39 | | `ENTS_F` | 88.62 | | `ENTS_P` | 88.90 | | `ENTS_R` | 88.34 | | `TRANSFORMER_LOSS` | 2319800.36 | | `TAGGER_LOSS` | 669777.78 | | `NER_LOSS` | 2048423.35 | | f911d1a2bbfc6ca6915d71c55f1f711f |
mit | ['generated_from_trainer'] | false | camembert-base-cae-fait-ext This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3098 - Precision: 0.7339 - Recall: 0.7107 - F1: 0.7161 | 1998ae443b959d1474548d9687d4f79b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.2626 | 1.0 | 61 | 1.1255 | 0.2541 | 0.5041 | 0.3379 | | 1.0858 | 2.0 | 122 | 0.9264 | 0.6300 | 0.6198 | 0.5705 | | 0.8364 | 3.0 | 183 | 0.8741 | 0.6460 | 0.6446 | 0.6391 | | 0.5045 | 4.0 | 244 | 0.7836 | 0.7252 | 0.7273 | 0.7171 | | 0.2866 | 5.0 | 305 | 0.9903 | 0.7352 | 0.6860 | 0.6918 | | 0.1896 | 6.0 | 366 | 1.0289 | 0.7422 | 0.7190 | 0.7257 | | 0.0975 | 7.0 | 427 | 1.1272 | 0.7565 | 0.7355 | 0.7396 | | 0.0679 | 8.0 | 488 | 1.2209 | 0.7389 | 0.7190 | 0.7237 | | 0.058 | 9.0 | 549 | 1.2647 | 0.7318 | 0.7025 | 0.7079 | | 0.0431 | 10.0 | 610 | 1.3098 | 0.7339 | 0.7107 | 0.7161 | | b412fdcef87917981751849b71a83c1d |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'speech', 'openslr', 'nepali'] | false | wav2vec2-large-xls-r-300m-nepali-openslr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an [OpenSLR Nepali ASR](https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned) dataset. It achieves the following results on the evaluation set: - eval_loss: 0.1767 - eval_wer: 0.2127 - eval_runtime: 595.3962 - eval_samples_per_second: 36.273 - eval_steps_per_second: 4.535 - epoch: 6.07 - step: 23200 | efbbd97cbfe0fb192c4ed02b0926cc69 |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'speech', 'openslr', 'nepali'] | false | Model description Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on one of the most popular English datasets for ASR, called LibriSpeech, Facebook AI presented a multi-lingual version of Wav2Vec2, called XLSR. XLSR stands for cross-lingual speech representations and refers to model's ability to learn speech representations that are useful across multiple languages. | 206cf90b96541690aa9db3637f066aa0 |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'speech', 'openslr', 'nepali'] | false | How to use? 1. Install transformers and librosa ``` pip install librosa, transformers ``` 2. Run the following code which loads your audio file, preprocessor, models, and returns your prediction ```python import librosa from transformers import pipeline audio, sample_rate = librosa.load("<path to your audio file>", sr=16000) recognizer = pipeline("automatic-speech-recognition", model="spktsagar/wav2vec2-large-xls-r-300m-nepali-openslr") prediction = recognizer(audio) ``` | 91146bac358d66433e60abbc8c7a498e |
apache-2.0 | ['generated_from_trainer', 'automatic-speech-recognition', 'speech', 'openslr', 'nepali'] | false | Intended uses & limitations The model is trained on the OpenSLR Nepali ASR dataset, which in itself has some incorrect transcriptions, so it is obvious that the model will not have perfect predictions for your transcript. Similarly, due to colab's resource limit utterances longer than 5 sec are filtered out from the dataset during training and evaluation. Hence, the model might not perform as expected when given audio input longer than 5 sec. | 2e253816c6e187be24123451e2b9801c |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/mini-mutants). Dataset is available [here](https://huggingface.co/datasets/huggingnft/mini-mutants). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [](https://github.com/AlekseyKorshuk/huggingnft) | 4e556d6dbf5642d6ca773e662458a421 |
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 dataset. It achieves the following results on the evaluation set: - Loss: 1.1586 | 227520f2e1d8ff9d4d593604da8269a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2203 | 1.0 | 5533 | 1.1569 | | 0.9452 | 2.0 | 11066 | 1.1234 | | 0.7656 | 3.0 | 16599 | 1.1586 | | ebbcdcabe856b6aa909cfea2e4b92340 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.01', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 61c11cc93ae214444d82d30a1ef6789d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-burak-new-300-v2-4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3402 - Wer: 0.2237 | 3f606f428e5714708113aecae7d32646 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 131 | 50ca1bc8bdf1437e4a58e0a83b12632a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 7.7711 | 2.45 | 500 | 3.1768 | 1.0 | | 3.1194 | 4.9 | 1000 | 2.6401 | 1.0 | | 1.4593 | 7.35 | 1500 | 0.5243 | 0.5960 | | 0.7581 | 9.8 | 2000 | 0.3534 | 0.4432 | | 0.5843 | 12.25 | 2500 | 0.3159 | 0.4157 | | 0.4703 | 14.71 | 3000 | 0.3003 | 0.3668 | | 0.4045 | 17.16 | 3500 | 0.2891 | 0.3414 | | 0.3581 | 19.61 | 4000 | 0.2609 | 0.3207 | | 0.3268 | 22.06 | 4500 | 0.2622 | 0.3207 | | 0.3063 | 24.51 | 5000 | 0.2805 | 0.3193 | | 0.2729 | 26.96 | 5500 | 0.2674 | 0.2884 | | 0.249 | 29.41 | 6000 | 0.2740 | 0.2953 | | 0.2275 | 31.86 | 6500 | 0.2729 | 0.2753 | | 0.2295 | 34.31 | 7000 | 0.2801 | 0.2691 | | 0.2105 | 36.76 | 7500 | 0.2992 | 0.2801 | | 0.1905 | 39.22 | 8000 | 0.2967 | 0.2663 | | 0.1884 | 41.67 | 8500 | 0.2911 | 0.2691 | | 0.1773 | 44.12 | 9000 | 0.2966 | 0.2753 | | 0.1672 | 46.57 | 9500 | 0.3051 | 0.2505 | | 0.1632 | 49.02 | 10000 | 0.2872 | 0.2491 | | 0.1553 | 51.47 | 10500 | 0.3121 | 0.2629 | | 0.1619 | 53.92 | 11000 | 0.3044 | 0.2581 | | 0.1444 | 56.37 | 11500 | 0.3135 | 0.2567 | | 0.1451 | 58.82 | 12000 | 0.3033 | 0.2519 | | 0.1386 | 61.27 | 12500 | 0.3079 | 0.2622 | | 0.1261 | 63.73 | 13000 | 0.3037 | 0.2395 | | 0.1287 | 66.18 | 13500 | 0.3221 | 0.2409 | | 0.1236 | 68.63 | 14000 | 0.3179 | 0.2464 | | 0.1215 | 71.08 | 14500 | 0.3521 | 0.2429 | | 0.1208 | 73.53 | 15000 | 0.3481 | 0.2540 | | 0.1128 | 75.98 | 15500 | 0.3288 | 0.2402 | | 0.1108 | 78.43 | 16000 | 0.3238 | 0.2450 | | 0.1074 | 80.88 | 16500 | 0.3178 | 0.2416 | | 0.1086 | 83.33 | 17000 | 0.3461 | 0.2361 | | 0.1059 | 85.78 | 17500 | 0.3342 | 0.2457 | | 0.0981 | 88.24 | 18000 | 0.3382 | 0.2354 | | 0.0995 | 90.69 | 18500 | 0.3466 | 0.2416 | | 0.0995 | 93.14 | 19000 | 0.3326 | 0.2271 | | 0.0929 | 95.59 | 19500 | 0.3526 | 0.2237 | | 0.0944 | 98.04 | 20000 | 0.3516 | 0.2347 | | 0.089 | 100.49 | 20500 | 0.3504 | 0.2271 | | 0.0915 | 102.94 | 21000 | 0.3425 | 0.2285 | | 0.0845 | 105.39 | 21500 | 0.3309 | 0.2306 | | 0.0887 | 107.84 | 22000 | 0.3196 | 0.2264 | | 0.0812 | 110.29 | 22500 | 0.3285 | 0.2264 | | 0.0856 | 112.75 | 23000 | 0.3347 | 0.2251 | | 0.0778 | 115.2 | 23500 | 0.3403 | 0.2271 | | 0.0748 | 117.65 | 24000 | 0.3427 | 0.2278 | | 0.0803 | 120.1 | 24500 | 0.3380 | 0.2223 | | 0.0768 | 122.55 | 25000 | 0.3392 | 0.2189 | | 0.0764 | 125.0 | 25500 | 0.3423 | 0.2278 | | 0.0786 | 127.45 | 26000 | 0.3423 | 0.2230 | | 0.0766 | 129.9 | 26500 | 0.3402 | 0.2237 | | 416530b3810a03577855aabc31fc6a45 |
apache-2.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Model Description GPT-Neo 125M is a transformer model based on EleutherAI's replication of the GPT-3 architecture https://huggingface.co/EleutherAI/gpt-neo-125M. It generates recipes for brewing beer in a YAML-like format which can be easily used for different purposes. | 99a7b5953e956c0d9de2cc4b07bc8b91 |
apache-2.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Training data This model was trained on a custom dataset of ~ 76,800 beer recipes from the internet. It includes recipes for the following styles of beer: * Strong American Ale * Pale American Ale * India Pale Ale (IPA) * Standard American Beer * Stout * English Pale Ale * IPA * American Porter and Stout * Sour Ale * Irish Beer * Strong British Ale * Belgian and French Ale * German Wheat and Rye Beer * Czech Lager * Spice/Herb/Vegetable Beer * Specialty Beer * American Ale * Pilsner * Belgian Ale * Strong Belgian Ale * Bock * Brown British Beer * German Wheat Beer * Fruit Beer * Amber Malty European Lager * Pale Malty European Lager * British Bitter * Amber and Brown American Beer * Light Hybrid Beer * Pale Commonwealth Beer * American Wild Ale * European Amber Lager * Belgian Strong Ale * International Lager * Amber Bitter European Lager * Light Lager * Scottish and Irish Ale * European Sour Ale * Trappist Ale * Strong European Beer * Porter * Historical Beer * Pale Bitter European Beer * Amber Hybrid Beer * Smoke Flavored/Wood-Aged Beer * Spiced Beer * Dark European Lager * Alternative Fermentables Beer * Mead * Strong Ale * Dark British Beer * Scottish Ale * Smoked Beer * English Brown Ale * Dark Lager * Cider or Perry * Wood Beer | f5f19421a76f1c6ba7ddf8081591fd2a |
apache-2.0 | ['text generation', 'pytorch', 'causal-lm'] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different recipe each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='b3ck1/gpt-neo-125M-finetuned-beer-recipes') >>> generator("style: Pilsner\nbatch_size: 20\nefficiency: 75\nboil_size:", do_sample=True, min_length=50, max_length=500) >>> print(output[0]['generated_text']) style: Pilsner batch_size: 20 efficiency: 70 boil_size: 24 boil_time: 60 fermentables: - name: Pale Ale type: Grain amount: 6.5 hops: - name: Saaz alpha: 3.5 use: Boil time: 60 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 30 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 10 amount: 0.06 - name: Saaz alpha: 3.5 use: Boil time: 0 amount: 0.06 yeasts: - name: Safale - American Ale Yeast US-05 amount: 0.11 min_temperature: 12 max_temperature: 25 primary_temp: null mash_steps: - step_temp: 65 step_time: 60 miscs: [] ``` | d55de1d8d5d3c9997affe1d8d08d6def |
apache-2.0 | ['translation'] | false | cel-eng * source group: Celtic languages * target group: English * OPUS readme: [cel-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cel-eng/README.md) * model: transformer * source language(s): bre cor cym gla gle glv * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-07-31.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opus2m-2020-07-31.zip) * test set translations: [opus2m-2020-07-31.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opus2m-2020-07-31.test.txt) * test set scores: [opus2m-2020-07-31.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opus2m-2020-07-31.eval.txt) | 5d4d2c1a7c291dd5aaa45a9852a41fad |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.bre-eng.bre.eng | 17.2 | 0.385 | | Tatoeba-test.cor-eng.cor.eng | 3.0 | 0.172 | | Tatoeba-test.cym-eng.cym.eng | 41.5 | 0.582 | | Tatoeba-test.gla-eng.gla.eng | 15.4 | 0.330 | | Tatoeba-test.gle-eng.gle.eng | 50.8 | 0.668 | | Tatoeba-test.glv-eng.glv.eng | 11.0 | 0.297 | | Tatoeba-test.multi.eng | 22.8 | 0.398 | | 9992e040828a73ab74517e3fca1c4bdf |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cel-eng - source_languages: cel - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cel-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['gd', 'ga', 'br', 'kw', 'gv', 'cy', 'cel', 'en'] - src_constituents: {'gla', 'gle', 'bre', 'cor', 'glv', 'cym'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opus2m-2020-07-31.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/cel-eng/opus2m-2020-07-31.test.txt - src_alpha3: cel - tgt_alpha3: eng - short_pair: cel-en - chrF2_score: 0.39799999999999996 - bleu: 22.8 - brevity_penalty: 1.0 - ref_len: 42097.0 - src_name: Celtic languages - tgt_name: English - train_date: 2020-07-31 - src_alpha2: cel - tgt_alpha2: en - prefer_old: False - long_pair: cel-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 963d148917fe5f7434b967ffb8d3b4ab |
apache-2.0 | ['CTC', 'Attention', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | wav2vec 2.0 with CTC/Attention trained on CommonVoice Kinyarwanda (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on CommonVoice (Kinyarwanda Language) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: | Release | Test WER | GPUs | |:--------------:|:--------------:| :--------:| | 03-06-21 | 18.91 | 2xV100 32GB | | c6a3922e6f686252748ec0f6c0586a9a |
apache-2.0 | ['CTC', 'Attention', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | Pipeline description This ASR system is composed of 2 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions (train.tsv) of CommonVoice (RW). - Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on CommonVoice En. The obtained final acoustic representation is given to the CTC and attention decoders. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. | 61ea96d085d9ffdb0e2a500b13659785 |
apache-2.0 | ['CTC', 'Attention', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | Install SpeechBrain First of all, please install tranformers and SpeechBrain with the following command: ``` pip install speechbrain transformers ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | 9cbc817a15b72655a972269e46540a68 |
apache-2.0 | ['CTC', 'Attention', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | Transcribing your own audio files (in Kinyarwanda) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-rw", savedir="pretrained_models/asr-wav2vec2-commonvoice-rw") asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-rw/example.mp3") ``` | 1d4a9653389bb2aad7e0fb65527f1966 |
apache-2.0 | ['CTC', 'Attention', 'pytorch', 'speechbrain', 'Transformer', 'hf-asr-leaderboard'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/CommonVoice/ASR/seq2seq python train_with_wav2vec.py hparams/train_rw_with_wav2vec.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing). | 9a27bd4b5ebca13eba4c4ac81a5227eb |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_3e-05_all_16_02_2022-16_16_08 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: 0.1630 - Precision: 0.3684 - Recall: 0.3714 - F1: 0.3699 - Accuracy: 0.9482 | 0fe8ca44b7b3bbfce87d27523942dddc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3339 | 0.1075 | 0.2324 | 0.1470 | 0.8379 | | No log | 2.0 | 76 | 0.3074 | 0.1589 | 0.2926 | 0.2060 | 0.8489 | | No log | 3.0 | 114 | 0.2914 | 0.2142 | 0.3278 | 0.2591 | 0.8591 | | No log | 4.0 | 152 | 0.2983 | 0.1951 | 0.3595 | 0.2529 | 0.8454 | | No log | 5.0 | 190 | 0.2997 | 0.1851 | 0.3528 | 0.2428 | 0.8487 | | 835c6960344b0f4fb942a6d58c8d56d4 |
apache-2.0 | ['automatic-speech-recognition', 'ja'] | false | exp_w2v2t_ja_unispeech_s569 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | da1feffd84cf73cb2a000ab9d367e9ad |
mit | ['spacy', 'token-classification', 'text-classification'] | false | To install this model: pip install https://huggingface.co/PlanTL-GOB-ES/es_bsc_demo_md/resolve/main/es_bsc_demo_md-any-py3-none-any.whl Spanish light weight pipeline by BSC. Components: floret static vectors, morphologizer, parser, attribute_ruler, lemmatizer, text classification. | Feature | Description | | --- | --- | | **Name** | `es_bsc_demo_md` | | **Version** | `3.4.1` | | **spaCy** | `>=3.4.1,<3.5.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `textcat` | | **Components** | `tok2vec`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `textcat` | | **Vectors** | -1 keys, 50000 unique vectors (300 dimensions) | | **Sources** | [UD Spanish AnCora v2.10](https://github.com/UniversalDependencies/UD_Spanish-AnCora) (Martínez Alonso, Héctor; Zeman, Daniel)<br /> [Spanish floret embeddings from BNE corpus] (https://zenodo.org/record/7314098) <br /> | | **License** | `mit` | | **Author** | [Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)](https://huggingface.co/PlanTL-GOB-ES/es_bsc_demo_md) | | **Copyright** | Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) | | **Funding** | This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL | | 94ddd4e086ec43bc7c05a8c1bc62e799 |
mit | ['spacy', 'token-classification', 'text-classification'] | false | Label Scheme <details> <summary>View label scheme (734 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X`, `ao0fp0`, `ao0fs0`, `ao0mp0`, `ao0ms0`, `aq0000`, `aq00p0`, `aq00s0`, `aq0cc0`, `aq0cn0`, `aq0cp0`, `aq0cs0`, `aq0fp0`, `aq0fpp`, `aq0fs0`, `aq0fsp`, `aq0fsp-B2`, `aq0mn0`, `aq0mp0`, `aq0mpp`, `aq0ms0`, `aq0msp`, `cc`, `cs`, `da0fp0`, `da0fs0`, `da0m00`, `da0mp0`, `da0ms0`, `da0ns0`, `dd0cp0`, `dd0cs0`, `dd0fp0`, `dd0fs0`, `dd0mp0`, `dd0ms0`, `de0cn0`, `di00p0`, `di0cp0`, `di0cs0`, `di0fp0`, `di0fs0`, `di0mp0`, `di0ms0`, `dn00p0`, `dn0cp0`, `dn0cs0`, `dn0fp0`, `dn0fs0`, `dn0mp0`, `dn0ms0`, `dp1cps`, `dp1css`, `dp1fpp`, `dp1fsp`, `dp1mpp`, `dp1msp`, `dp1mss`, `dp2cps`, `dp2css`, `dp2fpp`, `dp2fsp`, `dp3cp0`, `dp3cs0`, `dp3fs0`, `dp3mp0`, `dp3ms0`, `dt0cn0`, `dt0fs0`, `dt0ms0`, `faa`, `fat`, `fc`, `fd`, `fe`, `fg`, `fh`, `fia`, `fit`, `fp`, `fpa`, `fpt`, `fs`, `fx`, `fz`, `i`, `nc00000`, `nccn000`, `nccp000`, `nccs000`, `ncf0000`, `ncfn000`, `ncfp000`, `ncfs000`, `ncfs00a`, `ncmn000`, `ncmp000`, `ncms00`, `ncms000`, `np00000`, `np0000a`, `np0000l`, `np0000o`, `np0000p`, `p0000000`, `p010p000`, `p010s000`, `p020s000`, `p0300000`, `pd0cp000`, `pd0cs000`, `pd0fp000`, `pd0fs000`, `pd0mp000`, `pd0ms000`, `pd0ns000`, `pe000000`, `pi000000`, `pi00s000`, `pi0cp000`, `pi0cs000`, `pi0fp000`, `pi0fs000`, `pi0mp0`, `pi0mp000`, `pi0ms0`, `pi0ms000`, `pn0cp000`, `pn0cs000`, `pn0fp000`, `pn0fs000`, `pn0mp000`, `pn0ms000`, `pp1cn000`, `pp1cp000`, `pp1cs000`, `pp1csn00`, `pp1cso00`, `pp1fs000`, `pp1mp000`, `pp2cp000`, `pp2cp00p`, `pp2cs000`, `pp2cs00p`, `pp2csn00`, `pp2cso00`, `pp300000`, `pp30p000`, `pp30sa00`, `pp3cn000`, `pp3cna00`, `pp3cno00`, `pp3cpa00`, `pp3cpd00`, `pp3csa00`, `pp3csd00`, `pp3fp000`, `pp3fpa00`, `pp3fs000`, `pp3fsa00`, `pp3mp000`, `pp3mpa00`, `pp3ms000`, `pp3msa00`, `pp3ns000`, `pr00000`, `pr000000`, `pr0cn000`, `pr0cp000`, `pr0cs000`, `pr0fp000`, `pr0fs000`, `pr0mp000`, `pr0ms000`, `pt000000`, `pt0cp000`, `pt0cs000`, `pt0fp000`, `pt0mp000`, `pt0ms000`, `px1fp0p0`, `px1fs0p0`, `px1fs0s0`, `px1mp0p0`, `px1ms0p0`, `px1ms0s0`, `px2fs0s0`, `px2mp000`, `px2ms0s0`, `px3fp000`, `px3fs000`, `px3mp000`, `px3ms000`, `px3ns000`, `rg`, `rn`, `spcms`, `sps00`, `vag0000`, `vaic1p0`, `vaic3p0`, `vaic3s0`, `vaif1p0`, `vaif1s0`, `vaif2s0`, `vaif3p0`, `vaif3s0`, `vaii1p0`, `vaii1s0`, `vaii2s0`, `vaii3p0`, `vaii3s0`, `vaip1p0`, `vaip1s0`, `vaip2s0`, `vaip3p0`, `vaip3s0`, `vais3p0`, `vais3s0`, `vam02s0`, `vam03s0`, `van0000`, `vap00sm`, `vasi1p0`, `vasi1s0`, `vasi3p0`, `vasi3s0`, `vasp1p0`, `vasp1s0`, `vasp3p0`, `vasp3s0`, `vmg0000`, `vmic1p0`, `vmic1s0`, `vmic2s0`, `vmic3p0`, `vmic3s0`, `vmif1p0`, `vmif1s0`, `vmif2s0`, `vmif3p0`, `vmif3s0`, `vmii1p0`, `vmii1s0`, `vmii2s0`, `vmii3p0`, `vmii3s0`, `vmip1p0`, `vmip1s0`, `vmip2p0`, `vmip2s0`, `vmip3p0`, `vmip3s0`, `vmip3sm`, `vmis1p0`, `vmis1s0`, `vmis2s0`, `vmis3p0`, `vmis3s0`, `vmm01p0`, `vmm02p0`, `vmm02s0`, `vmm03p0`, `vmm03s0`, `vmn0000`, `vmp00fs`, `vmp00ms`, `vmp00pf`, `vmp00pm`, `vmp00sf`, `vmp00sm`, `vmsi1p0`, `vmsi1s0`, `vmsi3p0`, `vmsi3s0`, `vmsp1p0`, `vmsp1s0`, `vmsp2p0`, `vmsp2s0`, `vmsp3p0`, `vmsp3s0`, `vsg0000`, `vsic1s0`, `vsic2s0`, `vsic3p0`, `vsic3s0`, `vsif1s0`, `vsif3p0`, `vsif3s0`, `vsii1p0`, `vsii1s0`, `vsii3p0`, `vsii3s0`, `vsip1p0`, `vsip1s0`, `vsip2s0`, `vsip3p0`, `vsip3s0`, `vsis1s0`, `vsis3p0`, `vsis3s0`, `vsm02s0`, `vsm03s0`, `vsn0000`, `vsp00sm`, `vssi3p0`, `vssi3s0`, `vssp1p0`, `vssp1s0`, `vssp2s0`, `vssp3p0`, `vssp3s0`, `w`, `z`, `zm`, `zp`, `zu` | | **`morphologizer`** | `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=ADP`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=PROPN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `NumForm=Digit\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Comm`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=ADV`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PUNCT\|PunctType=Peri`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=ADJ`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `POS=PRON\|PronType=Int,Rel`, `Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=SCONJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf`, `POS=VERB\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Quot`, `POS=ADV\|Polarity=Neg`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc,Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc,Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Ger`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `AdvType=Tim\|POS=NOUN`, `Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=PART`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `AdvType=Tim\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `POS=PUNCT`, `POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Case=Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PUNCT\|PunctType=Colo`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PUNCT\|PunctType=Semi`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=INTJ`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PUNCT\|PunctType=Dash`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=NOUN\|VerbForm=Inf`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `POS=DET\|PronType=Ind`, `POS=DET\|PronType=Int,Rel`, `AdvType=Tim\|POS=ADV`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Degree=Abs\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Definite=Ind\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=SCONJ\|PronType=Int,Rel`, `Case=Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `NumType=Card\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Case=Acc,Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Acc,Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Abs\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Int,Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Com\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Pre\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NOUN\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Int,Rel`, `POS=SYM`, `Number=Sing\|POS=VERB\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Degree=Abs\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Abs\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Art`, `Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Def\|Foreign=Yes\|POS=DET\|PronType=Art`, `Case=Com\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `NumType=Card\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|Typo=Yes\|VerbForm=Fin`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Tot`, `AdvType=Tim\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=AUX\|VerbForm=Fin`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=X`, `Degree=Abs\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADP`, `Foreign=Yes\|POS=CCONJ`, `Foreign=Yes\|POS=PROPN`, `Case=Com\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=NOUN\|VerbForm=Part`, `Case=Com\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Number=Sing\|POS=DET\|PronType=Int,Rel`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=X`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Degree=Cmp\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Ind`, `POS=NOUN\|PunctType=Comm`, `POS=PRON\|PronType=Neg`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:impers`, `expl:pass`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`textcat`** | `Economía`, `Entretenimiento`, `Historia`, `Humanidades`, `Derecho`, `Matemáticas`, `Música`, `Filosofía`, `Política`, `Religión`, `Deporte`, `Ciencia_y_Tecnología` | </details> | 98c1d7200e59e1866c87d92b202f54fb |
mit | ['spacy', 'token-classification', 'text-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TAG_ACC` | 95.39 | | `POS_ACC` | 98.60 | | `MORPH_ACC` | 98.10 | | `LEMMA_ACC` | 97.98 | | `DEP_UAS` | 91.26 | | `DEP_LAS` | 88.09 | | `SENTS_P` | 95.38 | | `SENTS_R` | 96.54 | | `SENTS_F` | 95.96 | | `TOK2VEC_LOSS` | 7166396.29 | | `TAGGER_LOSS` | 1262344.25 | | `MORPHOLOGIZER_LOSS` | 311469.37 | | `PARSER_LOSS` | 4991259.73 | | `CATS_SCORE` | 99.14 | | `CATS_MICRO_P` | 97.52 | | `CATS_MICRO_R` | 96.19 | | `CATS_MICRO_F` | 96.85 | | `CATS_MACRO_P` | 97.25 | | `CATS_MACRO_R` | 95.42 | | `CATS_MACRO_F` | 96.31 | | `CATS_MACRO_AUC` | 99.14 | | bcddce0d97534fa1bf1079978bb5e905 |
apache-2.0 | ['generated_from_trainer'] | false | Summarization: mukayese/mbart-large-turkish-sum This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum/tu dataset. It achieves the following results on the evaluation set: - Rouge1: 47.4222 - Rouge2: 34.8624 - Rougel: 42.2487 - Rougelsum: 43.9494 Check [this](https://arxiv.org/abs/2203.01215) paper for more details on the model and the dataset. | a3a5157c2bfb79bd908ca02fecf14cf7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 - label_smoothing_factor: 0.1 | da83dc72cc08ba032ffa570594fff00d |
apache-2.0 | ['generated_from_trainer'] | false | Citation ``` @misc{safaya-etal-2022-mukayese, title={Mukayese: Turkish NLP Strikes Back}, author={Ali Safaya and Emirhan Kurtuluş and Arda Göktoğan and Deniz Yuret}, year={2022}, eprint={2203.01215}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 67478e06eab0300c119ccc1ca71009c8 |
mit | [] | false | model by avantcontra This your the Stable Diffusion model fine-tuned the face2contra-sd-dreambooth concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks face2contra** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And 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), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept:                      | e93210723bf38c3b07f75572effa1da1 |
creativeml-openrail-m | ['text-to-image'] | false | Duskfall's Pink Spider Plushie 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 plushiedsk (use that on your prompt) | eabfe12998999e8a2838c93f254eeb7d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 35.0 | b05cb335a33a651732d52f68226552f0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.2893 - Wer: 0.2713 | efcf78778328c987d2e9baba865d5200 |
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: 0.0003 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 00a23154d0afcce477adf7c3c60dff9c |
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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.8647 | 14.28 | 200 | 0.2758 | 0.2568 | | 1.3376 | 28.56 | 400 | 0.2754 | 0.2722 | | 1.1975 | 42.84 | 600 | 0.2929 | 0.2901 | | 1.1024 | 57.14 | 800 | 0.2904 | 0.2928 | | 1.0257 | 71.42 | 1000 | 0.2915 | 0.2823 | | 0.9628 | 85.7 | 1200 | 0.2936 | 0.2749 | | 0.9109 | 99.98 | 1400 | 0.2893 | 0.2713 | | e0e02fb60bf0e332d7ae6397ee2cd6ac |
apache-2.0 | ['generated_from_trainer'] | false | gpt-neo-125M-Byethon This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6609 | ab0117e6d42439db4aa46da541032199 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 237 | 0.8348 | | No log | 2.0 | 474 | 0.6931 | | 0.8151 | 3.0 | 711 | 0.6609 | | 18fd348e6209188c7bacca431087da61 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | This is WD1.4 with .safetensors and fp16, which is unofficial fork - [Waifu Diffusion 1.4 Anime Epoch 2 Safetensors](https://huggingface.co/subaqua/_unofficial-WD1.4-fp16-safetensors/resolve/main/wd-1-4-anime_e2-fp16.safetensors): A faster-loading and lighter version of WD1.4 Anime E2 - [Waifu Diffusion 1.4 Anime Safetensors Inference Config](https://huggingface.co/subaqua/_unofficial-WD1.4-fp16-safetensors/resolve/main/wd-1-4-anime_e2-fp16.yaml): A file included to allow for inference with Automatic's WebUI and with the original Stable Diffusion codebase. This configuration file is modified for "Waifu Diffusion 1.4 Anime Inference Config" with the following changes: ``` model: params: unet_config: params: use_checkpoint: False ``` | 23564b597bac4df300de26b8e1d0c523 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Inherited License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) | 22b7d94013bff5ea4120337c0c262700 |
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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 - mixed_precision_training: Native AMP | 04e353e002e650a29946d4d0de669be6 |
apache-2.0 | [] | false | MuRIL: Multilingual Representations for Indian Languages === MuRIL is a BERT model pre-trained on 17 Indian languages and their transliterated counterparts. We have released the pre-trained model (with the MLM layer intact, enabling masked word predictions) in this repository. We have also released the encoder on [TFHub](https://tfhub.dev/google/MuRIL/1) with an additional pre-processing module, that processes raw text into the expected input format for the encoder. You can find more details on MuRIL in this [paper](http://arxiv.org/abs/2103.10730). | 825b4b7038e3915b3295cd5c283b5054 |
apache-2.0 | [] | false | Overview This model uses a BERT base architecture [1] pretrained from scratch using the Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6] Indian languages. We use a training paradigm similar to multilingual bert, with a few modifications as listed: * We include translation and transliteration segment pairs in training as well. * We keep an exponent value of 0.3 and not 0.7 for upsampling, shown to enhance low-resource performance. [7] See the Training section for more details. | d23468b6902f8eb9f679a606b5c95494 |
apache-2.0 | [] | false | Training The MuRIL model is pre-trained on monolingual segments as well as parallel segments as detailed below : * Monolingual Data : We make use of publicly available corpora from Wikipedia and Common Crawl for 17 Indian languages. * Parallel Data : We have two types of parallel data : * Translated Data : We obtain translations of the above monolingual corpora using the Google NMT pipeline. We feed translated segment pairs as input. We also make use of the publicly available PMINDIA corpus. * Transliterated Data : We obtain transliterations of Wikipedia using the IndicTrans [8] library. We feed transliterated segment pairs as input. We also make use of the publicly available Dakshina dataset. We keep an exponent value of 0.3 to calculate duplication multiplier values for upsampling of lower resourced languages and set dupe factors accordingly. Note, we limit transliterated pairs to Wikipedia only. The model was trained using a self-supervised masked language modeling task. We do whole word masking with a maximum of 80 predictions. The model was trained for 1000K steps, with a batch size of 4096, and a max sequence length of 512. | ed5234974bcfca5fef00cc8d02a76b32 |
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