librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
giulio86/65	giulio86	2022-11-08T20:18:43Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-11-08T20:18:43Z	--- license: creativeml-openrail-m ---
huggingtweets/big___oven-codeinecucumber	huggingtweets	2022-11-08T19:32:56Z	103	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-25T19:41:48Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1579203041764442116/RSLookYD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1571653458972794884/eaxhUsib_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gutted & oskcar</div> <div style="text-align: center; font-size: 14px;">@big___oven-codeinecucumber</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gutted & oskcar. \| Data \| Gutted \| oskcar \| \| --- \| --- \| --- \| \| Tweets downloaded \| 1761 \| 2669 \| \| Retweets \| 243 \| 635 \| \| Short tweets \| 326 \| 308 \| \| Tweets kept \| 1192 \| 1726 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qyf2pl5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @big___oven-codeinecucumber's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rr9twhn) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rr9twhn/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/big___oven-codeinecucumber') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base	gary109	2022-11-08T19:17:51Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "dataset:ai_light_dance", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-07T10:38:40Z	--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer datasets: - ai_light_dance model-index: - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ai-light-dance_drums_ft_pretrain_wav2vec2-base This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base) on the GARY109/AI_LIGHT_DANCE - ONSET-DRUMS dataset. It achieves the following results on the evaluation set: - Loss: 1.8991 - Wer: 0.6046 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.9 \| 8 \| 2.0434 \| 0.6226 \| \| 0.4739 \| 1.9 \| 16 \| 2.1024 \| 0.6247 \| \| 0.4693 \| 2.9 \| 24 \| 1.9824 \| 0.6211 \| \| 0.5139 \| 3.9 \| 32 \| 2.2962 \| 0.6429 \| \| 0.5081 \| 4.9 \| 40 \| 2.2201 \| 0.6292 \| \| 0.5081 \| 5.9 \| 48 \| 2.1399 \| 0.6208 \| \| 0.5785 \| 6.9 \| 56 \| 2.1451 \| 0.6417 \| \| 0.533 \| 7.9 \| 64 \| 2.1184 \| 0.6330 \| \| 0.5141 \| 8.9 \| 72 \| 2.0230 \| 0.6342 \| \| 0.4971 \| 9.9 \| 80 \| 2.2137 \| 0.6381 \| \| 0.4971 \| 10.9 \| 88 \| 2.1159 \| 0.6253 \| \| 0.5645 \| 11.9 \| 96 \| 2.0966 \| 0.6247 \| \| 0.4932 \| 12.9 \| 104 \| 1.9249 \| 0.6223 \| \| 0.4918 \| 13.9 \| 112 \| 2.0445 \| 0.6235 \| \| 0.5053 \| 14.9 \| 120 \| 2.1317 \| 0.6304 \| \| 0.5053 \| 15.9 \| 128 \| 2.0723 \| 0.6256 \| \| 0.5565 \| 16.9 \| 136 \| 2.1390 \| 0.6402 \| \| 0.4819 \| 17.9 \| 144 \| 1.9556 \| 0.6321 \| \| 0.5131 \| 18.9 \| 152 \| 1.9886 \| 0.6333 \| \| 0.4798 \| 19.9 \| 160 \| 1.9700 \| 0.6259 \| \| 0.4798 \| 20.9 \| 168 \| 1.9771 \| 0.6295 \| \| 0.5221 \| 21.9 \| 176 \| 1.9880 \| 0.6235 \| \| 0.4862 \| 22.9 \| 184 \| 2.0994 \| 0.6298 \| \| 0.4831 \| 23.9 \| 192 \| 2.0521 \| 0.6205 \| \| 0.4952 \| 24.9 \| 200 \| 1.9838 \| 0.6064 \| \| 0.4952 \| 25.9 \| 208 \| 2.0319 \| 0.6103 \| \| 0.5119 \| 26.9 \| 216 \| 2.0419 \| 0.6160 \| \| 0.4996 \| 27.9 \| 224 \| 2.0073 \| 0.6178 \| \| 0.488 \| 28.9 \| 232 \| 2.1740 \| 0.6304 \| \| 0.4978 \| 29.9 \| 240 \| 2.2731 \| 0.6163 \| \| 0.4978 \| 30.9 \| 248 \| 2.2420 \| 0.6205 \| \| 0.5259 \| 31.9 \| 256 \| 2.0561 \| 0.6184 \| \| 0.47 \| 32.9 \| 264 \| 1.9455 \| 0.6136 \| \| 0.5132 \| 33.9 \| 272 \| 1.9307 \| 0.6043 \| \| 0.4972 \| 34.9 \| 280 \| 2.0536 \| 0.6127 \| \| 0.4972 \| 35.9 \| 288 \| 1.9113 \| 0.6223 \| \| 0.5147 \| 36.9 \| 296 \| 1.9317 \| 0.6286 \| \| 0.4914 \| 37.9 \| 304 \| 2.1810 \| 0.6241 \| \| 0.472 \| 38.9 \| 312 \| 2.1403 \| 0.6160 \| \| 0.4825 \| 39.9 \| 320 \| 2.1141 \| 0.6094 \| \| 0.4825 \| 40.9 \| 328 \| 2.2870 \| 0.6031 \| \| 0.5138 \| 41.9 \| 336 \| 2.1404 \| 0.6181 \| \| 0.48 \| 42.9 \| 344 \| 2.0243 \| 0.6265 \| \| 0.4598 \| 43.9 \| 352 \| 2.1117 \| 0.6199 \| \| 0.474 \| 44.9 \| 360 \| 2.0378 \| 0.6321 \| \| 0.474 \| 45.9 \| 368 \| 2.1919 \| 0.6211 \| \| 0.4933 \| 46.9 \| 376 \| 2.3645 \| 0.6109 \| \| 0.4692 \| 47.9 \| 384 \| 2.1920 \| 0.6076 \| \| 0.4716 \| 48.9 \| 392 \| 2.3663 \| 0.6034 \| \| 0.4601 \| 49.9 \| 400 \| 2.2838 \| 0.6280 \| \| 0.4601 \| 50.9 \| 408 \| 2.0287 \| 0.6148 \| \| 0.4891 \| 51.9 \| 416 \| 2.1346 \| 0.6130 \| \| 0.4506 \| 52.9 \| 424 \| 2.1556 \| 0.6181 \| \| 0.4581 \| 53.9 \| 432 \| 2.0560 \| 0.6229 \| \| 0.4485 \| 54.9 \| 440 \| 1.9944 \| 0.5971 \| \| 0.4485 \| 55.9 \| 448 \| 1.9791 \| 0.6097 \| \| 0.4942 \| 56.9 \| 456 \| 2.1166 \| 0.6070 \| \| 0.4748 \| 57.9 \| 464 \| 2.0271 \| 0.6124 \| \| 0.4229 \| 58.9 \| 472 \| 2.0437 \| 0.6229 \| \| 0.45 \| 59.9 \| 480 \| 2.1012 \| 0.6142 \| \| 0.45 \| 60.9 \| 488 \| 1.9151 \| 0.6049 \| \| 0.4936 \| 61.9 \| 496 \| 1.8991 \| 0.6046 \| \| 0.4602 \| 62.9 \| 504 \| 1.9813 \| 0.6112 \| \| 0.4626 \| 63.9 \| 512 \| 1.9372 \| 0.6136 \| \| 0.445 \| 64.9 \| 520 \| 1.9060 \| 0.6154 \| \| 0.445 \| 65.9 \| 528 \| 1.9574 \| 0.6151 \| \| 0.4907 \| 66.9 \| 536 \| 2.0947 \| 0.6022 \| \| 0.4723 \| 67.9 \| 544 \| 2.0061 \| 0.6010 \| \| 0.4103 \| 68.9 \| 552 \| 1.9557 \| 0.6094 \| \| 0.4808 \| 69.9 \| 560 \| 2.1042 \| 0.6088 \| \| 0.4808 \| 70.9 \| 568 \| 2.1360 \| 0.6073 \| \| 0.4682 \| 71.9 \| 576 \| 2.1290 \| 0.6013 \| \| 0.4472 \| 72.9 \| 584 \| 1.9454 \| 0.5989 \| \| 0.4259 \| 73.9 \| 592 \| 2.0937 \| 0.6043 \| \| 0.4464 \| 74.9 \| 600 \| 2.0822 \| 0.6058 \| \| 0.4464 \| 75.9 \| 608 \| 2.0128 \| 0.6058 \| \| 0.4775 \| 76.9 \| 616 \| 1.9744 \| 0.6094 \| \| 0.4394 \| 77.9 \| 624 \| 1.9992 \| 0.6010 \| \| 0.418 \| 78.9 \| 632 \| 2.1693 \| 0.5947 \| \| 0.4384 \| 79.9 \| 640 \| 2.1326 \| 0.5923 \| \| 0.4384 \| 80.9 \| 648 \| 2.1151 \| 0.5950 \| \| 0.4971 \| 81.9 \| 656 \| 2.1581 \| 0.5923 \| \| 0.4176 \| 82.9 \| 664 \| 2.0876 \| 0.6013 \| \| 0.4312 \| 83.9 \| 672 \| 2.1316 \| 0.5935 \| \| 0.4408 \| 84.9 \| 680 \| 2.2627 \| 0.5971 \| \| 0.4408 \| 85.9 \| 688 \| 2.2799 \| 0.6112 \| \| 0.4678 \| 86.9 \| 696 \| 2.1239 \| 0.5989 \| \| 0.4288 \| 87.9 \| 704 \| 2.1574 \| 0.5983 \| \| 0.4157 \| 88.9 \| 712 \| 2.2125 \| 0.5908 \| \| 0.444 \| 89.9 \| 720 \| 2.0542 \| 0.5986 \| \| 0.444 \| 90.9 \| 728 \| 2.0899 \| 0.5920 \| \| 0.4694 \| 91.9 \| 736 \| 2.1122 \| 0.6076 \| \| 0.4314 \| 92.9 \| 744 \| 2.0634 \| 0.5950 \| \| 0.4348 \| 93.9 \| 752 \| 2.0333 \| 0.6046 \| \| 0.4558 \| 94.9 \| 760 \| 2.1188 \| 0.5956 \| \| 0.4558 \| 95.9 \| 768 \| 2.0606 \| 0.5995 \| \| 0.461 \| 96.9 \| 776 \| 2.0600 \| 0.5971 \| \| 0.4258 \| 97.9 \| 784 \| 2.0479 \| 0.6040 \| \| 0.4395 \| 98.9 \| 792 \| 2.1282 \| 0.6055 \| \| 0.4282 \| 99.9 \| 800 \| 2.0593 \| 0.6043 \| \| 0.4282 \| 100.9 \| 808 \| 2.0592 \| 0.5920 \| \| 0.4623 \| 101.9 \| 816 \| 2.0852 \| 0.5944 \| \| 0.4392 \| 102.9 \| 824 \| 2.2024 \| 0.5920 \| \| 0.4308 \| 103.9 \| 832 \| 2.1786 \| 0.5935 \| \| 0.4375 \| 104.9 \| 840 \| 2.1085 \| 0.5911 \| \| 0.4375 \| 105.9 \| 848 \| 2.0724 \| 0.5974 \| \| 0.4501 \| 106.9 \| 856 \| 2.1306 \| 0.5881 \| \| 0.4273 \| 107.9 \| 864 \| 2.1340 \| 0.5899 \| \| 0.4234 \| 108.9 \| 872 \| 2.1125 \| 0.5980 \| \| 0.4289 \| 109.9 \| 880 \| 2.0526 \| 0.6007 \| \| 0.4289 \| 110.9 \| 888 \| 2.0955 \| 0.5884 \| \| 0.478 \| 111.9 \| 896 \| 2.1146 \| 0.5872 \| \| 0.4143 \| 112.9 \| 904 \| 2.2310 \| 0.5899 \| \| 0.4193 \| 113.9 \| 912 \| 2.2165 \| 0.5899 \| \| 0.4159 \| 114.9 \| 920 \| 2.1631 \| 0.5941 \| \| 0.4159 \| 115.9 \| 928 \| 2.1371 \| 0.5938 \| \| 0.4776 \| 116.9 \| 936 \| 2.0972 \| 0.5935 \| \| 0.4143 \| 117.9 \| 944 \| 2.1248 \| 0.5917 \| \| 0.4022 \| 118.9 \| 952 \| 2.1317 \| 0.5956 \| \| 0.4346 \| 119.9 \| 960 \| 2.1237 \| 0.5992 \| \| 0.4346 \| 120.9 \| 968 \| 2.0684 \| 0.5935 \| \| 0.4564 \| 121.9 \| 976 \| 2.0722 \| 0.5947 \| \| 0.4243 \| 122.9 \| 984 \| 2.1361 \| 0.5884 \| \| 0.413 \| 123.9 \| 992 \| 2.1207 \| 0.5893 \| \| 0.4113 \| 124.9 \| 1000 \| 2.0697 \| 0.5837 \| \| 0.4113 \| 125.9 \| 1008 \| 2.1005 \| 0.5875 \| \| 0.4426 \| 126.9 \| 1016 \| 2.0822 \| 0.5870 \| \| 0.4255 \| 127.9 \| 1024 \| 2.0572 \| 0.5959 \| \| 0.4214 \| 128.9 \| 1032 \| 2.0343 \| 0.5935 \| \| 0.4042 \| 129.9 \| 1040 \| 2.0282 \| 0.5902 \| \| 0.4042 \| 130.9 \| 1048 \| 2.0314 \| 0.5846 \| \| 0.4515 \| 131.9 \| 1056 \| 2.0621 \| 0.5870 \| \| 0.4138 \| 132.9 \| 1064 \| 2.0704 \| 0.5938 \| \| 0.4289 \| 133.9 \| 1072 \| 2.0222 \| 0.5896 \| \| 0.3908 \| 134.9 \| 1080 \| 2.0879 \| 0.5855 \| \| 0.3908 \| 135.9 \| 1088 \| 2.1068 \| 0.5822 \| \| 0.4489 \| 136.9 \| 1096 \| 2.0702 \| 0.5837 \| \| 0.4191 \| 137.9 \| 1104 \| 2.1093 \| 0.5881 \| \| 0.4149 \| 138.9 \| 1112 \| 2.1046 \| 0.5819 \| \| 0.4127 \| 139.9 \| 1120 \| 2.1729 \| 0.5777 \| \| 0.4127 \| 140.9 \| 1128 \| 2.1636 \| 0.5810 \| \| 0.4449 \| 141.9 \| 1136 \| 2.1515 \| 0.5786 \| \| 0.3977 \| 142.9 \| 1144 \| 2.1531 \| 0.5774 \| \| 0.4121 \| 143.9 \| 1152 \| 2.0857 \| 0.5816 \| \| 0.4363 \| 144.9 \| 1160 \| 2.1372 \| 0.5822 \| \| 0.4363 \| 145.9 \| 1168 \| 2.1902 \| 0.5828 \| \| 0.4318 \| 146.9 \| 1176 \| 2.1465 \| 0.5831 \| \| 0.4112 \| 147.9 \| 1184 \| 2.0697 \| 0.5858 \| \| 0.4292 \| 148.9 \| 1192 \| 2.0850 \| 0.5837 \| \| 0.4182 \| 149.9 \| 1200 \| 2.1171 \| 0.5846 \| \| 0.4182 \| 150.9 \| 1208 \| 2.1020 \| 0.5867 \| \| 0.4381 \| 151.9 \| 1216 \| 2.1052 \| 0.5849 \| \| 0.4235 \| 152.9 \| 1224 \| 2.1430 \| 0.5864 \| \| 0.4173 \| 153.9 \| 1232 \| 2.1131 \| 0.5834 \| \| 0.3927 \| 154.9 \| 1240 \| 2.1134 \| 0.5846 \| \| 0.3927 \| 155.9 \| 1248 \| 2.1173 \| 0.5846 \| \| 0.4492 \| 156.9 \| 1256 \| 2.0772 \| 0.5801 \| \| 0.4313 \| 157.9 \| 1264 \| 2.0309 \| 0.5861 \| \| 0.4015 \| 158.9 \| 1272 \| 2.0887 \| 0.5819 \| \| 0.4268 \| 159.9 \| 1280 \| 2.1812 \| 0.5849 \| \| 0.4268 \| 160.9 \| 1288 \| 2.1568 \| 0.5881 \| \| 0.4496 \| 161.9 \| 1296 \| 2.0805 \| 0.5801 \| \| 0.4121 \| 162.9 \| 1304 \| 2.0461 \| 0.5872 \| \| 0.401 \| 163.9 \| 1312 \| 2.0377 \| 0.5864 \| \| 0.4192 \| 164.9 \| 1320 \| 2.0183 \| 0.5872 \| \| 0.4192 \| 165.9 \| 1328 \| 2.0107 \| 0.5855 \| \| 0.4466 \| 166.9 \| 1336 \| 2.0528 \| 0.5881 \| \| 0.3981 \| 167.9 \| 1344 \| 2.0511 \| 0.5878 \| \| 0.3967 \| 168.9 \| 1352 \| 2.0374 \| 0.5867 \| \| 0.4072 \| 169.9 \| 1360 \| 2.0554 \| 0.5867 \| \| 0.4072 \| 170.9 \| 1368 \| 2.0388 \| 0.5858 \| \| 0.4581 \| 171.9 \| 1376 \| 2.0188 \| 0.5914 \| \| 0.3937 \| 172.9 \| 1384 \| 1.9999 \| 0.5852 \| \| 0.4074 \| 173.9 \| 1392 \| 1.9738 \| 0.5840 \| \| 0.4085 \| 174.9 \| 1400 \| 2.0090 \| 0.5843 \| \| 0.4085 \| 175.9 \| 1408 \| 1.9990 \| 0.5864 \| \| 0.4224 \| 176.9 \| 1416 \| 2.0391 \| 0.5852 \| \| 0.4471 \| 177.9 \| 1424 \| 2.0262 \| 0.5855 \| \| 0.4233 \| 178.9 \| 1432 \| 2.0621 \| 0.5801 \| \| 0.409 \| 179.9 \| 1440 \| 2.0486 \| 0.5846 \| \| 0.409 \| 180.9 \| 1448 \| 2.0508 \| 0.5807 \| \| 0.4518 \| 181.9 \| 1456 \| 2.0241 \| 0.5887 \| \| 0.4077 \| 182.9 \| 1464 \| 2.0169 \| 0.5843 \| \| 0.4197 \| 183.9 \| 1472 \| 2.0014 \| 0.5896 \| \| 0.4237 \| 184.9 \| 1480 \| 2.0189 \| 0.5843 \| \| 0.4237 \| 185.9 \| 1488 \| 2.0095 \| 0.5867 \| \| 0.4394 \| 186.9 \| 1496 \| 1.9993 \| 0.5884 \| \| 0.4299 \| 187.9 \| 1504 \| 2.0097 \| 0.5899 \| \| 0.4198 \| 188.9 \| 1512 \| 2.0049 \| 0.5870 \| \| 0.4116 \| 189.9 \| 1520 \| 1.9899 \| 0.5875 \| \| 0.4116 \| 190.9 \| 1528 \| 1.9814 \| 0.5881 \| \| 0.445 \| 191.9 \| 1536 \| 1.9820 \| 0.5887 \| \| 0.4198 \| 192.9 \| 1544 \| 1.9838 \| 0.5881 \| \| 0.4065 \| 193.9 \| 1552 \| 1.9849 \| 0.5884 \| \| 0.3917 \| 194.9 \| 1560 \| 1.9803 \| 0.5867 \| \| 0.3917 \| 195.9 \| 1568 \| 1.9777 \| 0.5881 \| \| 0.4239 \| 196.9 \| 1576 \| 1.9752 \| 0.5875 \| \| 0.4183 \| 197.9 \| 1584 \| 1.9766 \| 0.5872 \| \| 0.3965 \| 198.9 \| 1592 \| 1.9773 \| 0.5872 \| \| 0.4144 \| 199.9 \| 1600 \| 1.9781 \| 0.5872 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
mrahusain/q-FrozenLake-v1-4x4-noSlippery	mrahusain	2022-11-08T18:29:40Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-11-08T18:29:34Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mrahusain/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
ArafatBHossain/bert_uncased_fine_tuned_emotion_dataset	ArafatBHossain	2022-11-08T18:17:50Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-14T05:38:48Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_uncased_fine_tuned_emotion_dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_uncased_fine_tuned_emotion_dataset This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1870 - Accuracy: 0.943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2321 \| 1.0 \| 2000 \| 0.2690 \| 0.924 \| \| 0.1483 \| 2.0 \| 4000 \| 0.1683 \| 0.9415 \| \| 0.0954 \| 3.0 \| 6000 \| 0.1870 \| 0.943 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
philschmid/pyannote-segmentation	philschmid	2022-11-08T17:15:47Z	1,078	8	pyannote-audio	[ "pyannote-audio", "pytorch", "pyannote", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-segmentation", "voice-activity-detection", "overlapped-speech-detection", "resegmentation", "dataset:ami", "dataset:dihard", "dataset:voxconverse", "arxiv:2104.04045", "license:mit", "region:us" ]	voice-activity-detection	2022-11-08T17:13:14Z	--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-segmentation - voice-activity-detection - overlapped-speech-detection - resegmentation datasets: - ami - dihard - voxconverse license: mit inference: false --- # 🎹 Speaker segmentation ![Example](example.png) Model from [End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045), by Hervé Bredin and Antoine Laurent. [Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space. ## Support For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]). For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository. ## Usage Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation). ### Voice activity detection ```python from pyannote.audio.pipelines import VoiceActivityDetection pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation") HYPER_PARAMETERS = { # onset/offset activation thresholds "onset": 0.5, "offset": 0.5, # remove speech regions shorter than that many seconds. "min_duration_on": 0.0, # fill non-speech regions shorter than that many seconds. "min_duration_off": 0.0 } pipeline.instantiate(HYPER_PARAMETERS) vad = pipeline("audio.wav") # `vad` is a pyannote.core.Annotation instance containing speech regions ``` ### Overlapped speech detection ```python from pyannote.audio.pipelines import OverlappedSpeechDetection pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation") pipeline.instantiate(HYPER_PARAMETERS) osd = pipeline("audio.wav") # `osd` is a pyannote.core.Annotation instance containing overlapped speech regions ``` ### Resegmentation ```python from pyannote.audio.pipelines import Resegmentation pipeline = Resegmentation(segmentation="pyannote/segmentation", diarization="baseline") pipeline.instantiate(HYPER_PARAMETERS) resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline}) # where `baseline` should be provided as a pyannote.core.Annotation instance ``` ### Raw scores ```python from pyannote.audio import Inference inference = Inference("pyannote/segmentation") segmentation = inference("audio.wav") # `segmentation` is a pyannote.core.SlidingWindowFeature # instance containing raw segmentation scores like the # one pictured above (output) ``` ## Reproducible research In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation "](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters: \| Voice activity detection \| `onset` \| `offset` \| `min_duration_on` \| `min_duration_off` \| \| ------------------------ \| ------- \| -------- \| ----------------- \| ------------------ \| \| AMI Mix-Headset \| 0.684 \| 0.577 \| 0.181 \| 0.037 \| \| DIHARD3 \| 0.767 \| 0.377 \| 0.136 \| 0.067 \| \| VoxConverse \| 0.767 \| 0.713 \| 0.182 \| 0.501 \| \| Overlapped speech detection \| `onset` \| `offset` \| `min_duration_on` \| `min_duration_off` \| \| --------------------------- \| ------- \| -------- \| ----------------- \| ------------------ \| \| AMI Mix-Headset \| 0.448 \| 0.362 \| 0.116 \| 0.187 \| \| DIHARD3 \| 0.430 \| 0.320 \| 0.091 \| 0.144 \| \| VoxConverse \| 0.587 \| 0.426 \| 0.337 \| 0.112 \| \| Resegmentation of VBx \| `onset` \| `offset` \| `min_duration_on` \| `min_duration_off` \| \| --------------------- \| ------- \| -------- \| ----------------- \| ------------------ \| \| AMI Mix-Headset \| 0.542 \| 0.527 \| 0.044 \| 0.705 \| \| DIHARD3 \| 0.592 \| 0.489 \| 0.163 \| 0.182 \| \| VoxConverse \| 0.537 \| 0.724 \| 0.410 \| 0.563 \| Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories. ## Citation ```bibtex @inproceedings{Bredin2021, Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, Booktitle = {Proc. Interspeech 2021}, Address = {Brno, Czech Republic}, Month = {August}, Year = {2021}, ``` ```bibtex @inproceedings{Bredin2020, Title = {{pyannote.audio: neural building blocks for speaker diarization}}, Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, Address = {Barcelona, Spain}, Month = {May}, Year = {2020}, } ```
aorhan/ddpm-butterflies-128	aorhan	2022-11-08T17:09:51Z	2	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-11-08T16:38:49Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/aorhan/ddpm-butterflies-128/tensorboard?#scalars)
harmonai/maestro-150k	harmonai	2022-11-08T16:40:37Z	30	17	diffusers	[ "diffusers", "audio-generation", "license:mit", "diffusers:DanceDiffusionPipeline", "region:us" ]	null	2022-10-20T12:20:47Z	--- license: mit tags: - audio-generation --- [Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers. ## FP32 ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/maestro-150k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ``` ## FP16 Faster at a small loss of quality ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/maestro-150k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ```
espnet/iam_handwriting_ocr	espnet	2022-11-08T16:28:56Z	4	7	espnet	[ "espnet", "image-to-text", "ocr", "handwriting-recognition", "en", "dataset:iam", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	image-to-text	2022-11-04T17:05:39Z	--- tags: - espnet - image-to-text - ocr - handwriting-recognition language: en datasets: - iam license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/iam_handwriting_ocr` This model was trained by kenzheng99 using iam recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 2169367022b8939d22005e8cf45a65bb20bc0768 pip install -e . cd egs2/iam/ocr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/iam_handwriting_ocr ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Mon Nov 7 13:40:17 EST 2022` - python version: `3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.10.0` - Git hash: `2169367022b8939d22005e8cf45a65bb20bc0768` - Commit date: `Thu Nov 3 20:38:03 2022 -0400` ## asr_train_asr_conformer_extracted_en_char ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|inference_asr_model_valid.acc.ave/test\|2915\|25932\|80.5\|17.3\|2.2\|0.8\|20.3\|72.8\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|inference_asr_model_valid.acc.ave/test\|2915\|125616\|94.0\|4.2\|1.8\|0.7\|6.7\|72.8\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_extracted_en_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 35197 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 200 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 64 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_extracted_en_char/train/speech_shape - exp/asr_stats_extracted_en_char/train/text_shape.char valid_shape_file: - exp/asr_stats_extracted_en_char/valid/speech_shape - exp/asr_stats_extracted_en_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 800 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/extracted/train/feats.scp - speech - kaldi_ark - - dump/extracted/train/text - text - text valid_data_path_and_name_and_type: - - dump/extracted/valid/feats.scp - speech - kaldi_ark - - dump/extracted/valid/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - <space> - e - t - a - o - n - i - r - s - h - l - d - c - u - m - f - p - g - y - w - b - . - ',' - v - k - '-' - T - '''' - M - I - A - '"' - S - P - H - B - C - W - N - G - x - R - E - L - F - '0' - D - '1' - j - O - q - U - K - '!' - '3' - '9' - ( - z - ) - ':' - V - ; - '5' - '2' - J - '8' - Y - '4' - '6' - '?' - '#' - '&' - '7' - / - '*' - Q - X - Z - + - <sos/eos> init: xavier_uniform input_size: 100 ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: null frontend_conf: {} specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_extracted_en_char/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: '202209' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
PaulaAlfy/xlm-roberta-base-finetuned-panx-de-fr	PaulaAlfy	2022-11-08T15:55:07Z	107	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-08T15:16:21Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1907 - F1: 0.8682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2901 \| 1.0 \| 715 \| 0.1864 \| 0.8211 \| \| 0.1576 \| 2.0 \| 1430 \| 0.1667 \| 0.8441 \| \| 0.1038 \| 3.0 \| 2145 \| 0.1710 \| 0.8452 \| \| 0.0701 \| 4.0 \| 2860 \| 0.1787 \| 0.8636 \| \| 0.0449 \| 5.0 \| 3575 \| 0.1907 \| 0.8682 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
bigmorning/whisper_end22	bigmorning	2022-11-08T15:15:27Z	62	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-08T15:15:16Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_end22 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # whisper_end22 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1061 - Train Accuracy: 0.0341 - Validation Loss: 0.5635 - Validation Accuracy: 0.0314 - Epoch: 22 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 5.0856 \| 0.0116 \| 4.4440 \| 0.0123 \| 0 \| \| 4.3149 \| 0.0131 \| 4.0521 \| 0.0142 \| 1 \| \| 3.9260 \| 0.0146 \| 3.7264 \| 0.0153 \| 2 \| \| 3.5418 \| 0.0160 \| 3.3026 \| 0.0174 \| 3 \| \| 2.7510 \| 0.0198 \| 2.0157 \| 0.0241 \| 4 \| \| 1.6782 \| 0.0250 \| 1.3567 \| 0.0273 \| 5 \| \| 1.1705 \| 0.0274 \| 1.0678 \| 0.0286 \| 6 \| \| 0.9126 \| 0.0287 \| 0.9152 \| 0.0294 \| 7 \| \| 0.7514 \| 0.0296 \| 0.8057 \| 0.0299 \| 8 \| \| 0.6371 \| 0.0302 \| 0.7409 \| 0.0302 \| 9 \| \| 0.5498 \| 0.0307 \| 0.6854 \| 0.0306 \| 10 \| \| 0.4804 \| 0.0312 \| 0.6518 \| 0.0307 \| 11 \| \| 0.4214 \| 0.0316 \| 0.6200 \| 0.0310 \| 12 \| \| 0.3713 \| 0.0319 \| 0.5947 \| 0.0311 \| 13 \| \| 0.3281 \| 0.0322 \| 0.5841 \| 0.0311 \| 14 \| \| 0.2891 \| 0.0325 \| 0.5700 \| 0.0313 \| 15 \| \| 0.2550 \| 0.0328 \| 0.5614 \| 0.0313 \| 16 \| \| 0.2237 \| 0.0331 \| 0.5572 \| 0.0313 \| 17 \| \| 0.1959 \| 0.0333 \| 0.5563 \| 0.0314 \| 18 \| \| 0.1698 \| 0.0335 \| 0.5530 \| 0.0314 \| 19 \| \| 0.1455 \| 0.0337 \| 0.5590 \| 0.0314 \| 20 \| \| 0.1242 \| 0.0339 \| 0.5743 \| 0.0313 \| 21 \| \| 0.1061 \| 0.0341 \| 0.5635 \| 0.0314 \| 22 \| ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Tokenizers 0.13.2
bigmorning/whisper_0020	bigmorning	2022-11-08T15:05:11Z	59	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-08T15:05:01Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_0020 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # whisper_0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1698 - Train Accuracy: 0.0335 - Validation Loss: 0.5530 - Validation Accuracy: 0.0314 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 5.0856 \| 0.0116 \| 4.4440 \| 0.0123 \| 0 \| \| 4.3149 \| 0.0131 \| 4.0521 \| 0.0142 \| 1 \| \| 3.9260 \| 0.0146 \| 3.7264 \| 0.0153 \| 2 \| \| 3.5418 \| 0.0160 \| 3.3026 \| 0.0174 \| 3 \| \| 2.7510 \| 0.0198 \| 2.0157 \| 0.0241 \| 4 \| \| 1.6782 \| 0.0250 \| 1.3567 \| 0.0273 \| 5 \| \| 1.1705 \| 0.0274 \| 1.0678 \| 0.0286 \| 6 \| \| 0.9126 \| 0.0287 \| 0.9152 \| 0.0294 \| 7 \| \| 0.7514 \| 0.0296 \| 0.8057 \| 0.0299 \| 8 \| \| 0.6371 \| 0.0302 \| 0.7409 \| 0.0302 \| 9 \| \| 0.5498 \| 0.0307 \| 0.6854 \| 0.0306 \| 10 \| \| 0.4804 \| 0.0312 \| 0.6518 \| 0.0307 \| 11 \| \| 0.4214 \| 0.0316 \| 0.6200 \| 0.0310 \| 12 \| \| 0.3713 \| 0.0319 \| 0.5947 \| 0.0311 \| 13 \| \| 0.3281 \| 0.0322 \| 0.5841 \| 0.0311 \| 14 \| \| 0.2891 \| 0.0325 \| 0.5700 \| 0.0313 \| 15 \| \| 0.2550 \| 0.0328 \| 0.5614 \| 0.0313 \| 16 \| \| 0.2237 \| 0.0331 \| 0.5572 \| 0.0313 \| 17 \| \| 0.1959 \| 0.0333 \| 0.5563 \| 0.0314 \| 18 \| \| 0.1698 \| 0.0335 \| 0.5530 \| 0.0314 \| 19 \| ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Tokenizers 0.13.2
rosamondthalken/t5-base-sci-names	rosamondthalken	2022-11-08T14:39:36Z	8	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "scientific names", "text generation", "en", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-16T15:00:05Z	--- language: - en tags: - scientific names - text generation license: cc-by-sa-4.0 --- # t5-base-sci-names Biodiversity literature is dedicated to the identification, documentation, and categorization of plants, fungi, animals, and other living organisms. Correctly extracting the name of an organism within these documents involves finding the entire scientific name–including the genus, specific epithet, and author name. Extracting these names allows biologists to access documents about a species more comprehensively, and to track an organism’s history of documentation, which includes biological changes and changes in how scientists describe them. t5-base-sci-names uses advances in text-to-text generation to generate scientific names and authors from biodiversity literature. This model was trained on hand-labeled biodiversity texts, including labeled information about a mentioned organism's genus (abbreviated and expanded), specific epithet, and author. This model was trained to output 0-N scientific names with specific prefixes (e.g. "genus = " or "epithet = ") and performs best with anywhere from 20-120 words. You can also use the model in this tutorial for [scientific names generation](https://colab.research.google.com/drive/1GEpnCaMJYiPIhuZiDJ1X1pZsGtGSm8Ds?usp=sharing). Thanks to Damon Little and Nelson Salinas at the New York Botanical Gardens for their support. Note that this model is still a work in progress. Any feedback is welcome.
google/ddpm-ema-cat-256	google	2022-11-08T13:42:16Z	1,133	2	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-07-19T10:45:53Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-cat-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_3.png)
google/ddpm-church-256	google	2022-11-08T13:41:58Z	850	9	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-07-19T10:42:51Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-church-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_3.png)
google/ddpm-ema-bedroom-256	google	2022-11-08T13:41:41Z	392	2	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-07-18T19:49:13Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-bedroom-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_3.png)
google/ddpm-ema-celebahq-256	google	2022-11-08T13:41:29Z	10,679	6	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2022-07-19T10:42:32Z	--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) Paper: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) Authors: Jonathan Ho, Ajay Jain, Pieter Abbeel Abstract: We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. ## Inference DDPM models can use discrete noise schedulers such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the ddpm scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the ddim or pndm schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-celebahq-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) # <- TODO(PVP) add link ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_3.png)
ShinCore/MMDv1-18	ShinCore	2022-11-08T10:51:00Z	0	61	null	[ "stable-diffusion", "stable diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2022-11-05T10:51:25Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable diffusion - text-to-image --- MEGA MERGE DIFF (MMD) VERSION 1-18. 18 MERGED MODELS IN ONE ANNOUNCEMENT: - DUE TO THE FACT THAT I CANNOT SEEM TO CATCH A BREAK AND GET SOME TIME TO ASSUAGE MY OWN INSECURITIES ABOUT THE QUALITY OF THIS MODEL, I AM JUST GOING TO RELEASE IT. FIRST MODEL RELEASE: MMD V1-18 MODEL MERGE ALPHA: - DISCORD INVITE: https://discord.gg/WdmejvKCDG (EDIT 11/6, WILL NOT EXPIRE ANYMORE) - MODEL FAQ, MERGING METHODOLOGY, BORING CHARACTER BACKSTORY: https://discord.com/channels/900672465276116994/1035853968225615922 - LIST OF MERGED MODELS (DUE TO THE NATURE OF SOME OF THE MERGED MODELS, I CANNOT LIST THEM HERE): https://discord.com/channels/900672465276116994/1035895704377368687 - DOWNLOAD LINK: https://huggingface.co/ShinCore/MMDv1-18/tree/main SUMMARY: MMD V1-18 A MEGA MERGE OF SD 1.5 AND 17 OTHER MODELS. IT IS INTENDED TO BE A GENERALIST MODEL, NOT FOCUSED ON ANY SINGLE GENRE OR CATEGORY OR STYLE OR SUBJECT. THERE ARE ALREADY A PROLIFERATION OF GREAT MODELS OUT THERE, COVERING A BROAD SPECTRUM OF CONTENT. HOWEVER, THAT ALSO CAUSES A PROBLEM IN THAT WE HAVE A PROFLIERATION OF GREAT MODELS OUT THERE THAT DO ONE OR TWO THINGS REALLY WELL, BUT THATS KINDA IT. OTHER THAN THOSE ONE OR TWO THINGS THAT HAVE BEEN ADDED TO THE BASE SD MODEL, ITS NO DIFFERENT THAN ANY OF THE OTHER MODELS. MMD WAS CREATED TO ADDRESS THE ISSUE OF DISORGANIZED CONTENT FRAGMENTATION ACROSS HUGGINGFACE, DISCORD, REDDIT, RENTRY.ORG, 4CHAN, AND THE REMAINDER OF THE INTERNET. IT ALSO TRIES TO ADDRESS THE ISSUES INHERENT WITH THE BASE SD 1.5 MODEL. NAMELY, PROBLEMATIC ANATOMY, LACK OF RESPONSIVENESS TO PROMPT ENGINEERING, BLAND OUTPUTS, ETC. THE CURRENT SET OF MERGED MODELS ARE A CROSS SECTION OF MODELS THAT I FEEL IMPROVE AND ENRICH THE BASE MODEL. IN MY TESTS (WHICH YOU CAN TAKE A LOOK AT THE LOGS IN MY #EXPERIMENTS CHANNEL IN THE DISCORD I WORK OUT OF), THE MERGING OF A SPECIFIC SET OF MODELS HAS SHOWN TO IMPROVE HUMAN ANATOMY COHERENCY, INCREASE CREATIVITY AND DETAIL IN BOTH FORE/BACKGROUNDS, AND CAN BE MORE RESPONSIVE TO PROMPTING (PLEASE SEE PROMPTING NOTE BELOW). - DOWNSIDE: TRIGGER TERMS ASSOCIATED WITH SPECIFIC MODELS HAVE A DRASTICALLY REDUCED EFFECT. IF USING A TRIGGER TERM ASSOCIATED WITH A SPECIFIC MODEL, YOU MUST INCREASE THE STR TO SEE ANY EFFECT. THE MODEL CAN ALSO BE MUCH MORE SENSITIVE TO THE SETTINGS THAT YOU USE. I HAVE LISTED SOME RECOMMENDATIONS BELOW. IMPORTANT: I DO NOT, IN ANY WAY, SHAPE OR FORM, CLAIM THAT THIS MODEL IS SUPERIOR TO ANY OTHER MODEL OUT THERE. NOR DO I FEEL THAT I AM SOMEHOW SOME KIND OF SD GURU AND AM AN EXPERT OF ANY KIND. MY INTENTIONS IN CREATING THIS MODEL IS FOR MY OWN PERSONAL GOAL OF USING IT, AS WELL AS OTHER AI TOOLS, TO CREATE A STREAMLINED WORKFLOW PIPELINE THAT WILL ENABLE INDIE SOLO GAME DEVS TO CREATE GAMES WITH GREATER EASE AND EFFICIENCY. I DREAM OF SOMEDAY BEING A SOLO INDIE GAME DEV, AND THIS IS MY WAY OF HEADING TOWARDS THAT GOAL IN AN INDIRECT FASHION. I AM NOT A GENIUS. I AM NOT EVEN A GODDAMN CODER/PROGRAMMER/MATHMETICIAN. I AM COMPLETELY OUT OF MY DEPTH. I SOMETIMES FEEL LIKE THAT ZOOLANDER MEME, HOOTING AND POKING AT A COMPUTER THAT IS BEYOND MY LIMITED COMPREHENSION. I AM NOTHING MORE THAN AN OLD, TIRED, LAZY, AND GRUMPY BASTARD WHO IS SPENDING WHAT LITTLE FREE TIME I HAVE TRYING TO FIGURE THIS CRAP OUT SO THAT I DONT HAVE TO LEARN HOW TO CREATE A GAME FROM SCRATCH. I JUST WANT AN AI TO DO IT FOR ME. NOTES ABOUT USAGE: - MODEL CAN BE A BIT HARSH AND RIGID, BUT PUT OUT SOME AMAZING GENS. 2ND RELEASE WILL BE LESS EXTREME. RECOMMENDED SETTINGS: - IMG HEIGHT/WIDTH MUST BE SET TO A MULTIPLE OF 128 - SET HIRES FIX TO ON. SET FIRST PASS HEIGHT/WIDTH TO HALF OF IMG HEIGHT/WIDTH. - PLAY WITH DENOISING STR, I SET MINE TO .69, YMMV. - MODEL IS SENSITIVE TO CFG. I USUALLY USE 12.5, BUT OTHERS HAVE REPORTED BETTER OUTPUTS AT LOWER/HIGHER VALUES. TRY THEM OUT. DONT BE AFRAID OF GOING REALLY HIGH OR REALLY LOW. - SET RESIZE SEED OPTION TO 512X512. (DDIM RESPONDS BETTER TO THIS SETTING. OTHER SAMPLERS MAY NOT. TURN ON AND OFF AND COMPARE) PROMPTING: - MERGED MODELS USE A COMBO OF STANDARD SD BLIP/CLIP AND DANBOORU TAGS. I USE BOTH IN MY PROMPTS. TRY USING BOTH CLIP AND DANBOORU INTERROGATOR ON IMAGES, GET THE RESULTS FROM BOTH, AND USE THEM IN YOUR OWN PROMPTING. THEY SEEM TO REINFORCE EACH OTHER, THOUGH THIS IS DIFFICULT TO SCIENTIFICALLY VERIFY. I HAVE SEVERAL THEORIES THAT I INTEND TO TEST OUT, AS TIME PERMITS - USE NEGATIVE PROMPTING WITHOUT RESERVATIONS. YES, I HAVE SEEN STATEMENTS TO THE EFFECT THAT NEGATIVE PROMPTING IS LIKE A PLACEBO. ALL I CAN TELL YOU IS THAT, WHEN I REMOVED ALL OF MY NEGATIVE PROMPTS, MY GENS TURNED INTO A HORRORSHOW. I COULD NOT PUT THEM BACK ON FAST ENOUGH. 2ND RELEASE, MMD V1-18 MODEL MERGE (TONED DOWN) ALPHA: - THIS IS THE SAME AS THE FIRST RELEASE,BUT I MERGED BACK IN 25% OF SD 1.5. MORE FORGIVING, LESS EXTREME EDITION. EVERYTHING ELSE IS THE SAME. FINAL NOTE: IT HAS BEEN POINTED OUT TO ME THAT I AM USING ALL CAPS, AS IF I WAS SOMEHOW NOT ALREADY AWARE. AS I HAVE PREVIOUSLY POINTED OUT, I AM AN OLD, TIRED, LAZY AND GRUMPY BASTARD. MY PREVIOUS AND CURRENT PROFESSION HAS GOTTEN ME INTO THE HABIT OF USING ALL CAPS WHEN WORKING. DONT ASK ME THE REASONS, THEY ARE STUPID. AS IN, OTHER PEOPLE THAT I DEAL WITH. I AM PERFECTLY CAPABLE OF USING PROPER CAPITIALIZATION. I CHOOSE NOT TO WHEN I AM FOCUSED, GRINDING AWAY AT SOMETHING, HASTILY TRYING TO FINISH SOMETHING, OR AM IN A BAD MOOD BECAUSE SOMETHING IS IRRITATING ME. SO BASICALLY, PRETTY MUCH ALL THE TIME. I APOLOGIZE, BUT YOUR ARE GOING TO HAVE TO DEAL WITH IT.
bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif	bthomas	2022-11-08T10:23:54Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "SetFitbench", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-08T10:20:10Z	--- license: apache-2.0 tags: - SetFitbench - generated_from_trainer model-index: - name: setfit_bench_bert-base-uncased_finetuned_for_seqclassif results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # setfit_bench_bert-base-uncased_finetuned_for_seqclassif This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3437 \| 1.0 \| 189 \| 0.2666 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
troesy/hateBERT_3epoch	troesy	2022-11-08T10:21:20Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-08T10:07:36Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: hateBERT_3epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hateBERT_3epoch This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2174 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9174 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 174 \| 0.2301 \| 0.0 \| 0.0 \| 0.0 \| 0.9112 \| \| No log \| 2.0 \| 348 \| 0.2192 \| 0.0 \| 0.0 \| 0.0 \| 0.9148 \| \| 0.2311 \| 3.0 \| 522 \| 0.2174 \| 0.0 \| 0.0 \| 0.0 \| 0.9174 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
dreaming-tree/rl_class	dreaming-tree	2022-11-08T10:17:43Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-08T10:16:56Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 73.61 +/- 70.22 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
troesy/toxicBERT_3epoch	troesy	2022-11-08T10:00:48Z	20	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-04T16:27:22Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: toxicBERT_3epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # toxicBERT_3epoch This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2162 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 174 \| 0.2301 \| 0.0 \| 0.0 \| 0.0 \| 0.9110 \| \| No log \| 2.0 \| 348 \| 0.2186 \| 0.0 \| 0.0 \| 0.0 \| 0.9134 \| \| 0.2312 \| 3.0 \| 522 \| 0.2162 \| 0.0 \| 0.0 \| 0.0 \| 0.9173 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
tkubotake/xlm-roberta-base-finetuned-panx-all	tkubotake	2022-11-08T09:07:09Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T03:46:39Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2290 - F1: 0.8629 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1259 \| 1.0 \| 835 \| 0.1879 \| 0.8478 \| \| 0.078 \| 2.0 \| 1670 \| 0.2121 \| 0.8582 \| \| 0.0439 \| 3.0 \| 2505 \| 0.2290 \| 0.8629 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
sd-concepts-library/kodakvision500t	sd-concepts-library	2022-11-08T08:19:22Z	0	14	null	[ "license:mit", "region:us" ]	null	2022-11-08T07:57:07Z	--- license: mit --- ### KodakVision500T on Stable Diffusion This is the `<kodakvision_500T>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). This concept was trained on 6 photographs taken with Kodak Vision 3 500T, through 1800 steps. Here are some generated images from the concept that you will be able to use as a `style`: ![<kodakvision_500T> 4](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_4.png) ![<kodakvision_500T> 3](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_3.png) ![<kodakvision_500T> 2](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_2.png) ![<kodakvision_500T> 1](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_1.png)
Sushanti123/layoutxlm-finetuned-xfund-fr	Sushanti123	2022-11-08T07:26:56Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "dataset:xfun", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-01T08:40:16Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - xfun model-index: - name: layoutxlm-finetuned-xfund-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutxlm-finetuned-xfund-fr This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.10.0+cu111 - Datasets 2.6.1 - Tokenizers 0.13.2
GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan	GuiGel	2022-11-08T07:19:25Z	6	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]	token-classification	2022-11-08T07:16:36Z	--- tags: - flair - token-classification - sequence-tagger-model --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan") # make example sentence sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ```
Meow412/finetuning-sentiment-BERTmodel-A3-allcontents	Meow412	2022-11-08T06:40:42Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-08T05:51:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-BERTmodel-A3-allcontents results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-BERTmodel-A3-allcontents This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2951 - Accuracy: 0.8814 - F1: 0.4138 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
bguan/Reinforce-CartPole-v1	bguan	2022-11-08T05:55:49Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-11-08T02:43:41Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 440.00 +/- 88.54 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
Meow412/finetuning-sentiment-BERTmodel-A3	Meow412	2022-11-08T05:36:32Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-08T05:15:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-BERTmodel-A3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-BERTmodel-A3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3307 - Accuracy: 0.8656 - F1: 0.3576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
RamAnanth1/positive-reframing	RamAnanth1	2022-11-08T04:47:00Z	34	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "arxiv:2204.02952", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-01T20:50:51Z	# Positive Perspectives with Text Reframing Based on the paper [Inducing Positive Perspectives with Text Reframing](https://arxiv.org/abs/2204.02952), this model focuses on the positive reframing task. The purpose of the model is to neutralize a negative point of view and generate a more positive perspective without changing the original meaning. The model provided is obtained from this [HuggingFace Space](https://huggingface.co/spaces/Ella2323/Positive-Reframing) and stored in a separate repository to increase ease of use. All credits go to the original contributors of the abovementioned HuggingFace Space. ### Available strategies for positive reframing: growth: viewing a challenging event as an opportunity for the author to specifically grow or improve himself. impermanence: Saying that bad things don't last forever, will get better soon, and/or that other people have had similar difficulties. neutralizing: Replacing a negative word with a neutral word. For example, “This was a terrible day” becomes “This was a long day”. optimism: Focusing on things about the situation itself, at that moment, that are good (not just predicting a better future). self_affirmation: Talking about what strengths the author already has, or values he admires, such as love, courage, perseverance, etc. thankfulness: Expressing gratitude or gratitude with keywords like appreciate, happy for it, grateful for, good thing, etc.
kit-nlp/bert-base-japanese-sentiment-irony	kit-nlp	2022-11-08T04:23:27Z	481	4	transformers	[ "transformers", "pytorch", "bert", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T06:29:21Z	--- language: ja license: cc-by-sa-4.0 --- # BERT Base Japanese for Irony This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection. The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony02, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-irony" } ```
kit-nlp/yacis-electra-small-japanese-irony	kit-nlp	2022-11-08T04:16:30Z	5	0	transformers	[ "transformers", "pytorch", "electra", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T07:05:34Z	--- language: ja license: cc-by-sa-4.0 --- # YACIS ELECTRA Small Japanese for Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [YACIS ELECTRA small Japanese](https://huggingface.co/ptaszynski/yacis-electra-small-japanese), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022yaciselectra-small-irony, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/yacis-electra-small-japanese-irony" } ```
kit-nlp/electra-small-japanese-discriminator-irony	kit-nlp	2022-11-08T04:11:04Z	4	0	transformers	[ "transformers", "pytorch", "electra", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T07:14:32Z	--- language: ja license: cc-by-sa-4.0 --- # ELECTRA small Japanese discriminator for Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [ELECTRA small Japanese discriminator](https://huggingface.co/izumi-lab/electra-small-japanese-discriminator), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/electra-small-japanese-discriminator-irony" } ```
bigmorning/bigmorning_whisper	bigmorning	2022-11-08T03:44:46Z	61	0	transformers	[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-08T03:13:01Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bigmorning_whisper results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # bigmorning_whisper This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.2
dhshin/ddpm-butterflies-128	dhshin	2022-11-08T03:16:58Z	3	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-25T01:06:18Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/dhshin/ddpm-butterflies-128/tensorboard?#scalars)
QianMolloy/distilbert-base-uncased-finetuned-emotion	QianMolloy	2022-11-08T03:06:18Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T02:51:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.928851862350588 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2178 - Accuracy: 0.9285 - F1: 0.9289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8227 \| 1.0 \| 250 \| 0.3212 \| 0.8985 \| 0.8932 \| \| 0.2463 \| 2.0 \| 500 \| 0.2178 \| 0.9285 \| 0.9289 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.10.0 - Datasets 2.6.1 - Tokenizers 0.13.1
BigSalmon/InformalToFormalLincoln90Paraphrase	BigSalmon	2022-11-08T03:06:10Z	163	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-08T02:37:14Z	data: https://github.com/BigSalmon2/InformalToFormalDataset Text Generation Informal Formal ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase") ``` ``` Demo: https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy ``` ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" input_ids = tokenizer.encode(prompt, return_tensors='pt') outputs = model.generate(input_ids=input_ids, max_length=10 + len(prompt), temperature=1.0, top_k=50, top_p=0.95, do_sample=True, num_return_sequences=5, early_stopping=True) for i in range(5): print(tokenizer.decode(outputs[i])) ``` Most likely outputs (Disclaimer: I highly recommend using this over just generating): ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" text = tokenizer.encode(prompt) myinput, past_key_values = torch.tensor([text]), None myinput = myinput myinput= myinput.to(device) logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) logits = logits[0,-1] probabilities = torch.nn.functional.softmax(logits) best_logits, best_indices = logits.topk(250) best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] text.append(best_indices[0].item()) best_probabilities = probabilities[best_indices].tolist() words = [] print(best_words) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. * informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. * informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` original: microsoft word's [MASK] pricing invites competition. Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition. * original: the library’s quiet atmosphere encourages visitors to [blank] in their work. Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work. ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. * Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. * Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. * Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. * Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. * Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about * Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers * Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of * Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone * Search: What are phrase synonyms for " ``` ``` - nebraska - unicamerical legislature - different from federal house and senate text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate. * - penny has practically no value - should be taken out of circulation - just as other coins have been in us history - lost use - value not enough - to make environmental consequences worthy text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness. * - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. * original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. * wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. * sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. * 1: ``` Keywords to sentences or sentence. ``` ngos are characterized by: □ voluntary citizens' group that is organized on a local, national or international level □ encourage political participation □ often serve humanitarian functions □ work for social, economic, or environmental change * what are the drawbacks of living near an airbnb? □ noise □ parking □ traffic □ security □ strangers * ``` ``` original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung. adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung. * original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark. adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark. * original: ``` ``` original: had trouble deciding. translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation. * original: ``` ``` input: not loyal 1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ). * input: ``` ``` first: ( was complicit in / was involved in ). antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ). * first: ( have no qualms about / see no issue with ). antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ). * first: ( do not see eye to eye / disagree often ). antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ). * first: ``` ``` stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground. * languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo. * dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia. * embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons. ``` Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above): ``` his contention [blank] by the evidence [sep] was refuted [answer] * few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer] * when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer] * the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer] * the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer] * microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer] * ``` ``` original: microsoft word's [MASK] pricing invites competition. Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition. * original: the library’s quiet atmosphere encourages visitors to [blank] in their work. Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work. ``` Backwards ``` Essay Intro (National Parks): text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ). * Essay Intro (D.C. Statehood): washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ). ``` ``` topic: the Golden State Warriors. characterization 1: the reigning kings of the NBA. characterization 2: possessed of a remarkable cohesion. characterization 3: helmed by superstar Stephen Curry. characterization 4: perched atop the league’s hierarchy. characterization 5: boasting a litany of hall-of-famers. * topic: emojis. characterization 1: shorthand for a digital generation. characterization 2: more versatile than words. characterization 3: the latest frontier in language. characterization 4: a form of self-expression. characterization 5: quintessentially millennial. characterization 6: reflective of a tech-centric world. * topic: ``` ``` regular: illinois went against the census' population-loss prediction by getting more residents. VBG: defying the census' prediction of population loss, illinois experienced growth. * regular: microsoft word’s high pricing increases the likelihood of competition. VBG: extortionately priced, microsoft word is inviting competition. * regular: ``` ``` source: badminton should be more popular in the US. QUERY: Based on the given topic, can you develop a story outline? target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing. * source: movies in theaters should be free. QUERY: Based on the given topic, can you develop a story outline? target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay. * source: ``` ``` in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure. * the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule. * the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement. * ``` ``` it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise. question: what does “do likewise” mean in the above context? (a) make the same journey (b) share in the promise of the american dream (c) start anew in the land of opportunity (d) make landfall on the united states * in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure. question: what does “this orientation” mean in the above context? (a) visible business practices (b) candor with the public (c) open, honest communication (d) culture of accountability ``` ``` example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot. text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities. * example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear. text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student. ``` ``` <Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle> * <Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle> ``` ``` accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult (a) in reverential tones (b) with great affection (c) in adulatory fashion (d) in glowing terms ``` ``` clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ). ``` ``` description: when someone thinks that their view is the only right one. synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous. *** description: when you put something off. synonyms: shelve, defer, table, postpone. ``` ``` organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea. rewrite phrases: meritocratic, viability, vision rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability. ``` Note Of all the masking techniques, this one works the best. ``` <Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle> * <Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle> ``` ``` essence: when someone's views are keeping within reasonable. refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ). * essence: when things are worked through in a petty way. refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling. ``` ``` description: when someone thinks that their view is the only right one. synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous. * description: when you put something off. synonyms: shelve, defer, table, postpone. ``` ``` organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea. rewrite phrases: meritocratic, viability, vision rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability. ``` ``` music before bedtime [makes for being able to relax] -> is a recipe for relaxation. ``` ``` [people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway. ``` ``` in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal. * politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ). ``` ``` Q: What is whistleblower protection? A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer. Q: Why are whistleblower protections important? A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution. Q: Why would an employer engage in retribution? A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing. ``` ``` original: the meritocratic nature of crowdfunding [MASK] into their vision's viability. infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability. ``` ``` Leadership \| Lecture 17: Worker Morale What Workers Look for in Companies: • Benefits o Tuition reimbursement o Paid parental leave o 401K matching o Profit sharing o Pension plans o Free meals • Social responsibility o Environmental stewardship o Charitable contributions o Diversity • Work-life balance o Telecommuting o Paid holidays and vacation o Casual dress • Growth opportunities • Job security • Competitive compensation • Recognition o Open-door policies o Whistleblower protection o Employee-of-the-month awards o Positive performance reviews o Bonuses ``` ``` description: business keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification ``` ``` 3. In this task, you are given a company name and you need to find its industry. McDonalds -- Restaurant Facebook -- Social Network IKEA -- Furniture American Express -- Credit Services Nokia -- Telecom Nintendo -- Entertainment 4. In this task, you are given a Month and you need to convert it to its corresponding season April -- Spring December -- Winter July -- Summer October -- Fall February -- Winter 5. In this task, you are given a sentence with a missing word and you need to predict the correct word. Managers should set an _____ for their employees. -- example Some people spend more than four _____ in the gym. -- hours The police were on the _____ of arresting the suspect. -- verge They were looking for _____ on how to solve the problem. -- guidance What is the _____ of the coffee? -- price 6. In this task, you are given a paragraph and you need to reorder it to make it logical. It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters. It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman. It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth. ``` ``` trivia: What is the population of South Korea? response: 51 million. * trivia: What is the minimum voting age in the US? response: 18. * trivia: What are the first ten amendments of the US constitution called? response: Bill of Rights. ```
huggingtweets/sensanders	huggingtweets	2022-11-08T02:38:49Z	109	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-28T15:31:16Z	--- language: en thumbnail: http://www.huggingtweets.com/sensanders/1667875118330/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/794619281271033856/Fs0QQaH7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bernie Sanders</div> <div style="text-align: center; font-size: 14px;">@sensanders</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bernie Sanders. \| Data \| Bernie Sanders \| \| --- \| --- \| \| Tweets downloaded \| 3249 \| \| Retweets \| 276 \| \| Short tweets \| 4 \| \| Tweets kept \| 2969 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ghx24nl1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sensanders's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31jf3liz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31jf3liz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sensanders') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tomrb/bettercallbloom-3b	tomrb	2022-11-08T01:12:59Z	9	5	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-20T17:17:16Z	--- language: en license: mit --- # BetterCallBloom-3b Finetuned Bloom-3b model on the r/legaladvice subreddit from pileoflaw ## Model description BLOOM-3B is a 3,002,557,440 parameters model pretrained by the BigScience initiative. ## Intended uses & limitations ### How to use ### Limitations and bias ## Training data ## Training procedure ### Preprocessing ## Evaluation results ### BibTeX entry and citation info
kit-nlp/bert-base-japanese-basic-char-v2-irony	kit-nlp	2022-11-08T00:10:26Z	105	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T07:33:23Z	--- language: ja license: cc-by-sa-4.0 --- # bert-base-irony This is a BERT Base model for the Japanese language finetuned for automatic irony detection. The model was based on [BERT base Japanese](https://huggingface.co/hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony, title={北見工業大学テキスト情報処理研究室 BERT Base 皮肉検出モデル (RIT ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-basic-char-v2-irony" } ```
yongauh/distilbert-base-uncased-finetuned-emotion	yongauh	2022-11-07T23:48:42Z	5	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T23:35:23Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.921 - name: F1 type: f1 value: 0.9211554013340549 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.921 - F1: 0.9212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8473 \| 1.0 \| 250 \| 0.3167 \| 0.908 \| 0.9061 \| \| 0.2561 \| 2.0 \| 500 \| 0.2208 \| 0.921 \| 0.9212 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.11.0
Devarshi/Brain_Tumor_Detector_swin	Devarshi	2022-11-07T22:28:14Z	50	4	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-07T06:37:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - f1 - recall - precision model-index: - name: Brain_Tumor_Detector_swin results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9981308411214953 - name: F1 type: f1 value: 0.9985111662531018 - name: Recall type: recall value: 0.9990069513406157 - name: Precision type: precision value: 0.998015873015873 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Brain_Tumor_Detector_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0054 - Accuracy: 0.9981 - F1: 0.9985 - Recall: 0.9990 - Precision: 0.9980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Recall \| Precision \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:------:\|:---------:\| \| 0.079 \| 1.0 \| 113 \| 0.0283 \| 0.9882 \| 0.9906 \| 0.9930 \| 0.9881 \| \| 0.0575 \| 2.0 \| 226 \| 0.0121 \| 0.9956 \| 0.9965 \| 0.9950 \| 0.9980 \| \| 0.0312 \| 3.0 \| 339 \| 0.0054 \| 0.9981 \| 0.9985 \| 0.9990 \| 0.9980 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
AlekseyKorshuk/amazon-reviews-input-output-6.7b-best	AlekseyKorshuk	2022-11-07T22:14:21Z	7	1	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/amazon-reviews-input-output", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T21:47:46Z	--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/amazon-reviews-input-output metrics: - accuracy model-index: - name: amazon-reviews-input-output-6.7b-best results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/amazon-reviews-input-output type: AlekseyKorshuk/amazon-reviews-input-output metrics: - name: Accuracy type: accuracy value: 0.040325203252032524 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amazon-reviews-input-output-6.7b-best This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set: - Loss: 2.6953 - Accuracy: 0.0403 - Samples: 100 - Perplexity: 14.8101 - Table: <wandb.data_types.Table object at 0x7fc684448b50> ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.9912 \| 0.06 \| 1 \| 2.7441 \| 0.0404 \| \| 2.9329 \| 0.12 \| 2 \| 2.7441 \| 0.0404 \| \| 2.9138 \| 0.19 \| 3 \| 2.8262 \| 0.0389 \| \| 2.9395 \| 0.25 \| 4 \| 2.8262 \| 0.0389 \| \| 2.9109 \| 0.31 \| 5 \| 2.7949 \| 0.0399 \| \| 2.8391 \| 0.38 \| 6 \| 2.7461 \| 0.0403 \| \| 2.9368 \| 0.44 \| 7 \| 2.7207 \| 0.0398 \| \| 2.7583 \| 0.5 \| 8 \| 2.7070 \| 0.0403 \| \| 2.9756 \| 0.56 \| 9 \| 2.6836 \| 0.0408 \| \| 2.8442 \| 0.62 \| 10 \| 2.6738 \| 0.0403 \| \| 2.7312 \| 0.69 \| 11 \| 2.6680 \| 0.0405 \| \| 2.7439 \| 0.75 \| 12 \| 2.6699 \| 0.0404 \| \| 2.9075 \| 0.81 \| 13 \| 2.6797 \| 0.0403 \| \| 2.8518 \| 0.88 \| 14 \| 2.6797 \| 0.0403 \| \| 2.8579 \| 0.94 \| 15 \| 2.6777 \| 0.0404 \| \| 2.8916 \| 1.0 \| 16 \| 2.6953 \| 0.0403 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
bishalbaaniya/bishalbaaniya-finetuned-myaamia-to-english	bishalbaaniya	2022-11-07T21:15:33Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-27T03:24:09Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: bishalbaaniya-finetuned-myaamia-to-english results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bishalbaaniya-finetuned-myaamia-to-english This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0090 - Bleu: 0.1637 - Gen Len: 7.977 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 4.1712 \| 1.0 \| 1082 \| 4.0090 \| 0.1637 \| 7.977 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
ilevs/distilrubert-tiny-cased-conversational-finetuned	ilevs	2022-11-07T21:06:23Z	109	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-06T14:54:50Z	--- tags: - generated_from_trainer model-index: - name: distilrubert-tiny-cased-conversational-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilrubert-tiny-cased-conversational-finetuned This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
AlekseyKorshuk/amazon-reviews-input-output-1.3b	AlekseyKorshuk	2022-11-07T20:45:36Z	5	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/amazon-reviews-input-output", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T20:26:17Z	--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/amazon-reviews-input-output metrics: - accuracy model-index: - name: amazon-reviews-input-output-1.3b results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/amazon-reviews-input-output type: AlekseyKorshuk/amazon-reviews-input-output metrics: - name: Accuracy type: accuracy value: 0.03550813008130081 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amazon-reviews-input-output-1.3b This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set: - Loss: 3.5488 - Accuracy: 0.0355 - Samples: 100 - Perplexity: 34.7725 - Table: <wandb.data_types.Table object at 0x7ffa3c3fd700> ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.2024 \| 0.06 \| 1 \| 2.9121 \| 0.0385 \| \| 3.1226 \| 0.12 \| 2 \| 2.9121 \| 0.0385 \| \| 3.1321 \| 0.19 \| 3 \| 2.8477 \| 0.0394 \| \| 2.9875 \| 0.25 \| 4 \| 2.8477 \| 0.0394 \| \| 2.9717 \| 0.31 \| 5 \| 2.8555 \| 0.0391 \| \| 2.9341 \| 0.38 \| 6 \| 2.8438 \| 0.0392 \| \| 3.0376 \| 0.44 \| 7 \| 2.8184 \| 0.0396 \| \| 2.8164 \| 0.5 \| 8 \| 2.7988 \| 0.0395 \| \| 3.0857 \| 0.56 \| 9 \| 2.7988 \| 0.0394 \| \| 2.9492 \| 0.62 \| 10 \| 2.7969 \| 0.0395 \| \| 2.8633 \| 0.69 \| 11 \| 2.7969 \| 0.0395 \| \| 2.8994 \| 0.75 \| 12 \| 2.7910 \| 0.0398 \| \| 3.0024 \| 0.81 \| 13 \| 2.7812 \| 0.0401 \| \| 2.937 \| 0.88 \| 14 \| 2.7812 \| 0.0399 \| \| 2.9963 \| 0.94 \| 15 \| 2.7812 \| 0.0399 \| \| 3.0168 \| 1.0 \| 16 \| 2.7754 \| 0.04 \| \| 2.2589 \| 1.06 \| 17 \| 2.7715 \| 0.0397 \| \| 2.2568 \| 1.12 \| 18 \| 2.7793 \| 0.0395 \| \| 2.3138 \| 1.19 \| 19 \| 2.8027 \| 0.0393 \| \| 2.2759 \| 1.25 \| 20 \| 2.8184 \| 0.0393 \| \| 2.5137 \| 1.31 \| 21 \| 2.8262 \| 0.0390 \| \| 2.2997 \| 1.38 \| 22 \| 2.8320 \| 0.0388 \| \| 2.2693 \| 1.44 \| 23 \| 2.8359 \| 0.0392 \| \| 2.204 \| 1.5 \| 24 \| 2.8379 \| 0.0387 \| \| 2.3713 \| 1.56 \| 25 \| 2.8359 \| 0.0391 \| \| 2.3448 \| 1.62 \| 26 \| 2.8340 \| 0.0391 \| \| 2.217 \| 1.69 \| 27 \| 2.8359 \| 0.0391 \| \| 2.3082 \| 1.75 \| 28 \| 2.8379 \| 0.0385 \| \| 2.2878 \| 1.81 \| 29 \| 2.8379 \| 0.0386 \| \| 2.2429 \| 1.88 \| 30 \| 2.8379 \| 0.0385 \| \| 2.2838 \| 1.94 \| 31 \| 2.8359 \| 0.0385 \| \| 2.4038 \| 2.0 \| 32 \| 2.8379 \| 0.0387 \| \| 1.8481 \| 2.06 \| 33 \| 2.8555 \| 0.0384 \| \| 1.657 \| 2.12 \| 34 \| 2.8965 \| 0.0382 \| \| 1.6996 \| 2.19 \| 35 \| 2.9590 \| 0.0380 \| \| 1.6741 \| 2.25 \| 36 \| 3.0312 \| 0.0379 \| \| 1.594 \| 2.31 \| 37 \| 3.0410 \| 0.0380 \| \| 1.5201 \| 2.38 \| 38 \| 3.0156 \| 0.0381 \| \| 1.5149 \| 2.44 \| 39 \| 3.0137 \| 0.0380 \| \| 1.5521 \| 2.5 \| 40 \| 3.0176 \| 0.0379 \| \| 1.5364 \| 2.56 \| 41 \| 3.0273 \| 0.0378 \| \| 1.5385 \| 2.62 \| 42 \| 3.0391 \| 0.0380 \| \| 1.4794 \| 2.69 \| 43 \| 3.0488 \| 0.0380 \| \| 1.4313 \| 2.75 \| 44 \| 3.0527 \| 0.0378 \| \| 1.5071 \| 2.81 \| 45 \| 3.0469 \| 0.0378 \| \| 1.4799 \| 2.88 \| 46 \| 3.0449 \| 0.0378 \| \| 1.521 \| 2.94 \| 47 \| 3.0371 \| 0.0380 \| \| 1.4603 \| 3.0 \| 48 \| 3.0410 \| 0.0379 \| \| 1.25 \| 3.06 \| 49 \| 3.0859 \| 0.0381 \| \| 1.0411 \| 3.12 \| 50 \| 3.1797 \| 0.0375 \| \| 1.0385 \| 3.19 \| 51 \| 3.2969 \| 0.0371 \| \| 1.0254 \| 3.25 \| 52 \| 3.3613 \| 0.0367 \| \| 0.9656 \| 3.31 \| 53 \| 3.3633 \| 0.0368 \| \| 1.036 \| 3.38 \| 54 \| 3.3359 \| 0.0366 \| \| 0.9366 \| 3.44 \| 55 \| 3.2949 \| 0.0366 \| \| 0.9712 \| 3.5 \| 56 \| 3.2695 \| 0.0367 \| \| 1.0066 \| 3.56 \| 57 \| 3.2676 \| 0.0366 \| \| 0.9952 \| 3.62 \| 58 \| 3.2773 \| 0.0368 \| \| 1.0352 \| 3.69 \| 59 \| 3.2891 \| 0.0367 \| \| 1.0212 \| 3.75 \| 60 \| 3.3164 \| 0.0362 \| \| 0.9468 \| 3.81 \| 61 \| 3.3203 \| 0.0360 \| \| 0.9155 \| 3.88 \| 62 \| 3.3223 \| 0.0366 \| \| 0.8552 \| 3.94 \| 63 \| 3.3262 \| 0.0370 \| \| 0.9575 \| 4.0 \| 64 \| 3.3340 \| 0.0370 \| \| 0.6384 \| 4.06 \| 65 \| 3.375 \| 0.0370 \| \| 0.6436 \| 4.12 \| 66 \| 3.4453 \| 0.0364 \| \| 0.5752 \| 4.19 \| 67 \| 3.5391 \| 0.0358 \| \| 0.6542 \| 4.25 \| 68 \| 3.6016 \| 0.0354 \| \| 0.6724 \| 4.31 \| 69 \| 3.6016 \| 0.0354 \| \| 0.591 \| 4.38 \| 70 \| 3.5938 \| 0.0359 \| \| 0.5346 \| 4.44 \| 71 \| 3.5801 \| 0.0361 \| \| 0.5112 \| 4.5 \| 72 \| 3.5762 \| 0.0361 \| \| 0.5443 \| 4.56 \| 73 \| 3.5840 \| 0.0362 \| \| 0.5689 \| 4.62 \| 74 \| 3.6152 \| 0.0358 \| \| 0.5667 \| 4.69 \| 75 \| 3.6328 \| 0.0358 \| \| 0.554 \| 4.75 \| 76 \| 3.6348 \| 0.0357 \| \| 0.6087 \| 4.81 \| 77 \| 3.625 \| 0.0355 \| \| 0.5236 \| 4.88 \| 78 \| 3.6152 \| 0.0355 \| \| 0.5458 \| 4.94 \| 79 \| 3.5781 \| 0.0355 \| \| 0.5702 \| 5.0 \| 80 \| 3.5488 \| 0.0355 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
okho0653/distilbert-base-zero-shot	okho0653	2022-11-07T20:44:16Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T20:40:31Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-zero-shot results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-zero-shot This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7147 - eval_accuracy: 0.0741 - eval_f1: 0.1379 - eval_runtime: 1.1794 - eval_samples_per_second: 22.894 - eval_steps_per_second: 1.696 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
Meow412/finetuning-sentiment-model-A3	Meow412	2022-11-07T20:39:27Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T20:30:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-A3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-A3 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: 0.3212 - Accuracy: 0.8760 - F1: 0.3516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
edbeeching/atari_zaxxon_3333	edbeeching	2022-11-07T20:31:59Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:30:50Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_zaxxon type: atari_zaxxon metrics: - type: mean_reward value: 12600.00 +/- 0.00 name: mean_reward verified: false --- A(n) APPO model trained on the atari_zaxxon environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_videopinball_3333	edbeeching	2022-11-07T20:27:56Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:26:42Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_videopinball type: atari_videopinball metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_videopinball environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_venture_3333	edbeeching	2022-11-07T20:26:22Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:25:21Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_venture type: atari_venture metrics: - type: mean_reward value: 1650.00 +/- 250.00 name: mean_reward verified: false --- A(n) APPO model trained on the atari_venture environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_upndown_3333	edbeeching	2022-11-07T20:25:01Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:23:31Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_upndown type: atari_upndown metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_upndown environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_tutankham_3333	edbeeching	2022-11-07T20:23:10Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:22:04Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tutankham type: atari_tutankham metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_tutankham environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_timepilot_3333	edbeeching	2022-11-07T20:21:45Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:20:54Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_timepilot type: atari_timepilot metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_timepilot environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_tennis_3333	edbeeching	2022-11-07T20:20:35Z	3	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:19:22Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tennis type: atari_tennis metrics: - type: mean_reward value: 24.00 +/- 0.00 name: mean_reward verified: false --- A(n) APPO model trained on the atari_tennis environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_spaceinvaders_3333	edbeeching	2022-11-07T20:17:41Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:16:43Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_spaceinvaders type: atari_spaceinvaders metrics: - type: mean_reward value: 2212.50 +/- 2.50 name: mean_reward verified: false --- A(n) APPO model trained on the atari_spaceinvaders environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_skiing_3333	edbeeching	2022-11-07T20:14:49Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:13:46Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_skiing type: atari_skiing metrics: - type: mean_reward value: -10184.29 +/- 133.20 name: mean_reward verified: false --- A(n) APPO model trained on the atari_skiing environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_seaquest_3333	edbeeching	2022-11-07T20:13:26Z	3	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:12:29Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_seaquest type: atari_seaquest metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_seaquest environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_roadrunner_3333	edbeeching	2022-11-07T20:10:27Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:09:13Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_roadrunner type: atari_roadrunner metrics: - type: mean_reward value: 84000.00 +/- 0.00 name: mean_reward verified: false --- A(n) APPO model trained on the atari_roadrunner environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
Ananjas/AwooAI	Ananjas	2022-11-07T20:08:29Z	105	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T19:37:57Z	--- tags: - conversational ---
edbeeching/atari_qbert_3333	edbeeching	2022-11-07T20:07:22Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-11-07T20:06:16Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_qbert type: atari_qbert metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) APPO model trained on the atari_qbert environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
artemnech/dialoT5-base	artemnech	2022-11-07T18:58:36Z	7	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-08-29T10:37:48Z	How to use: ``` from collections import deque from bs4 import BeautifulSoup import requests from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer import torch model_name = 'artemnech/dialoT5-base' model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) def generate(text, kwargs): model.eval() inputs = tokenizer(text, return_tensors='pt').to(model.device) with torch.no_grad(): hypotheses = model.generate(inputs, **kwargs) return tokenizer.decode(hypotheses[0], skip_special_tokens=True) def dialog(context): keyword = generate('keyword: ' + ' '.join(context), num_beams=2,) knowlege = '' if keyword != 'no_keywords': resp = requests.get(f"https://en.wikipedia.org/wiki/{keyword}") root = BeautifulSoup(resp.content, "html.parser") knowlege ="knowlege: " + " ".join([_.text.strip() for _ in root.find("div", class_="mw-body-content mw-content-ltr").find_all("p", limit=2)]) answ = generate(f'dialog: ' + knowlege + ' '.join(context), num_beams=3, do_sample=True, temperature=1.1, encoder_no_repeat_ngram_size=5, no_repeat_ngram_size=5, max_new_tokens = 30) return answ context =deque([], maxlen=4) while True: text = input() text = 'user1>>: ' + text context.append(text) answ = dialog(context) context.append('user2>>: ' + answ) print('bot: ', answ) ```
azuresonance/bert-finetuned-ner	azuresonance	2022-11-07T18:08:45Z	15	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T17:58:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9351422898742554 - name: Recall type: recall value: 0.9511948838774823 - name: F1 type: f1 value: 0.943100283664275 - name: Accuracy type: accuracy value: 0.9867251427562254 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9351 - Recall: 0.9512 - F1: 0.9431 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0861 \| 1.0 \| 1756 \| 0.0691 \| 0.9094 \| 0.9322 \| 0.9206 \| 0.9809 \| \| 0.034 \| 2.0 \| 3512 \| 0.0605 \| 0.9303 \| 0.9482 \| 0.9392 \| 0.9861 \| \| 0.0162 \| 3.0 \| 5268 \| 0.0604 \| 0.9351 \| 0.9512 \| 0.9431 \| 0.9867 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
GuiGel/xlm-roberta-large-flert-finetune-meddocan	GuiGel	2022-11-07T17:36:11Z	3	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]	token-classification	2022-11-07T17:32:35Z	--- tags: - flair - token-classification - sequence-tagger-model --- ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("GuiGel/xlm-roberta-large-flert-finetune-meddocan") # make example sentence sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ```
mqymmayy/mt5-small-finetuned-amazon-en-es	mqymmayy	2022-11-07T16:44:48Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-11-07T14:21:59Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0294 - Rouge1: 16.5993 - Rouge2: 8.0138 - Rougel: 16.1315 - Rougelsum: 16.2931 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 6.5928 \| 1.0 \| 1209 \| 3.3005 \| 14.7775 \| 6.4604 \| 14.2574 \| 14.3422 \| \| 3.9024 \| 2.0 \| 2418 \| 3.1399 \| 16.8632 \| 8.6474 \| 16.065 \| 16.2114 \| \| 3.5806 \| 3.0 \| 3627 \| 3.0869 \| 18.2422 \| 9.2647 \| 17.6227 \| 17.7649 \| \| 3.4201 \| 4.0 \| 4836 \| 3.0590 \| 17.7826 \| 8.9742 \| 16.9951 \| 17.1804 \| \| 3.3202 \| 5.0 \| 6045 \| 3.0598 \| 17.7808 \| 8.6038 \| 17.2243 \| 17.4125 \| \| 3.2436 \| 6.0 \| 7254 \| 3.0409 \| 16.8469 \| 8.2339 \| 16.3935 \| 16.5818 \| \| 3.2079 \| 7.0 \| 8463 \| 3.0332 \| 16.8148 \| 8.2115 \| 16.3166 \| 16.4832 \| \| 3.1801 \| 8.0 \| 9672 \| 3.0294 \| 16.5993 \| 8.0138 \| 16.1315 \| 16.2931 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
zhiguoxu/chinese-macbert-base-finetuned-ner	zhiguoxu	2022-11-07T15:45:01Z	13	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-23T12:03:17Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: chinese-macbert-base-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # chinese-macbert-base-finetuned-ner This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2420 - F1: 0.9224 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 57 - eval_batch_size: 57 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.6141 \| 1.0 \| 1 \| 2.6454 \| 0.0 \| \| 2.7076 \| 2.0 \| 2 \| 2.0034 \| 0.0 \| \| 2.0979 \| 3.0 \| 3 \| 1.6276 \| 0.0 \| \| 1.7264 \| 4.0 \| 4 \| 1.3419 \| 0.3522 \| \| 1.4691 \| 5.0 \| 5 \| 1.1239 \| 0.4091 \| \| 1.2504 \| 6.0 \| 6 \| 0.9532 \| 0.5514 \| \| 1.0798 \| 7.0 \| 7 \| 0.8129 \| 0.5895 \| \| 0.9279 \| 8.0 \| 8 \| 0.6987 \| 0.625 \| \| 0.8179 \| 9.0 \| 9 \| 0.6081 \| 0.6392 \| \| 0.7202 \| 10.0 \| 10 \| 0.5346 \| 0.6667 \| \| 0.6377 \| 11.0 \| 11 \| 0.4731 \| 0.7451 \| \| 0.5751 \| 12.0 \| 12 \| 0.4226 \| 0.7925 \| \| 0.5202 \| 13.0 \| 13 \| 0.3804 \| 0.7685 \| \| 0.4733 \| 14.0 \| 14 \| 0.3447 \| 0.7928 \| \| 0.44 \| 15.0 \| 15 \| 0.3145 \| 0.8509 \| \| 0.4047 \| 16.0 \| 16 \| 0.2899 \| 0.8918 \| \| 0.3773 \| 17.0 \| 17 \| 0.2707 \| 0.8966 \| \| 0.353 \| 18.0 \| 18 \| 0.2563 \| 0.9052 \| \| 0.3413 \| 19.0 \| 19 \| 0.2468 \| 0.9224 \| \| 0.3314 \| 20.0 \| 20 \| 0.2420 \| 0.9224 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0+cu102 - Datasets 1.18.4 - Tokenizers 0.12.1
Rundstedtz/distilbert-base-uncased-letters-from-jenny	Rundstedtz	2022-11-07T15:35:42Z	5	1	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-07T15:27:32Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Rundstedtz/distilbert-base-uncased-letters-from-jenny results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Rundstedtz/distilbert-base-uncased-letters-from-jenny This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5319 - Validation Loss: 2.9614 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -988, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5319 \| 2.9614 \| 0 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.2
kevinbror/Svenskmodell	kevinbror	2022-11-07T15:20:39Z	62	0	transformers	[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]	question-answering	2022-11-07T15:20:15Z	--- tags: - generated_from_keras_callback model-index: - name: Svenskmodell results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Svenskmodell This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3411 - Train End Logits Accuracy: 0.8882 - Train Start Logits Accuracy: 0.8838 - Validation Loss: 1.4988 - Validation End Logits Accuracy: 0.6713 - Validation Start Logits Accuracy: 0.6669 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 28792, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 1.2392 \| 0.6241 \| 0.6197 \| 1.0970 \| 0.6588 \| 0.6543 \| 0 \| \| 0.7549 \| 0.7615 \| 0.7569 \| 1.1185 \| 0.6673 \| 0.6665 \| 1 \| \| 0.5005 \| 0.8376 \| 0.8323 \| 1.3299 \| 0.6571 \| 0.6503 \| 2 \| \| 0.3411 \| 0.8882 \| 0.8838 \| 1.4988 \| 0.6713 \| 0.6669 \| 3 \| ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1
jasonsheih/bert-base-uncased-finetuned-vr-comfort-2125	jasonsheih	2022-11-07T13:58:05Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-05T13:26:15Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-base-uncased-finetuned-vr-comfort-2125 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-vr-comfort-2125 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0440 - Accuracy: 0.8431 - F1: 0.8437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7159 \| 1.0 \| 157 \| 0.6408 \| 0.7401 \| 0.6612 \| \| 0.5559 \| 2.0 \| 314 \| 0.5362 \| 0.7952 \| 0.7684 \| \| 0.362 \| 3.0 \| 471 \| 0.5135 \| 0.8204 \| 0.8132 \| \| 0.1918 \| 4.0 \| 628 \| 0.6109 \| 0.8407 \| 0.8388 \| \| 0.1192 \| 5.0 \| 785 \| 0.6947 \| 0.8347 \| 0.8316 \| \| 0.0661 \| 6.0 \| 942 \| 0.7843 \| 0.8455 \| 0.8467 \| \| 0.0507 \| 7.0 \| 1099 \| 0.9312 \| 0.8168 \| 0.8271 \| \| 0.0406 \| 8.0 \| 1256 \| 0.8616 \| 0.8467 \| 0.8488 \| \| 0.0268 \| 9.0 \| 1413 \| 0.8403 \| 0.8443 \| 0.8478 \| \| 0.0251 \| 10.0 \| 1570 \| 0.8662 \| 0.8467 \| 0.8472 \| \| 0.0188 \| 11.0 \| 1727 \| 0.9418 \| 0.8563 \| 0.8530 \| \| 0.0195 \| 12.0 \| 1884 \| 0.9541 \| 0.8479 \| 0.8469 \| \| 0.0172 \| 13.0 \| 2041 \| 0.9372 \| 0.8407 \| 0.8413 \| \| 0.0142 \| 14.0 \| 2198 \| 0.9883 \| 0.8491 \| 0.8469 \| \| 0.0156 \| 15.0 \| 2355 \| 1.0150 \| 0.8419 \| 0.8428 \| \| 0.0138 \| 16.0 \| 2512 \| 1.0035 \| 0.8479 \| 0.8466 \| \| 0.013 \| 17.0 \| 2669 \| 1.0909 \| 0.8299 \| 0.8355 \| \| 0.0115 \| 18.0 \| 2826 \| 1.0278 \| 0.8515 \| 0.8490 \| \| 0.0107 \| 19.0 \| 2983 \| 1.0419 \| 0.8431 \| 0.8437 \| \| 0.0101 \| 20.0 \| 3140 \| 1.0440 \| 0.8431 \| 0.8437 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
t-bank-ai/ruDialoGPT-medium	t-bank-ai	2022-11-07T13:34:43Z	1,169	35	transformers	[ "transformers", "pytorch", "gpt2", "conversational", "ru", "arxiv:2001.09977", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-07-12T14:52:19Z	--- license: mit widget: - text: "@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@" example_title: "how r u" - text: "@@ПЕРВЫЙ@@ что ты делал на выходных? @@ВТОРОЙ@@" example_title: "wyd" language: - ru tags: - conversational --- This generation model is based on [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents The model was trained with context size 3 On a private validation set we calculated metrics introduced in [this paper](https://arxiv.org/pdf/2001.09977.pdf): - Sensibleness: Crowdsourcers were asked whether model's response makes sense given the context - Specificity: Crowdsourcers were asked whether model's response is specific for given context, in other words we don't want our model to give general and boring responses - SSA which is the average of two metrics above (Sensibleness Specificity Average) \| \| sensibleness \| specificity \| SSA \| \|:----------------------------------------------------\|---------------:\|--------------:\|------:\| \| [tinkoff-ai/ruDialoGPT-small](https://huggingface.co/tinkoff-ai/ruDialoGPT-small) \| 0.64 \| 0.5 \| 0.57 \| \| [tinkoff-ai/ruDialoGPT-medium](https://huggingface.co/tinkoff-ai/ruDialoGPT-medium) \| 0.78 \| 0.69 \| 0.735 \| How to use: ```python import torch from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-medium') model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium') inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt') generated_token_ids = model.generate( **inputs, top_k=10, top_p=0.95, num_beams=3, num_return_sequences=3, do_sample=True, no_repeat_ngram_size=2, temperature=1.2, repetition_penalty=1.2, length_penalty=1.0, eos_token_id=50257, max_new_tokens=40 ) context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids] context_with_response ```
FacVain/turkish-sentiment-XMLRoBERTa	FacVain	2022-11-07T11:19:48Z	0	0	null	[ "tr", "region:us" ]	null	2022-11-07T09:57:24Z	--- language: tr tag: text-classification widget: - text: "Oldukça kullanışlı bir ürün." --- This repository contains two models that has been finetuned on twitter-XMLRoBERTa https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base. 3_Label model can classify text as positive, neutral and negative. 2_Label_Twitter is finetuned with tweets and can predict tweets as positive and negative.
tatakof/testpyramidsrnd	tatakof	2022-11-07T11:00:36Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-11-07T11:00:28Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: franfram/testpyramidsrnd 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ronanki/xlm-r-multilingual-v1-2022-11-07	ronanki	2022-11-07T10:50:15Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-11-07T10:49:57Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/xlm-r-multilingual-v1-2022-11-07 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## 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('ronanki/xlm-r-multilingual-v1-2022-11-07') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ronanki/xlm-r-multilingual-v1-2022-11-07') model = AutoModel.from_pretrained('ronanki/xlm-r-multilingual-v1-2022-11-07') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/xlm-r-multilingual-v1-2022-11-07) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 348 with parameters: ``` {'batch_size': 64} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1044, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
silveto/distilbert-base-uncased-finetuned-squad	silveto	2022-11-07T10:44:30Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-11-02T17:43:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: 1.1531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2297 \| 1.0 \| 5533 \| 1.1547 \| \| 0.9688 \| 2.0 \| 11066 \| 1.1278 \| \| 0.763 \| 3.0 \| 16599 \| 1.1531 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.10.3
Shyam-311/distilroberta-base-finetuned-wikitext2	Shyam-311	2022-11-07T10:34:57Z	164	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-07T10:01:02Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0843 \| 1.0 \| 2406 \| 1.9226 \| \| 1.9913 \| 2.0 \| 4812 \| 1.8820 \| \| 1.9597 \| 3.0 \| 7218 \| 1.8214 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
nguyenkhoa2407/bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07	nguyenkhoa2407	2022-11-07T10:30:30Z	9	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:favsbot", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T10:23:28Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - favsbot metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07 results: - task: name: Token Classification type: token-classification dataset: name: favsbot type: favsbot config: default split: train args: default metrics: - name: Precision type: precision value: 0.8275862068965517 - name: Recall type: recall value: 0.96 - name: F1 type: f1 value: 0.888888888888889 - name: Accuracy type: accuracy value: 0.9444444444444444 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1169 - Precision: 0.8276 - Recall: 0.96 - F1: 0.8889 - Accuracy: 0.9444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 1.6302 \| 0.0 \| 0.0 \| 0.0 \| 0.5972 \| \| No log \| 2.0 \| 20 \| 1.0453 \| 0.6667 \| 0.08 \| 0.1429 \| 0.6389 \| \| No log \| 3.0 \| 30 \| 0.7286 \| 0.8421 \| 0.64 \| 0.7273 \| 0.8472 \| \| No log \| 4.0 \| 40 \| 0.5296 \| 0.8 \| 0.8 \| 0.8000 \| 0.8889 \| \| No log \| 5.0 \| 50 \| 0.3960 \| 0.8214 \| 0.92 \| 0.8679 \| 0.9306 \| \| No log \| 6.0 \| 60 \| 0.2987 \| 0.8214 \| 0.92 \| 0.8679 \| 0.9306 \| \| No log \| 7.0 \| 70 \| 0.2424 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 8.0 \| 80 \| 0.2151 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 9.0 \| 90 \| 0.1815 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 10.0 \| 100 \| 0.1675 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 11.0 \| 110 \| 0.1504 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 12.0 \| 120 \| 0.1410 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 13.0 \| 130 \| 0.1350 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 14.0 \| 140 \| 0.1281 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 15.0 \| 150 \| 0.1239 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 16.0 \| 160 \| 0.1190 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 17.0 \| 170 \| 0.1187 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 18.0 \| 180 \| 0.1180 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 19.0 \| 190 \| 0.1170 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| \| No log \| 20.0 \| 200 \| 0.1169 \| 0.8276 \| 0.96 \| 0.8889 \| 0.9444 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
julien-c/pokemon-predict-hp	julien-c	2022-11-07T10:11:46Z	0	1	mlconsole	[ "mlconsole", "tabular-regression", "dataset:julien-c/kaggle-rounakbanik-pokemon", "license:apache-2.0", "model-index", "region:us" ]	tabular-regression	2022-10-10T08:28:52Z	--- license: apache-2.0 tags: - mlconsole - tabular-regression library_name: mlconsole inference: false datasets: - julien-c/kaggle-rounakbanik-pokemon metrics: - mae - loss model-index: - name: pokemon-predict-hp results: - task: type: tabular-regression name: tabular-regression dataset: type: julien-c/kaggle-rounakbanik-pokemon name: pokemon.csv metrics: - type: mae name: Mean absolute error value: 15.908513069152832 - type: loss name: Model loss value: 647.6045532226562 --- # pokemon.csv (#0) Trained on [ML Console](https://mlconsole.com) on the [julien-c/kaggle-rounakbanik-pokemon](https://huggingface.co/datasets/julien-c/kaggle-rounakbanik-pokemon). [Load the model on ML Console](https://mlconsole.com/model/hf/julien-c/pokemon-predict-hp). ### Screenshots of training ![](screenshots/training-curve.png) ![](screenshots/predict.png)
Shyam-311/distilgpt2-finetuned-wikitext2	Shyam-311	2022-11-07T09:55:19Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T09:08:03Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7602 \| 1.0 \| 2334 \| 3.6669 \| \| 3.653 \| 2.0 \| 4668 \| 3.6472 \| \| 3.6006 \| 3.0 \| 7002 \| 3.6421 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2
thisisHJLee/wav2vec2-large-xls-r-300m-korean-convsen3	thisisHJLee	2022-11-07T09:31:00Z	108	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-07T05:04:25Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-convsen3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-korean-convsen3 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: 0.0089 - Cer: 0.0010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5769 \| 1.0 \| 1762 \| 0.0593 \| 0.0124 \| \| 0.0927 \| 2.0 \| 3524 \| 0.0106 \| 0.0014 \| \| 0.0571 \| 3.0 \| 5286 \| 0.0089 \| 0.0010 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.11.0
pig4431/Sentiment140_BERT_5E	pig4431	2022-11-07T08:46:38Z	10	1	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:sentiment140", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T08:39:06Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - sentiment140 metrics: - accuracy model-index: - name: Sentiment140_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: sentiment140 type: sentiment140 config: sentiment140 split: train args: sentiment140 metrics: - name: Accuracy type: accuracy value: 0.82 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Sentiment140_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.7061 - Accuracy: 0.82 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6882 \| 0.08 \| 50 \| 0.6047 \| 0.7 \| \| 0.6223 \| 0.16 \| 100 \| 0.5137 \| 0.8067 \| \| 0.5463 \| 0.24 \| 150 \| 0.4573 \| 0.8067 \| \| 0.4922 \| 0.32 \| 200 \| 0.4790 \| 0.8 \| \| 0.4821 \| 0.4 \| 250 \| 0.4207 \| 0.8267 \| \| 0.4985 \| 0.48 \| 300 \| 0.4267 \| 0.8067 \| \| 0.4455 \| 0.56 \| 350 \| 0.4301 \| 0.8133 \| \| 0.469 \| 0.64 \| 400 \| 0.4294 \| 0.82 \| \| 0.4906 \| 0.72 \| 450 \| 0.4059 \| 0.8067 \| \| 0.4006 \| 0.8 \| 500 \| 0.4181 \| 0.8133 \| \| 0.445 \| 0.88 \| 550 \| 0.3948 \| 0.8267 \| \| 0.4302 \| 0.96 \| 600 \| 0.3976 \| 0.84 \| \| 0.4442 \| 1.04 \| 650 \| 0.3887 \| 0.8533 \| \| 0.3424 \| 1.12 \| 700 \| 0.4119 \| 0.8267 \| \| 0.3589 \| 1.2 \| 750 \| 0.4083 \| 0.8533 \| \| 0.3737 \| 1.28 \| 800 \| 0.4253 \| 0.8333 \| \| 0.334 \| 1.36 \| 850 \| 0.4147 \| 0.86 \| \| 0.3637 \| 1.44 \| 900 \| 0.3926 \| 0.8533 \| \| 0.3388 \| 1.52 \| 950 \| 0.4084 \| 0.8267 \| \| 0.3375 \| 1.6 \| 1000 \| 0.4132 \| 0.8467 \| \| 0.3725 \| 1.68 \| 1050 \| 0.3965 \| 0.8467 \| \| 0.3649 \| 1.76 \| 1100 \| 0.3956 \| 0.8333 \| \| 0.3799 \| 1.84 \| 1150 \| 0.3923 \| 0.8333 \| \| 0.3695 \| 1.92 \| 1200 \| 0.4266 \| 0.84 \| \| 0.3233 \| 2.0 \| 1250 \| 0.4225 \| 0.8333 \| \| 0.2313 \| 2.08 \| 1300 \| 0.4672 \| 0.8333 \| \| 0.231 \| 2.16 \| 1350 \| 0.5212 \| 0.8133 \| \| 0.2526 \| 2.24 \| 1400 \| 0.5392 \| 0.8067 \| \| 0.2721 \| 2.32 \| 1450 \| 0.4895 \| 0.82 \| \| 0.2141 \| 2.4 \| 1500 \| 0.5258 \| 0.8133 \| \| 0.2658 \| 2.48 \| 1550 \| 0.5046 \| 0.8267 \| \| 0.2386 \| 2.56 \| 1600 \| 0.4873 \| 0.8267 \| \| 0.2493 \| 2.64 \| 1650 \| 0.4950 \| 0.8333 \| \| 0.2692 \| 2.72 \| 1700 \| 0.5080 \| 0.8267 \| \| 0.2226 \| 2.8 \| 1750 \| 0.5016 \| 0.8467 \| \| 0.2522 \| 2.88 \| 1800 \| 0.5068 \| 0.8267 \| \| 0.2556 \| 2.96 \| 1850 \| 0.4937 \| 0.8267 \| \| 0.2311 \| 3.04 \| 1900 \| 0.5103 \| 0.8267 \| \| 0.1703 \| 3.12 \| 1950 \| 0.5680 \| 0.82 \| \| 0.1744 \| 3.2 \| 2000 \| 0.5501 \| 0.82 \| \| 0.1667 \| 3.28 \| 2050 \| 0.6142 \| 0.82 \| \| 0.1863 \| 3.36 \| 2100 \| 0.6355 \| 0.82 \| \| 0.2543 \| 3.44 \| 2150 \| 0.6000 \| 0.8133 \| \| 0.1565 \| 3.52 \| 2200 \| 0.6618 \| 0.8267 \| \| 0.1531 \| 3.6 \| 2250 \| 0.6595 \| 0.8133 \| \| 0.1915 \| 3.68 \| 2300 \| 0.6647 \| 0.8267 \| \| 0.1601 \| 3.76 \| 2350 \| 0.6729 \| 0.8267 \| \| 0.176 \| 3.84 \| 2400 \| 0.6699 \| 0.82 \| \| 0.1815 \| 3.92 \| 2450 \| 0.6819 \| 0.8067 \| \| 0.1987 \| 4.0 \| 2500 \| 0.6543 \| 0.8333 \| \| 0.1236 \| 4.08 \| 2550 \| 0.6686 \| 0.8333 \| \| 0.1599 \| 4.16 \| 2600 \| 0.6583 \| 0.8267 \| \| 0.1256 \| 4.24 \| 2650 \| 0.6871 \| 0.8267 \| \| 0.1291 \| 4.32 \| 2700 \| 0.6855 \| 0.82 \| \| 0.1198 \| 4.4 \| 2750 \| 0.6901 \| 0.82 \| \| 0.1245 \| 4.48 \| 2800 \| 0.7152 \| 0.8267 \| \| 0.1784 \| 4.56 \| 2850 \| 0.7053 \| 0.82 \| \| 0.1705 \| 4.64 \| 2900 \| 0.7016 \| 0.82 \| \| 0.1265 \| 4.72 \| 2950 \| 0.7013 \| 0.82 \| \| 0.1192 \| 4.8 \| 3000 \| 0.7084 \| 0.82 \| \| 0.174 \| 4.88 \| 3050 \| 0.7062 \| 0.82 \| \| 0.1328 \| 4.96 \| 3100 \| 0.7061 \| 0.82 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
cynthiachan/finetuned-bert-base	cynthiachan	2022-11-07T07:56:55Z	12	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:cynthiachan/FeedRef2022", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T06:51:03Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cynthiachan/FeedRef2022 model-index: - name: training results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # training This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef2022 dataset. It achieves the following results on the evaluation set: - Loss: 0.0514 - Attackid Precision: 0.8889 - Attackid Recall: 0.9231 - Attackid F1: 0.9057 - Attackid Number: 52 - Bitcoinaddr Precision: 0.875 - Bitcoinaddr Recall: 1.0 - Bitcoinaddr F1: 0.9333 - Bitcoinaddr Number: 7 - Cve Precision: 0.8378 - Cve Recall: 0.9538 - Cve F1: 0.8921 - Cve Number: 65 - Defenderthreat Precision: 0.875 - Defenderthreat Recall: 1.0 - Defenderthreat F1: 0.9333 - Defenderthreat Number: 7 - Domain Precision: 0.9279 - Domain Recall: 0.9369 - Domain F1: 0.9324 - Domain Number: 206 - Email Precision: 0.8333 - Email Recall: 0.9302 - Email F1: 0.8791 - Email Number: 43 - Filepath Precision: 0.8857 - Filepath Recall: 0.9195 - Filepath F1: 0.9023 - Filepath Number: 1652 - Fingerprint Precision: 0.0 - Fingerprint Recall: 0.0 - Fingerprint F1: 0.0 - Fingerprint Number: 2 - Hostname Precision: 0.8910 - Hostname Recall: 0.9653 - Hostname F1: 0.9267 - Hostname Number: 144 - Ipv4 Precision: 0.9767 - Ipv4 Recall: 0.9825 - Ipv4 F1: 0.9796 - Ipv4 Number: 171 - Ipv6 Precision: 0.3333 - Ipv6 Recall: 1.0 - Ipv6 F1: 0.5 - Ipv6 Number: 3 - Md5 Precision: 0.9141 - Md5 Recall: 0.9857 - Md5 F1: 0.9486 - Md5 Number: 421 - Sha1 Precision: 0.8545 - Sha1 Recall: 0.9592 - Sha1 F1: 0.9038 - Sha1 Number: 49 - Sha256 Precision: 0.9120 - Sha256 Recall: 0.9919 - Sha256 F1: 0.9502 - Sha256 Number: 491 - Uri Precision: 0.3333 - Uri Recall: 0.4545 - Uri F1: 0.3846 - Uri Number: 11 - Overall Precision: 0.8946 - Overall Recall: 0.9446 - Overall F1: 0.9189 - Overall Accuracy: 0.9886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Attackid Precision \| Attackid Recall \| Attackid F1 \| Attackid Number \| Bitcoinaddr Precision \| Bitcoinaddr Recall \| Bitcoinaddr F1 \| Bitcoinaddr Number \| Cve Precision \| Cve Recall \| Cve F1 \| Cve Number \| Defenderthreat Precision \| Defenderthreat Recall \| Defenderthreat F1 \| Defenderthreat Number \| Domain Precision \| Domain Recall \| Domain F1 \| Domain Number \| Email Precision \| Email Recall \| Email F1 \| Email Number \| Filepath Precision \| Filepath Recall \| Filepath F1 \| Filepath Number \| Fingerprint Precision \| Fingerprint Recall \| Fingerprint F1 \| Fingerprint Number \| Hostname Precision \| Hostname Recall \| Hostname F1 \| Hostname Number \| Ipv4 Precision \| Ipv4 Recall \| Ipv4 F1 \| Ipv4 Number \| Ipv6 Precision \| Ipv6 Recall \| Ipv6 F1 \| Ipv6 Number \| Md5 Precision \| Md5 Recall \| Md5 F1 \| Md5 Number \| Sha1 Precision \| Sha1 Recall \| Sha1 F1 \| Sha1 Number \| Sha256 Precision \| Sha256 Recall \| Sha256 F1 \| Sha256 Number \| Uri Precision \| Uri Recall \| Uri F1 \| Uri Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| 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\| 0.3691 \| 0.04 \| 500 \| 0.3054 \| 0.0 \| 0.0 \| 0.0 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.0 \| 0.0 \| 0.0 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.0 \| 0.0 \| 0.0 \| 206 \| 0.0 \| 0.0 \| 0.0 \| 43 \| 0.1917 \| 0.5975 \| 0.2903 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.0 \| 0.0 \| 0.0 \| 144 \| 0.5747 \| 0.5848 \| 0.5797 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.4160 \| 0.7648 \| 0.5389 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.5131 \| 0.9145 \| 0.6574 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.2665 \| 0.5590 \| 0.3610 \| 0.9297 \| \| 0.2388 \| 0.07 \| 1000 \| 0.2124 \| 0.0 \| 0.0 \| 0.0 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7467 \| 0.8615 \| 0.8 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.0 \| 0.0 \| 0.0 \| 206 \| 0.0 \| 0.0 \| 0.0 \| 43 \| 0.3846 \| 0.4661 \| 0.4215 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.3534 \| 0.6528 \| 0.4585 \| 144 \| 0.6667 \| 0.5614 \| 0.6095 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.5275 \| 0.9097 \| 0.6678 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.8787 \| 0.9002 \| 0.8893 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.4932 \| 0.5539 \| 0.5218 \| 0.9491 \| \| 0.1817 \| 0.11 \| 1500 \| 0.2025 \| 0.4433 \| 0.8269 \| 0.5772 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7941 \| 0.8308 \| 0.8120 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.2241 \| 0.6602 \| 0.3346 \| 206 \| 0.1538 \| 0.2326 \| 0.1852 \| 43 \| 0.4561 \| 0.6816 \| 0.5465 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.0042 \| 0.0069 \| 0.0052 \| 144 \| 0.6522 \| 0.7018 \| 0.6761 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.5671 \| 0.8527 \| 0.6812 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.7623 \| 0.9470 \| 0.8447 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.4654 \| 0.6961 \| 0.5579 \| 0.9563 \| \| 0.1552 \| 0.15 \| 2000 \| 0.1581 \| 0.6119 \| 0.7885 \| 0.6891 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8235 \| 0.8615 \| 0.8421 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.4979 \| 0.5680 \| 0.5306 \| 206 \| 0.4795 \| 0.8140 \| 0.6034 \| 43 \| 0.4876 \| 0.7960 \| 0.6047 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.5682 \| 0.6944 \| 0.625 \| 144 \| 0.4692 \| 0.8012 \| 0.5918 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.5321 \| 0.9240 \| 0.6753 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.7951 \| 0.9328 \| 0.8585 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.5345 \| 0.7966 \| 0.6398 \| 0.9622 \| \| 0.1567 \| 0.19 \| 2500 \| 0.1619 \| 0.6032 \| 0.7308 \| 0.6609 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8133 \| 0.9385 \| 0.8714 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6257 \| 0.5680 \| 0.5954 \| 206 \| 0.1379 \| 0.1860 \| 0.1584 \| 43 \| 0.5788 \| 0.7512 \| 0.6538 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.4981 \| 0.9097 \| 0.6437 \| 144 \| 0.7233 \| 0.8713 \| 0.7905 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7723 \| 0.9264 \| 0.8423 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.7523 \| 0.9837 \| 0.8526 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6308 \| 0.7876 \| 0.7006 \| 0.9628 \| \| 0.1588 \| 0.22 \| 3000 \| 0.1409 \| 0.4050 \| 0.9423 \| 0.5665 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.5962 \| 0.9538 \| 0.7337 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6805 \| 0.7961 \| 0.7338 \| 206 \| 0.5821 \| 0.9070 \| 0.7091 \| 43 \| 0.6291 \| 0.7712 \| 0.6930 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6902 \| 0.8819 \| 0.7744 \| 144 \| 0.5737 \| 0.8421 \| 0.6825 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.5678 \| 0.9454 \| 0.7094 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.8582 \| 0.9735 \| 0.9122 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6300 \| 0.8228 \| 0.7136 \| 0.9664 \| \| 0.1257 \| 0.26 \| 3500 \| 0.1417 \| 0.5541 \| 0.7885 \| 0.6508 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6854 \| 0.9385 \| 0.7922 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6828 \| 0.7524 \| 0.7159 \| 206 \| 0.5217 \| 0.8372 \| 0.6429 \| 43 \| 0.6314 \| 0.7155 \| 0.6708 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.5261 \| 0.9097 \| 0.6667 \| 144 \| 0.7562 \| 0.8889 \| 0.8172 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7435 \| 0.9501 \| 0.8342 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.7325 \| 0.9817 \| 0.8390 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6627 \| 0.7942 \| 0.7225 \| 0.9658 \| \| 0.1229 \| 0.3 \| 4000 \| 0.1455 \| 0.6567 \| 0.8462 \| 0.7395 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7391 \| 0.7846 \| 0.7612 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6858 \| 0.7524 \| 0.7176 \| 206 \| 0.4321 \| 0.8140 \| 0.5645 \| 43 \| 0.6740 \| 0.7809 \| 0.7235 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6452 \| 0.8333 \| 0.7273 \| 144 \| 0.5455 \| 0.5614 \| 0.5533 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7697 \| 0.8575 \| 0.8112 \| 421 \| 0.3645 \| 0.7959 \| 0.5 \| 49 \| 0.6948 \| 0.9735 \| 0.8109 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6684 \| 0.8029 \| 0.7295 \| 0.9667 \| \| 0.1323 \| 0.34 \| 4500 \| 0.1323 \| 0.6719 \| 0.8269 \| 0.7414 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7910 \| 0.8154 \| 0.8030 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6064 \| 0.7330 \| 0.6637 \| 206 \| 0.74 \| 0.8605 \| 0.7957 \| 43 \| 0.6802 \| 0.7391 \| 0.7084 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.5935 \| 0.5069 \| 0.5468 \| 144 \| 0.7826 \| 0.7368 \| 0.7590 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7783 \| 0.8171 \| 0.7972 \| 421 \| 0.3810 \| 0.8163 \| 0.5195 \| 49 \| 0.8368 \| 0.9715 \| 0.8992 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7049 \| 0.7717 \| 0.7368 \| 0.9680 \| \| 0.1379 \| 0.37 \| 5000 \| 0.1088 \| 0.5930 \| 0.9808 \| 0.7391 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.725 \| 0.8923 \| 0.8 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7619 \| 0.6990 \| 0.7291 \| 206 \| 0.5556 \| 0.9302 \| 0.6957 \| 43 \| 0.6551 \| 0.8360 \| 0.7346 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7127 \| 0.8958 \| 0.7938 \| 144 \| 0.7989 \| 0.8596 \| 0.8282 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7665 \| 0.9359 \| 0.8428 \| 421 \| 0.3729 \| 0.4490 \| 0.4074 \| 49 \| 0.7278 \| 0.9695 \| 0.8314 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6886 \| 0.8550 \| 0.7629 \| 0.9738 \| \| 0.1162 \| 0.41 \| 5500 \| 0.1205 \| 0.5765 \| 0.9423 \| 0.7153 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8026 \| 0.9385 \| 0.8652 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7960 \| 0.7767 \| 0.7862 \| 206 \| 0.6032 \| 0.8837 \| 0.7170 \| 43 \| 0.6724 \| 0.8099 \| 0.7348 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6791 \| 0.8819 \| 0.7674 \| 144 \| 0.8041 \| 0.9123 \| 0.8548 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7188 \| 0.9287 \| 0.8104 \| 421 \| 0.5714 \| 0.8163 \| 0.6723 \| 49 \| 0.8088 \| 0.9735 \| 0.8835 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7033 \| 0.8538 \| 0.7713 \| 0.9711 \| \| 0.1128 \| 0.45 \| 6000 \| 0.1165 \| 0.6575 \| 0.9231 \| 0.768 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7143 \| 0.9231 \| 0.8054 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7703 \| 0.7816 \| 0.7759 \| 206 \| 0.6724 \| 0.9070 \| 0.7723 \| 43 \| 0.6634 \| 0.7706 \| 0.7130 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6580 \| 0.8819 \| 0.7537 \| 144 \| 0.8434 \| 0.8187 \| 0.8309 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8032 \| 0.9596 \| 0.8745 \| 421 \| 0.6066 \| 0.7551 \| 0.6727 \| 49 \| 0.8554 \| 0.9756 \| 0.9115 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7201 \| 0.8327 \| 0.7723 \| 0.9736 \| \| 0.11 \| 0.49 \| 6500 \| 0.1374 \| 0.7167 \| 0.8269 \| 0.7679 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7273 \| 0.8615 \| 0.7887 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7592 \| 0.7039 \| 0.7305 \| 206 \| 0.725 \| 0.6744 \| 0.6988 \| 43 \| 0.6129 \| 0.7524 \| 0.6755 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7151 \| 0.8542 \| 0.7785 \| 144 \| 0.7919 \| 0.8012 \| 0.7965 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7821 \| 0.9549 \| 0.8599 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.6880 \| 0.9837 \| 0.8097 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.6710 \| 0.8005 \| 0.7300 \| 0.9680 \| \| 0.1152 \| 0.52 \| 7000 \| 0.1152 \| 0.6933 \| 1.0 \| 0.8189 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6374 \| 0.8923 \| 0.7436 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6103 \| 0.6311 \| 0.6205 \| 206 \| 0.6739 \| 0.7209 \| 0.6966 \| 43 \| 0.6969 \| 0.7960 \| 0.7431 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7721 \| 0.7292 \| 0.75 \| 144 \| 0.8526 \| 0.7778 \| 0.8135 \| 171 \| 0.0192 \| 0.3333 \| 0.0364 \| 3 \| 0.8549 \| 0.9097 \| 0.8815 \| 421 \| 0.4706 \| 0.8163 \| 0.5970 \| 49 \| 0.8625 \| 0.9837 \| 0.9191 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7271 \| 0.8216 \| 0.7715 \| 0.9722 \| \| 0.1084 \| 0.56 \| 7500 \| 0.1073 \| 0.75 \| 0.8077 \| 0.7778 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6484 \| 0.9077 \| 0.7564 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7313 \| 0.8058 \| 0.7667 \| 206 \| 0.6452 \| 0.9302 \| 0.7619 \| 43 \| 0.6933 \| 0.8196 \| 0.7512 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6818 \| 0.9375 \| 0.7895 \| 144 \| 0.6872 \| 0.9123 \| 0.7839 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8789 \| 0.9477 \| 0.9120 \| 421 \| 0.7451 \| 0.7755 \| 0.76 \| 49 \| 0.8374 \| 0.9857 \| 0.9055 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7277 \| 0.8643 \| 0.7902 \| 0.9741 \| \| 0.0789 \| 0.6 \| 8000 \| 0.0958 \| 0.7719 \| 0.8462 \| 0.8073 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7403 \| 0.8769 \| 0.8028 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7731 \| 0.8107 \| 0.7915 \| 206 \| 0.74 \| 0.8605 \| 0.7957 \| 43 \| 0.7408 \| 0.7924 \| 0.7657 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6749 \| 0.9514 \| 0.7896 \| 144 \| 0.8011 \| 0.8480 \| 0.8239 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8299 \| 0.9620 \| 0.8911 \| 421 \| 0.5686 \| 0.5918 \| 0.58 \| 49 \| 0.8770 \| 0.9878 \| 0.9291 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7700 \| 0.8469 \| 0.8066 \| 0.9760 \| \| 0.1149 \| 0.64 \| 8500 \| 0.1334 \| 1.0 \| 0.7692 \| 0.8696 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6795 \| 0.8154 \| 0.7413 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7336 \| 0.7621 \| 0.7476 \| 206 \| 0.3824 \| 0.6047 \| 0.4685 \| 43 \| 0.6318 \| 0.5454 \| 0.5854 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8227 \| 0.8056 \| 0.8140 \| 144 \| 0.7707 \| 0.7076 \| 0.7378 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8776 \| 0.9026 \| 0.8899 \| 421 \| 0.6129 \| 0.7755 \| 0.6847 \| 49 \| 0.8339 \| 0.9817 \| 0.9018 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7231 \| 0.6961 \| 0.7094 \| 0.9673 \| \| 0.1155 \| 0.67 \| 9000 \| 0.1052 \| 0.6267 \| 0.9038 \| 0.7402 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7294 \| 0.9538 \| 0.8267 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7232 \| 0.7864 \| 0.7535 \| 206 \| 0.7391 \| 0.7907 \| 0.7640 \| 43 \| 0.7494 \| 0.7312 \| 0.7402 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7531 \| 0.8472 \| 0.7974 \| 144 \| 0.8708 \| 0.9064 \| 0.8883 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8340 \| 0.9667 \| 0.8955 \| 421 \| 0.5714 \| 0.5714 \| 0.5714 \| 49 \| 0.8709 \| 0.9756 \| 0.9203 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7664 \| 0.8135 \| 0.7893 \| 0.9742 \| \| 0.0926 \| 0.71 \| 9500 \| 0.1048 \| 0.6438 \| 0.9038 \| 0.752 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6610 \| 0.6 \| 0.6290 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7541 \| 0.6699 \| 0.7095 \| 206 \| 0.7308 \| 0.8837 \| 0.8 \| 43 \| 0.6768 \| 0.8456 \| 0.7519 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7119 \| 0.875 \| 0.7850 \| 144 \| 0.8343 \| 0.8830 \| 0.8580 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8712 \| 0.9477 \| 0.9078 \| 421 \| 0.7193 \| 0.8367 \| 0.7736 \| 49 \| 0.8476 \| 0.9857 \| 0.9115 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7322 \| 0.8604 \| 0.7911 \| 0.9760 \| \| 0.0982 \| 0.75 \| 10000 \| 0.0985 \| 0.6533 \| 0.9423 \| 0.7717 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7027 \| 0.8 \| 0.7482 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7671 \| 0.8155 \| 0.7906 \| 206 \| 0.7143 \| 0.9302 \| 0.8081 \| 43 \| 0.7465 \| 0.8039 \| 0.7741 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6507 \| 0.9444 \| 0.7705 \| 144 \| 0.9106 \| 0.9532 \| 0.9314 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8008 \| 0.9264 \| 0.8590 \| 421 \| 0.5641 \| 0.8980 \| 0.6929 \| 49 \| 0.8460 \| 0.9735 \| 0.9053 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7633 \| 0.8568 \| 0.8074 \| 0.9769 \| \| 0.085 \| 0.79 \| 10500 \| 0.0972 \| 0.6184 \| 0.9038 \| 0.7344 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8154 \| 0.8154 \| 0.8154 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7236 \| 0.8641 \| 0.7876 \| 206 \| 0.7755 \| 0.8837 \| 0.8261 \| 43 \| 0.7544 \| 0.8105 \| 0.7814 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7081 \| 0.9097 \| 0.7964 \| 144 \| 0.8778 \| 0.9240 \| 0.9003 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8976 \| 0.9572 \| 0.9264 \| 421 \| 0.8039 \| 0.8367 \| 0.8200 \| 49 \| 0.8432 \| 0.9857 \| 0.9089 \| 491 \| 0.1111 \| 0.0909 \| 0.1000 \| 11 \| 0.7852 \| 0.8643 \| 0.8229 \| 0.9779 \| \| 0.0981 \| 0.82 \| 11000 \| 0.1092 \| 0.6944 \| 0.9615 \| 0.8065 \| 52 \| 0.2 \| 0.1429 \| 0.1667 \| 7 \| 0.7262 \| 0.9385 \| 0.8188 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6842 \| 0.8835 \| 0.7712 \| 206 \| 0.6667 \| 0.7907 \| 0.7234 \| 43 \| 0.7117 \| 0.8251 \| 0.7642 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7159 \| 0.875 \| 0.7875 \| 144 \| 0.9337 \| 0.9064 \| 0.9199 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7175 \| 0.9715 \| 0.8254 \| 421 \| 0.0 \| 0.0 \| 0.0 \| 49 \| 0.8620 \| 0.9796 \| 0.9171 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7399 \| 0.8610 \| 0.7959 \| 0.9737 \| \| 0.0892 \| 0.86 \| 11500 \| 0.0969 \| 0.6049 \| 0.9423 \| 0.7368 \| 52 \| 0.4545 \| 0.7143 \| 0.5556 \| 7 \| 0.0 \| 0.0 \| 0.0 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8 \| 0.8155 \| 0.8077 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.6975 \| 0.8571 \| 0.7691 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7397 \| 0.75 \| 0.7448 \| 144 \| 0.8841 \| 0.8480 \| 0.8657 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8821 \| 0.9596 \| 0.9192 \| 421 \| 0.9474 \| 0.7347 \| 0.8276 \| 49 \| 0.8251 \| 0.9511 \| 0.8836 \| 491 \| 0.25 \| 0.1818 \| 0.2105 \| 11 \| 0.7557 \| 0.8544 \| 0.8020 \| 0.9759 \| \| 0.0924 \| 0.9 \| 12000 \| 0.0971 \| 0.7059 \| 0.9231 \| 0.8000 \| 52 \| 0.4615 \| 0.8571 \| 0.6 \| 7 \| 0.8108 \| 0.9231 \| 0.8633 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7331 \| 0.8932 \| 0.8053 \| 206 \| 0.8 \| 0.9302 \| 0.8602 \| 43 \| 0.7544 \| 0.8535 \| 0.8009 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7697 \| 0.8819 \| 0.8220 \| 144 \| 0.8947 \| 0.8947 \| 0.8947 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7758 \| 0.9454 \| 0.8522 \| 421 \| 0.4516 \| 0.8571 \| 0.5915 \| 49 \| 0.8618 \| 0.9776 \| 0.9160 \| 491 \| 0.08 \| 0.1818 \| 0.1111 \| 11 \| 0.7664 \| 0.8875 \| 0.8225 \| 0.9782 \| \| 0.0784 \| 0.94 \| 12500 \| 0.1113 \| 0.6623 \| 0.9808 \| 0.7907 \| 52 \| 0.6667 \| 0.8571 \| 0.75 \| 7 \| 0.8406 \| 0.8923 \| 0.8657 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6865 \| 0.8398 \| 0.7555 \| 206 \| 0.7547 \| 0.9302 \| 0.8333 \| 43 \| 0.7858 \| 0.7863 \| 0.7861 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8026 \| 0.8472 \| 0.8243 \| 144 \| 0.8629 \| 0.8830 \| 0.8728 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8462 \| 0.9406 \| 0.8909 \| 421 \| 0.56 \| 0.8571 \| 0.6774 \| 49 \| 0.9119 \| 0.9695 \| 0.9398 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.8022 \| 0.8466 \| 0.8238 \| 0.9774 \| \| 0.1063 \| 0.97 \| 13000 \| 0.0932 \| 0.6538 \| 0.9808 \| 0.7846 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7838 \| 0.8923 \| 0.8345 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7738 \| 0.8301 \| 0.8009 \| 206 \| 0.75 \| 0.8372 \| 0.7912 \| 43 \| 0.6979 \| 0.8529 \| 0.7676 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7086 \| 0.8611 \| 0.7774 \| 144 \| 0.8703 \| 0.9415 \| 0.9045 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.6184 \| 0.8931 \| 0.7308 \| 421 \| 0.2424 \| 0.1633 \| 0.1951 \| 49 \| 0.8511 \| 0.9776 \| 0.9100 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7176 \| 0.8646 \| 0.7843 \| 0.9760 \| \| 0.0765 \| 1.01 \| 13500 \| 0.0892 \| 0.6806 \| 0.9423 \| 0.7903 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6714 \| 0.7231 \| 0.6963 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8416 \| 0.8252 \| 0.8333 \| 206 \| 0.7917 \| 0.8837 \| 0.8352 \| 43 \| 0.7330 \| 0.8559 \| 0.7897 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7105 \| 0.9375 \| 0.8084 \| 144 \| 0.8757 \| 0.9474 \| 0.9101 \| 171 \| 0.125 \| 1.0 \| 0.2222 \| 3 \| 0.8769 \| 0.9810 \| 0.9260 \| 421 \| 0.5970 \| 0.8163 \| 0.6897 \| 49 \| 0.8761 \| 0.9796 \| 0.925 \| 491 \| 0.0 \| 0.0 \| 0.0 \| 11 \| 0.7696 \| 0.8881 \| 0.8246 \| 0.9790 \| \| 0.0677 \| 1.05 \| 14000 \| 0.0804 \| 0.6667 \| 0.9231 \| 0.7742 \| 52 \| 0.3333 \| 0.7143 \| 0.4545 \| 7 \| 0.7941 \| 0.8308 \| 0.8120 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8112 \| 0.7718 \| 0.7910 \| 206 \| 0.7234 \| 0.7907 \| 0.7556 \| 43 \| 0.7725 \| 0.8487 \| 0.8088 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7949 \| 0.8611 \| 0.8267 \| 144 \| 0.9401 \| 0.9181 \| 0.9290 \| 171 \| 0.1765 \| 1.0 \| 0.3 \| 3 \| 0.8613 \| 0.9739 \| 0.9142 \| 421 \| 0.4868 \| 0.7551 \| 0.592 \| 49 \| 0.8881 \| 0.9857 \| 0.9344 \| 491 \| 0.2222 \| 0.1818 \| 0.2000 \| 11 \| 0.7978 \| 0.8782 \| 0.8360 \| 0.9805 \| \| 0.0544 \| 1.09 \| 14500 \| 0.0924 \| 0.9216 \| 0.9038 \| 0.9126 \| 52 \| 0.1875 \| 0.4286 \| 0.2609 \| 7 \| 0.7973 \| 0.9077 \| 0.8489 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7511 \| 0.8641 \| 0.8036 \| 206 \| 0.78 \| 0.9070 \| 0.8387 \| 43 \| 0.7361 \| 0.8747 \| 0.7994 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.6569 \| 0.9306 \| 0.7701 \| 144 \| 0.9253 \| 0.9415 \| 0.9333 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.9146 \| 0.9667 \| 0.9400 \| 421 \| 0.6308 \| 0.8367 \| 0.7193 \| 49 \| 0.8121 \| 0.9857 \| 0.8905 \| 491 \| 0.0833 \| 0.1818 \| 0.1143 \| 11 \| 0.7679 \| 0.9025 \| 0.8298 \| 0.9793 \| \| 0.0797 \| 1.12 \| 15000 \| 0.0851 \| 0.9057 \| 0.9231 \| 0.9143 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7294 \| 0.9538 \| 0.8267 \| 65 \| 0.5 \| 0.5714 \| 0.5333 \| 7 \| 0.7909 \| 0.8447 \| 0.8169 \| 206 \| 0.8125 \| 0.9070 \| 0.8571 \| 43 \| 0.8104 \| 0.8432 \| 0.8265 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7 \| 0.8264 \| 0.7580 \| 144 \| 0.8804 \| 0.9474 \| 0.9127 \| 171 \| 0.2222 \| 0.6667 \| 0.3333 \| 3 \| 0.8834 \| 0.9359 \| 0.9089 \| 421 \| 0.5056 \| 0.9184 \| 0.6522 \| 49 \| 0.8436 \| 0.9776 \| 0.9057 \| 491 \| 0.0625 \| 0.0909 \| 0.0741 \| 11 \| 0.8077 \| 0.8794 \| 0.8420 \| 0.9793 \| \| 0.0544 \| 1.16 \| 15500 \| 0.0905 \| 0.7 \| 0.9423 \| 0.8033 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6421 \| 0.9385 \| 0.7625 \| 65 \| 0.25 \| 0.2857 \| 0.2667 \| 7 \| 0.8018 \| 0.8447 \| 0.8227 \| 206 \| 0.7273 \| 0.9302 \| 0.8163 \| 43 \| 0.7642 \| 0.8571 \| 0.8080 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8098 \| 0.9167 \| 0.8599 \| 144 \| 0.9261 \| 0.9532 \| 0.9395 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.6976 \| 0.9810 \| 0.8154 \| 421 \| 0.6066 \| 0.7551 \| 0.6727 \| 49 \| 0.8948 \| 0.9878 \| 0.9390 \| 491 \| 0.3636 \| 0.3636 \| 0.3636 \| 11 \| 0.7664 \| 0.8953 \| 0.8259 \| 0.9793 \| \| 0.0815 \| 1.2 \| 16000 \| 0.0799 \| 0.9804 \| 0.9615 \| 0.9709 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.6593 \| 0.9231 \| 0.7692 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.825 \| 0.8010 \| 0.8128 \| 206 \| 0.6667 \| 0.9302 \| 0.7767 \| 43 \| 0.7140 \| 0.8523 \| 0.7770 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7016 \| 0.9306 \| 0.8000 \| 144 \| 0.9096 \| 0.9415 \| 0.9253 \| 171 \| 0.3 \| 1.0 \| 0.4615 \| 3 \| 0.7203 \| 0.9359 \| 0.8140 \| 421 \| 0.3193 \| 0.7755 \| 0.4524 \| 49 \| 0.8548 \| 0.9470 \| 0.8986 \| 491 \| 0.5 \| 0.4545 \| 0.4762 \| 11 \| 0.7339 \| 0.8794 \| 0.8001 \| 0.9780 \| \| 0.0647 \| 1.24 \| 16500 \| 0.0739 \| 0.8889 \| 0.9231 \| 0.9057 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7692 \| 0.9231 \| 0.8392 \| 65 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8077 \| 0.8155 \| 0.8116 \| 206 \| 0.8 \| 0.9302 \| 0.8602 \| 43 \| 0.7750 \| 0.8717 \| 0.8205 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8467 \| 0.8819 \| 0.8639 \| 144 \| 0.92 \| 0.9415 \| 0.9306 \| 171 \| 0.0682 \| 1.0 \| 0.1277 \| 3 \| 0.8515 \| 0.9810 \| 0.9117 \| 421 \| 0.9318 \| 0.8367 \| 0.8817 \| 49 \| 0.9120 \| 0.9919 \| 0.9502 \| 491 \| 0.1875 \| 0.2727 \| 0.2222 \| 11 \| 0.8066 \| 0.8998 \| 0.8507 \| 0.9820 \| \| 0.0532 \| 1.27 \| 17000 \| 0.0870 \| 0.8491 \| 0.8654 \| 0.8571 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.8657 \| 0.8923 \| 0.8788 \| 65 \| 0.5714 \| 0.5714 \| 0.5714 \| 7 \| 0.7404 \| 0.8447 \| 0.7891 \| 206 \| 0.8163 \| 0.9302 \| 0.8696 \| 43 \| 0.8296 \| 0.8547 \| 0.8420 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8217 \| 0.8958 \| 0.8571 \| 144 \| 0.8931 \| 0.8304 \| 0.8606 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8369 \| 0.9382 \| 0.8847 \| 421 \| 0.9574 \| 0.9184 \| 0.9375 \| 49 \| 0.9026 \| 0.9817 \| 0.9405 \| 491 \| 0.5714 \| 0.3636 \| 0.4444 \| 11 \| 0.8367 \| 0.8815 \| 0.8585 \| 0.9810 \| \| 0.0673 \| 1.31 \| 17500 \| 0.0851 \| 0.8929 \| 0.9615 \| 0.9259 \| 52 \| 0.5714 \| 0.5714 \| 0.5714 \| 7 \| 0.7024 \| 0.9077 \| 0.7919 \| 65 \| 0.4 \| 0.2857 \| 0.3333 \| 7 \| 0.7817 \| 0.8689 \| 0.8230 \| 206 \| 0.7959 \| 0.9070 \| 0.8478 \| 43 \| 0.8198 \| 0.8511 \| 0.8352 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7738 \| 0.9028 \| 0.8333 \| 144 \| 0.9162 \| 0.9591 \| 0.9371 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8655 \| 0.9786 \| 0.9186 \| 421 \| 0.775 \| 0.6327 \| 0.6966 \| 49 \| 0.8377 \| 0.9776 \| 0.9023 \| 491 \| 0.2143 \| 0.2727 \| 0.2400 \| 11 \| 0.8231 \| 0.8902 \| 0.8553 \| 0.9816 \| \| 0.0715 \| 1.35 \| 18000 \| 0.0821 \| 0.8868 \| 0.9038 \| 0.8952 \| 52 \| 0.1 \| 1.0 \| 0.1818 \| 7 \| 0.6778 \| 0.9385 \| 0.7871 \| 65 \| 0.8 \| 0.5714 \| 0.6667 \| 7 \| 0.7653 \| 0.7913 \| 0.7780 \| 206 \| 0.78 \| 0.9070 \| 0.8387 \| 43 \| 0.7410 \| 0.8989 \| 0.8124 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7458 \| 0.9167 \| 0.8224 \| 144 \| 0.8713 \| 0.8713 \| 0.8713 \| 171 \| 0.2727 \| 1.0 \| 0.4286 \| 3 \| 0.8008 \| 0.9644 \| 0.875 \| 421 \| 0.4333 \| 0.7959 \| 0.5612 \| 49 \| 0.8920 \| 0.9756 \| 0.9319 \| 491 \| 0.8333 \| 0.4545 \| 0.5882 \| 11 \| 0.7578 \| 0.9082 \| 0.8262 \| 0.9793 \| \| 0.0778 \| 1.39 \| 18500 \| 0.0661 \| 0.9074 \| 0.9423 \| 0.9245 \| 52 \| 0.0714 \| 0.1429 \| 0.0952 \| 7 \| 0.8 \| 0.9231 \| 0.8571 \| 65 \| 1.0 \| 0.2857 \| 0.4444 \| 7 \| 0.8757 \| 0.7864 \| 0.8286 \| 206 \| 0.7547 \| 0.9302 \| 0.8333 \| 43 \| 0.7831 \| 0.8674 \| 0.8231 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8323 \| 0.9306 \| 0.8787 \| 144 \| 0.8859 \| 0.9532 \| 0.9183 \| 171 \| 0.1875 \| 1.0 \| 0.3158 \| 3 \| 0.9138 \| 0.9572 \| 0.9350 \| 421 \| 0.7963 \| 0.8776 \| 0.8350 \| 49 \| 0.8544 \| 0.9919 \| 0.9180 \| 491 \| 0.2 \| 0.1818 \| 0.1905 \| 11 \| 0.8172 \| 0.8971 \| 0.8553 \| 0.9829 \| \| 0.0672 \| 1.42 \| 19000 \| 0.0841 \| 0.6538 \| 0.9808 \| 0.7846 \| 52 \| 0.2593 \| 1.0 \| 0.4118 \| 7 \| 0.6703 \| 0.9385 \| 0.7821 \| 65 \| 0.4 \| 0.2857 \| 0.3333 \| 7 \| 0.8162 \| 0.7330 \| 0.7724 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.7510 \| 0.8747 \| 0.8082 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7432 \| 0.9444 \| 0.8318 \| 144 \| 0.8477 \| 0.9766 \| 0.9076 \| 171 \| 0.1579 \| 1.0 \| 0.2727 \| 3 \| 0.8103 \| 0.9739 \| 0.8846 \| 421 \| 0.6327 \| 0.6327 \| 0.6327 \| 49 \| 0.7970 \| 0.9674 \| 0.8740 \| 491 \| 0.1190 \| 0.4545 \| 0.1887 \| 11 \| 0.7558 \| 0.8977 \| 0.8207 \| 0.9787 \| \| 0.0802 \| 1.46 \| 19500 \| 0.0682 \| 0.8276 \| 0.9231 \| 0.8727 \| 52 \| 0.4615 \| 0.8571 \| 0.6 \| 7 \| 0.7468 \| 0.9077 \| 0.8194 \| 65 \| 0.3333 \| 0.2857 \| 0.3077 \| 7 \| 0.7621 \| 0.8398 \| 0.7991 \| 206 \| 0.9091 \| 0.9302 \| 0.9195 \| 43 \| 0.7958 \| 0.8801 \| 0.8359 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7735 \| 0.9722 \| 0.8615 \| 144 \| 0.9357 \| 0.9357 \| 0.9357 \| 171 \| 0.3333 \| 1.0 \| 0.5 \| 3 \| 0.8385 \| 0.9620 \| 0.8960 \| 421 \| 0.5556 \| 0.9184 \| 0.6923 \| 49 \| 0.8845 \| 0.9674 \| 0.9241 \| 491 \| 0.2778 \| 0.4545 \| 0.3448 \| 11 \| 0.8074 \| 0.9070 \| 0.8543 \| 0.9819 \| \| 0.0886 \| 1.5 \| 20000 \| 0.0633 \| 0.9259 \| 0.9615 \| 0.9434 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.7568 \| 0.8615 \| 0.8058 \| 65 \| 0.5714 \| 0.5714 \| 0.5714 \| 7 \| 0.8980 \| 0.8544 \| 0.8756 \| 206 \| 0.9302 \| 0.9302 \| 0.9302 \| 43 \| 0.8470 \| 0.8916 \| 0.8688 \| 1652 \| 0.25 \| 1.0 \| 0.4 \| 2 \| 0.8373 \| 0.9653 \| 0.8968 \| 144 \| 0.9032 \| 0.9825 \| 0.9412 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.9044 \| 0.9667 \| 0.9346 \| 421 \| 0.7931 \| 0.9388 \| 0.8598 \| 49 \| 0.8342 \| 0.9939 \| 0.9071 \| 491 \| 0.1053 \| 0.3636 \| 0.1633 \| 11 \| 0.8471 \| 0.9185 \| 0.8814 \| 0.9833 \| \| 0.0525 \| 1.54 \| 20500 \| 0.0632 \| 0.8197 \| 0.9615 \| 0.8850 \| 52 \| 0.7 \| 1.0 \| 0.8235 \| 7 \| 0.6742 \| 0.9231 \| 0.7792 \| 65 \| 0.4444 \| 0.5714 \| 0.5 \| 7 \| 0.7819 \| 0.9223 \| 0.8463 \| 206 \| 0.6721 \| 0.9535 \| 0.7885 \| 43 \| 0.8220 \| 0.8723 \| 0.8464 \| 1652 \| 0.0909 \| 0.5 \| 0.1538 \| 2 \| 0.7812 \| 0.8681 \| 0.8224 \| 144 \| 0.9180 \| 0.9825 \| 0.9492 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8838 \| 0.9572 \| 0.9190 \| 421 \| 0.5 \| 0.9592 \| 0.6573 \| 49 \| 0.8173 \| 0.9837 \| 0.8928 \| 491 \| 0.25 \| 0.3636 \| 0.2963 \| 11 \| 0.8092 \| 0.9097 \| 0.8565 \| 0.9828 \| \| 0.0664 \| 1.57 \| 21000 \| 0.0671 \| 0.8197 \| 0.9615 \| 0.8850 \| 52 \| 0.5385 \| 1.0 \| 0.7000 \| 7 \| 0.6778 \| 0.9385 \| 0.7871 \| 65 \| 0.375 \| 0.4286 \| 0.4000 \| 7 \| 0.7932 \| 0.9126 \| 0.8488 \| 206 \| 0.72 \| 0.8372 \| 0.7742 \| 43 \| 0.7546 \| 0.8935 \| 0.8182 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7571 \| 0.9306 \| 0.8349 \| 144 \| 0.8777 \| 0.9649 \| 0.9192 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8867 \| 0.9667 \| 0.9250 \| 421 \| 0.8846 \| 0.9388 \| 0.9109 \| 49 \| 0.8199 \| 0.9919 \| 0.8977 \| 491 \| 0.3333 \| 0.4545 \| 0.3846 \| 11 \| 0.7829 \| 0.9221 \| 0.8468 \| 0.9830 \| \| 0.0524 \| 1.61 \| 21500 \| 0.0674 \| 0.8305 \| 0.9423 \| 0.8829 \| 52 \| 0.5833 \| 1.0 \| 0.7368 \| 7 \| 0.7763 \| 0.9077 \| 0.8369 \| 65 \| 0.375 \| 0.4286 \| 0.4000 \| 7 \| 0.8889 \| 0.8544 \| 0.8713 \| 206 \| 0.7692 \| 0.9302 \| 0.8421 \| 43 \| 0.8235 \| 0.8838 \| 0.8526 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9041 \| 0.9167 \| 0.9103 \| 144 \| 0.9527 \| 0.9415 \| 0.9471 \| 171 \| 0.4286 \| 1.0 \| 0.6 \| 3 \| 0.9470 \| 0.9762 \| 0.9614 \| 421 \| 0.7857 \| 0.8980 \| 0.8381 \| 49 \| 0.8857 \| 0.9939 \| 0.9367 \| 491 \| 0.5 \| 0.4545 \| 0.4762 \| 11 \| 0.8555 \| 0.9140 \| 0.8838 \| 0.9844 \| \| 0.0603 \| 1.65 \| 22000 \| 0.0735 \| 0.7812 \| 0.9615 \| 0.8621 \| 52 \| 0.0 \| 0.0 \| 0.0 \| 7 \| 0.9206 \| 0.8923 \| 0.9062 \| 65 \| 0.8 \| 0.5714 \| 0.6667 \| 7 \| 0.8062 \| 0.8883 \| 0.8453 \| 206 \| 0.6721 \| 0.9535 \| 0.7885 \| 43 \| 0.8402 \| 0.8051 \| 0.8223 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8036 \| 0.9375 \| 0.8654 \| 144 \| 0.9167 \| 0.9649 \| 0.9402 \| 171 \| 0.3 \| 1.0 \| 0.4615 \| 3 \| 0.9249 \| 0.9359 \| 0.9303 \| 421 \| 0.7077 \| 0.9388 \| 0.8070 \| 49 \| 0.9198 \| 0.9817 \| 0.9498 \| 491 \| 0.6667 \| 0.5455 \| 0.6 \| 11 \| 0.8558 \| 0.8715 \| 0.8636 \| 0.9822 \| \| 0.0674 \| 1.69 \| 22500 \| 0.0639 \| 0.8103 \| 0.9038 \| 0.8545 \| 52 \| 0.2 \| 0.2857 \| 0.2353 \| 7 \| 0.7838 \| 0.8923 \| 0.8345 \| 65 \| 1.0 \| 0.5714 \| 0.7273 \| 7 \| 0.8852 \| 0.8981 \| 0.8916 \| 206 \| 0.8163 \| 0.9302 \| 0.8696 \| 43 \| 0.8393 \| 0.8759 \| 0.8572 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8618 \| 0.9097 \| 0.8851 \| 144 \| 0.8771 \| 0.9181 \| 0.8971 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.9400 \| 0.9667 \| 0.9532 \| 421 \| 0.9388 \| 0.9388 \| 0.9388 \| 49 \| 0.9030 \| 0.9857 \| 0.9426 \| 491 \| 0.3846 \| 0.4545 \| 0.4167 \| 11 \| 0.8633 \| 0.9064 \| 0.8844 \| 0.9843 \| \| 0.0693 \| 1.72 \| 23000 \| 0.0773 \| 0.7143 \| 0.9615 \| 0.8197 \| 52 \| 0.8571 \| 0.8571 \| 0.8571 \| 7 \| 0.8356 \| 0.9385 \| 0.8841 \| 65 \| 0.625 \| 0.7143 \| 0.6667 \| 7 \| 0.8009 \| 0.8786 \| 0.8380 \| 206 \| 0.7119 \| 0.9767 \| 0.8235 \| 43 \| 0.7847 \| 0.9001 \| 0.8385 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7640 \| 0.9444 \| 0.8447 \| 144 \| 0.8836 \| 0.9766 \| 0.9278 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7143 \| 0.9501 \| 0.8155 \| 421 \| 0.3780 \| 0.9796 \| 0.5455 \| 49 \| 0.8134 \| 0.9674 \| 0.8837 \| 491 \| 0.5714 \| 0.3636 \| 0.4444 \| 11 \| 0.7688 \| 0.9212 \| 0.8381 \| 0.9808 \| \| 0.0383 \| 1.76 \| 23500 \| 0.0667 \| 0.6410 \| 0.9615 \| 0.7692 \| 52 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.7692 \| 0.9231 \| 0.8392 \| 65 \| 0.8333 \| 0.7143 \| 0.7692 \| 7 \| 0.8326 \| 0.8689 \| 0.8504 \| 206 \| 0.7636 \| 0.9767 \| 0.8571 \| 43 \| 0.8580 \| 0.8777 \| 0.8677 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8571 \| 0.9167 \| 0.8859 \| 144 \| 0.9405 \| 0.9240 \| 0.9322 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8901 \| 0.9810 \| 0.9333 \| 421 \| 0.88 \| 0.8980 \| 0.8889 \| 49 \| 0.9112 \| 0.9817 \| 0.9451 \| 491 \| 0.3636 \| 0.3636 \| 0.3636 \| 11 \| 0.8628 \| 0.9097 \| 0.8856 \| 0.9845 \| \| 0.0496 \| 1.8 \| 24000 \| 0.0712 \| 0.8 \| 0.9231 \| 0.8571 \| 52 \| 0.8571 \| 0.8571 \| 0.8571 \| 7 \| 0.7262 \| 0.9385 \| 0.8188 \| 65 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.8390 \| 0.8350 \| 0.8370 \| 206 \| 0.8889 \| 0.9302 \| 0.9091 \| 43 \| 0.8522 \| 0.8692 \| 0.8607 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8867 \| 0.9236 \| 0.9048 \| 144 \| 0.9598 \| 0.9766 \| 0.9681 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8963 \| 0.9857 \| 0.9389 \| 421 \| 0.7015 \| 0.9592 \| 0.8103 \| 49 \| 0.9412 \| 0.9776 \| 0.9590 \| 491 \| 0.25 \| 0.5455 \| 0.3429 \| 11 \| 0.8659 \| 0.9073 \| 0.8861 \| 0.9848 \| \| 0.0465 \| 1.84 \| 24500 \| 0.0612 \| 0.6667 \| 0.9615 \| 0.7874 \| 52 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.7625 \| 0.9385 \| 0.8414 \| 65 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.8287 \| 0.8689 \| 0.8483 \| 206 \| 0.7407 \| 0.9302 \| 0.8247 \| 43 \| 0.8236 \| 0.8904 \| 0.8557 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.7919 \| 0.9514 \| 0.8644 \| 144 \| 0.9326 \| 0.9708 \| 0.9513 \| 171 \| 0.0513 \| 0.6667 \| 0.0952 \| 3 \| 0.9079 \| 0.9834 \| 0.9441 \| 421 \| 0.8958 \| 0.8776 \| 0.8866 \| 49 \| 0.9186 \| 0.9878 \| 0.9519 \| 491 \| 0.1765 \| 0.2727 \| 0.2143 \| 11 \| 0.8355 \| 0.9212 \| 0.8762 \| 0.9853 \| \| 0.0446 \| 1.87 \| 25000 \| 0.0662 \| 0.6410 \| 0.9615 \| 0.7692 \| 52 \| 0.6364 \| 1.0 \| 0.7778 \| 7 \| 0.8732 \| 0.9538 \| 0.9118 \| 65 \| 0.8333 \| 0.7143 \| 0.7692 \| 7 \| 0.9378 \| 0.8786 \| 0.9073 \| 206 \| 0.8333 \| 0.9302 \| 0.8791 \| 43 \| 0.8362 \| 0.8747 \| 0.8550 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8447 \| 0.9444 \| 0.8918 \| 144 \| 0.9598 \| 0.9766 \| 0.9681 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.92 \| 0.9834 \| 0.9506 \| 421 \| 0.9070 \| 0.7959 \| 0.8478 \| 49 \| 0.9186 \| 0.9878 \| 0.9519 \| 491 \| 0.3636 \| 0.3636 \| 0.3636 \| 11 \| 0.8659 \| 0.9131 \| 0.8889 \| 0.9851 \| \| 0.0496 \| 1.91 \| 25500 \| 0.0653 \| 0.7612 \| 0.9808 \| 0.8571 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8472 \| 0.9385 \| 0.8905 \| 65 \| 0.5 \| 0.5714 \| 0.5333 \| 7 \| 0.9158 \| 0.8981 \| 0.9069 \| 206 \| 0.8367 \| 0.9535 \| 0.8913 \| 43 \| 0.8487 \| 0.8729 \| 0.8606 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8889 \| 0.9444 \| 0.9158 \| 144 \| 0.9586 \| 0.9474 \| 0.9529 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.9077 \| 0.9810 \| 0.9429 \| 421 \| 0.7895 \| 0.9184 \| 0.8491 \| 49 \| 0.9120 \| 0.9919 \| 0.9502 \| 491 \| 0.5 \| 0.2727 \| 0.3529 \| 11 \| 0.8714 \| 0.9137 \| 0.8921 \| 0.9854 \| \| 0.0689 \| 1.95 \| 26000 \| 0.0689 \| 0.8596 \| 0.9423 \| 0.8991 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.7887 \| 0.8615 \| 0.8235 \| 65 \| 0.5714 \| 0.5714 \| 0.5714 \| 7 \| 0.9064 \| 0.8932 \| 0.8998 \| 206 \| 0.8367 \| 0.9535 \| 0.8913 \| 43 \| 0.8217 \| 0.9122 \| 0.8646 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8232 \| 0.9375 \| 0.8766 \| 144 \| 0.9222 \| 0.9708 \| 0.9459 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.8827 \| 0.9834 \| 0.9303 \| 421 \| 0.9744 \| 0.7755 \| 0.8636 \| 49 \| 0.8574 \| 0.9919 \| 0.9197 \| 491 \| 0.4286 \| 0.2727 \| 0.3333 \| 11 \| 0.8441 \| 0.9299 \| 0.8849 \| 0.9842 \| \| 0.0465 \| 1.99 \| 26500 \| 0.1060 \| 0.8136 \| 0.9231 \| 0.8649 \| 52 \| 0.5 \| 1.0 \| 0.6667 \| 7 \| 0.7778 \| 0.8615 \| 0.8175 \| 65 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.8552 \| 0.9175 \| 0.8852 \| 206 \| 0.82 \| 0.9535 \| 0.8817 \| 43 \| 0.8698 \| 0.8977 \| 0.8835 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8904 \| 0.9028 \| 0.8966 \| 144 \| 0.9643 \| 0.9474 \| 0.9558 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.7361 \| 0.9739 \| 0.8384 \| 421 \| 0.25 \| 0.0612 \| 0.0984 \| 49 \| 0.8832 \| 0.9552 \| 0.9178 \| 491 \| 0.1 \| 0.1818 \| 0.1290 \| 11 \| 0.8384 \| 0.9040 \| 0.8700 \| 0.9796 \| \| 0.0448 \| 2.02 \| 27000 \| 0.0686 \| 0.7385 \| 0.9231 \| 0.8205 \| 52 \| 0.625 \| 0.7143 \| 0.6667 \| 7 \| 0.8714 \| 0.9385 \| 0.9037 \| 65 \| 0.625 \| 0.7143 \| 0.6667 \| 7 \| 0.8545 \| 0.9126 \| 0.8826 \| 206 \| 0.6727 \| 0.8605 \| 0.7551 \| 43 \| 0.8778 \| 0.8959 \| 0.8868 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9116 \| 0.9306 \| 0.9210 \| 144 \| 0.9538 \| 0.9649 \| 0.9593 \| 171 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.9157 \| 0.9549 \| 0.9349 \| 421 \| 0.875 \| 0.8571 \| 0.8660 \| 49 \| 0.8855 \| 0.9919 \| 0.9356 \| 491 \| 0.4 \| 0.3636 \| 0.3810 \| 11 \| 0.8790 \| 0.9200 \| 0.8990 \| 0.9854 \| \| 0.0379 \| 2.06 \| 27500 \| 0.0633 \| 0.8421 \| 0.9231 \| 0.8807 \| 52 \| 0.2308 \| 0.4286 \| 0.3 \| 7 \| 0.8824 \| 0.9231 \| 0.9023 \| 65 \| 0.4545 \| 0.7143 \| 0.5556 \| 7 \| 0.8451 \| 0.9272 \| 0.8843 \| 206 \| 0.7037 \| 0.8837 \| 0.7835 \| 43 \| 0.8901 \| 0.8674 \| 0.8786 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8303 \| 0.9514 \| 0.8867 \| 144 \| 0.9706 \| 0.9649 \| 0.9677 \| 171 \| 0.3333 \| 1.0 \| 0.5 \| 3 \| 0.9300 \| 0.9786 \| 0.9537 \| 421 \| 0.9149 \| 0.8776 \| 0.8958 \| 49 \| 0.8385 \| 0.9939 \| 0.9096 \| 491 \| 0.2 \| 0.3636 \| 0.2581 \| 11 \| 0.8719 \| 0.9116 \| 0.8913 \| 0.9859 \| \| 0.0352 \| 2.1 \| 28000 \| 0.0653 \| 0.8772 \| 0.9615 \| 0.9174 \| 52 \| 0.5556 \| 0.7143 \| 0.6250 \| 7 \| 0.8158 \| 0.9538 \| 0.8794 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.8733 \| 0.9369 \| 0.9040 \| 206 \| 0.8913 \| 0.9535 \| 0.9213 \| 43 \| 0.8272 \| 0.9128 \| 0.8679 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8824 \| 0.9375 \| 0.9091 \| 144 \| 0.9706 \| 0.9649 \| 0.9677 \| 171 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.8790 \| 0.9834 \| 0.9283 \| 421 \| 0.9 \| 0.9184 \| 0.9091 \| 49 \| 0.8692 \| 0.9878 \| 0.9247 \| 491 \| 0.25 \| 0.4545 \| 0.3226 \| 11 \| 0.8493 \| 0.9377 \| 0.8913 \| 0.9844 \| \| 0.0328 \| 2.14 \| 28500 \| 0.0599 \| 0.8772 \| 0.9615 \| 0.9174 \| 52 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.8806 \| 0.9077 \| 0.8939 \| 65 \| 0.5556 \| 0.7143 \| 0.6250 \| 7 \| 0.8804 \| 0.8932 \| 0.8867 \| 206 \| 0.8542 \| 0.9535 \| 0.9011 \| 43 \| 0.8680 \| 0.9074 \| 0.8872 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.85 \| 0.9444 \| 0.8947 \| 144 \| 0.9701 \| 0.9474 \| 0.9586 \| 171 \| 0.2727 \| 1.0 \| 0.4286 \| 3 \| 0.9452 \| 0.9834 \| 0.9639 \| 421 \| 0.6714 \| 0.9592 \| 0.7899 \| 49 \| 0.8937 \| 0.9756 \| 0.9328 \| 491 \| 0.5 \| 0.4545 \| 0.4762 \| 11 \| 0.8786 \| 0.9293 \| 0.9032 \| 0.9867 \| \| 0.0473 \| 2.17 \| 29000 \| 0.0595 \| 0.7692 \| 0.9615 \| 0.8547 \| 52 \| 0.2222 \| 0.2857 \| 0.25 \| 7 \| 0.8493 \| 0.9538 \| 0.8986 \| 65 \| 0.6667 \| 0.5714 \| 0.6154 \| 7 \| 0.8889 \| 0.9320 \| 0.9100 \| 206 \| 0.8367 \| 0.9535 \| 0.8913 \| 43 \| 0.8341 \| 0.9189 \| 0.8744 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8466 \| 0.9583 \| 0.8990 \| 144 \| 0.9711 \| 0.9825 \| 0.9767 \| 171 \| 0.2143 \| 1.0 \| 0.3529 \| 3 \| 0.9234 \| 0.9739 \| 0.9480 \| 421 \| 0.75 \| 0.9184 \| 0.8257 \| 49 \| 0.8844 \| 0.9817 \| 0.9305 \| 491 \| 0.5556 \| 0.4545 \| 0.5 \| 11 \| 0.8557 \| 0.9386 \| 0.8953 \| 0.9855 \| \| 0.0511 \| 2.21 \| 29500 \| 0.0668 \| 0.6849 \| 0.9615 \| 0.8000 \| 52 \| 0.1522 \| 1.0 \| 0.2642 \| 7 \| 0.7561 \| 0.9538 \| 0.8435 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.8761 \| 0.9272 \| 0.9009 \| 206 \| 0.8039 \| 0.9535 \| 0.8723 \| 43 \| 0.8154 \| 0.9195 \| 0.8643 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8491 \| 0.9375 \| 0.8911 \| 144 \| 0.9709 \| 0.9766 \| 0.9738 \| 171 \| 0.2727 \| 1.0 \| 0.4286 \| 3 \| 0.8939 \| 0.9810 \| 0.9354 \| 421 \| 0.5789 \| 0.8980 \| 0.704 \| 49 \| 0.8403 \| 0.9857 \| 0.9072 \| 491 \| 0.5 \| 0.4545 \| 0.4762 \| 11 \| 0.8214 \| 0.9407 \| 0.8770 \| 0.9845 \| \| 0.0369 \| 2.25 \| 30000 \| 0.0695 \| 0.6579 \| 0.9615 \| 0.7812 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8732 \| 0.9538 \| 0.9118 \| 65 \| 0.625 \| 0.7143 \| 0.6667 \| 7 \| 0.9154 \| 0.8932 \| 0.9042 \| 206 \| 0.9535 \| 0.9535 \| 0.9535 \| 43 \| 0.8883 \| 0.9001 \| 0.8942 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9013 \| 0.9514 \| 0.9257 \| 144 \| 0.9527 \| 0.9415 \| 0.9471 \| 171 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.9126 \| 0.9430 \| 0.9276 \| 421 \| 0.5104 \| 1.0 \| 0.6759 \| 49 \| 0.9286 \| 0.9796 \| 0.9534 \| 491 \| 0.3571 \| 0.4545 \| 0.4 \| 11 \| 0.8837 \| 0.9233 \| 0.9030 \| 0.9854 \| \| 0.041 \| 2.29 \| 30500 \| 0.0623 \| 0.9091 \| 0.9615 \| 0.9346 \| 52 \| 0.4375 \| 1.0 \| 0.6087 \| 7 \| 0.8378 \| 0.9538 \| 0.8921 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.9061 \| 0.9369 \| 0.9212 \| 206 \| 0.8723 \| 0.9535 \| 0.9111 \| 43 \| 0.8486 \| 0.9225 \| 0.8840 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8940 \| 0.9375 \| 0.9153 \| 144 \| 0.9708 \| 0.9708 \| 0.9708 \| 171 \| 1.0 \| 1.0 \| 1.0 \| 3 \| 0.9556 \| 0.9715 \| 0.9635 \| 421 \| 0.7705 \| 0.9592 \| 0.8545 \| 49 \| 0.9310 \| 0.9898 \| 0.9595 \| 491 \| 0.3333 \| 0.4545 \| 0.3846 \| 11 \| 0.8803 \| 0.9428 \| 0.9105 \| 0.9853 \| \| 0.0385 \| 2.32 \| 31000 \| 0.0632 \| 0.9091 \| 0.9615 \| 0.9346 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.7848 \| 0.9538 \| 0.8611 \| 65 \| 0.5556 \| 0.7143 \| 0.6250 \| 7 \| 0.8915 \| 0.9175 \| 0.9043 \| 206 \| 0.9111 \| 0.9535 \| 0.9318 \| 43 \| 0.8486 \| 0.9092 \| 0.8778 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8961 \| 0.9583 \| 0.9262 \| 144 \| 0.9709 \| 0.9766 \| 0.9738 \| 171 \| 1.0 \| 1.0 \| 1.0 \| 3 \| 0.9180 \| 0.9834 \| 0.9495 \| 421 \| 0.8478 \| 0.7959 \| 0.8211 \| 49 \| 0.8959 \| 0.9817 \| 0.9368 \| 491 \| 0.3125 \| 0.4545 \| 0.3704 \| 11 \| 0.8724 \| 0.9338 \| 0.9021 \| 0.9849 \| \| 0.0415 \| 2.36 \| 31500 \| 0.0647 \| 0.8929 \| 0.9615 \| 0.9259 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8493 \| 0.9538 \| 0.8986 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.9363 \| 0.9272 \| 0.9317 \| 206 \| 0.875 \| 0.9767 \| 0.9231 \| 43 \| 0.8679 \| 0.9025 \| 0.8849 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8726 \| 0.9514 \| 0.9103 \| 144 \| 0.9429 \| 0.9649 \| 0.9538 \| 171 \| 0.3 \| 1.0 \| 0.4615 \| 3 \| 0.9154 \| 0.9762 \| 0.9448 \| 421 \| 0.7719 \| 0.8980 \| 0.8302 \| 49 \| 0.9067 \| 0.9898 \| 0.9464 \| 491 \| 0.1579 \| 0.5455 \| 0.2449 \| 11 \| 0.8767 \| 0.9329 \| 0.9039 \| 0.9847 \| \| 0.0454 \| 2.4 \| 32000 \| 0.0606 \| 0.9091 \| 0.9615 \| 0.9346 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8971 \| 0.9385 \| 0.9173 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9139 \| 0.9272 \| 0.9205 \| 206 \| 0.8542 \| 0.9535 \| 0.9011 \| 43 \| 0.8652 \| 0.9056 \| 0.8849 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9122 \| 0.9375 \| 0.9247 \| 144 \| 0.9483 \| 0.9649 \| 0.9565 \| 171 \| 0.3 \| 1.0 \| 0.4615 \| 3 \| 0.9321 \| 0.9786 \| 0.9548 \| 421 \| 0.8136 \| 0.9796 \| 0.8889 \| 49 \| 0.9455 \| 0.9898 \| 0.9672 \| 491 \| 0.2143 \| 0.5455 \| 0.3077 \| 11 \| 0.888 \| 0.9350 \| 0.9109 \| 0.9869 \| \| 0.0334 \| 2.44 \| 32500 \| 0.0610 \| 0.8929 \| 0.9615 \| 0.9259 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8714 \| 0.9385 \| 0.9037 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8977 \| 0.9369 \| 0.9169 \| 206 \| 0.9130 \| 0.9767 \| 0.9438 \| 43 \| 0.8463 \| 0.9068 \| 0.8755 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9007 \| 0.9444 \| 0.9220 \| 144 \| 0.9532 \| 0.9532 \| 0.9532 \| 171 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.9536 \| 0.9762 \| 0.9648 \| 421 \| 0.8545 \| 0.9592 \| 0.9038 \| 49 \| 0.9419 \| 0.9898 \| 0.9652 \| 491 \| 0.25 \| 0.5455 \| 0.3429 \| 11 \| 0.8813 \| 0.9356 \| 0.9076 \| 0.9867 \| \| 0.0453 \| 2.47 \| 33000 \| 0.0610 \| 0.8929 \| 0.9615 \| 0.9259 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8732 \| 0.9538 \| 0.9118 \| 65 \| 0.5556 \| 0.7143 \| 0.6250 \| 7 \| 0.9057 \| 0.9320 \| 0.9187 \| 206 \| 0.9302 \| 0.9302 \| 0.9302 \| 43 \| 0.8668 \| 0.9098 \| 0.8878 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8882 \| 0.9375 \| 0.9122 \| 144 \| 0.9588 \| 0.9532 \| 0.9560 \| 171 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.9303 \| 0.9834 \| 0.9561 \| 421 \| 1.0 \| 0.8980 \| 0.9462 \| 49 \| 0.9455 \| 0.9898 \| 0.9672 \| 491 \| 0.7143 \| 0.4545 \| 0.5556 \| 11 \| 0.8952 \| 0.9353 \| 0.9148 \| 0.9875 \| \| 0.0225 \| 2.51 \| 33500 \| 0.0607 \| 0.9259 \| 0.9615 \| 0.9434 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.7949 \| 0.9538 \| 0.8671 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8733 \| 0.9369 \| 0.9040 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.8641 \| 0.9007 \| 0.8820 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8616 \| 0.9514 \| 0.9043 \| 144 \| 0.9412 \| 0.9357 \| 0.9384 \| 171 \| 0.3333 \| 1.0 \| 0.5 \| 3 \| 0.9281 \| 0.9810 \| 0.9538 \| 421 \| 0.8868 \| 0.9592 \| 0.9216 \| 49 \| 0.9365 \| 0.9919 \| 0.9634 \| 491 \| 0.4545 \| 0.4545 \| 0.4545 \| 11 \| 0.8844 \| 0.9323 \| 0.9077 \| 0.9876 \| \| 0.0276 \| 2.55 \| 34000 \| 0.0603 \| 0.8909 \| 0.9423 \| 0.9159 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.775 \| 0.9538 \| 0.8552 \| 65 \| 0.6667 \| 0.8571 \| 0.75 \| 7 \| 0.8894 \| 0.9369 \| 0.9125 \| 206 \| 0.9111 \| 0.9535 \| 0.9318 \| 43 \| 0.8661 \| 0.9201 \| 0.8923 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8688 \| 0.9653 \| 0.9145 \| 144 \| 0.9649 \| 0.9649 \| 0.9649 \| 171 \| 0.1667 \| 1.0 \| 0.2857 \| 3 \| 0.9649 \| 0.9786 \| 0.9717 \| 421 \| 0.9020 \| 0.9388 \| 0.92 \| 49 \| 0.9222 \| 0.9898 \| 0.9548 \| 491 \| 0.4545 \| 0.4545 \| 0.4545 \| 11 \| 0.8868 \| 0.9428 \| 0.9140 \| 0.9877 \| \| 0.0291 \| 2.59 \| 34500 \| 0.0605 \| 0.9074 \| 0.9423 \| 0.9245 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8378 \| 0.9538 \| 0.8921 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.9091 \| 0.9223 \| 0.9157 \| 206 \| 0.9524 \| 0.9302 \| 0.9412 \| 43 \| 0.8707 \| 0.9213 \| 0.8953 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8947 \| 0.9444 \| 0.9189 \| 144 \| 0.9758 \| 0.9415 \| 0.9583 \| 171 \| 0.25 \| 1.0 \| 0.4 \| 3 \| 0.9448 \| 0.9762 \| 0.9603 \| 421 \| 0.9787 \| 0.9388 \| 0.9583 \| 49 \| 0.8952 \| 0.9919 \| 0.9411 \| 491 \| 0.2632 \| 0.4545 \| 0.3333 \| 11 \| 0.8885 \| 0.9401 \| 0.9136 \| 0.9881 \| \| 0.0264 \| 2.62 \| 35000 \| 0.0616 \| 0.9074 \| 0.9423 \| 0.9245 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8493 \| 0.9538 \| 0.8986 \| 65 \| 0.75 \| 0.8571 \| 0.8000 \| 7 \| 0.9019 \| 0.9369 \| 0.9190 \| 206 \| 0.8913 \| 0.9535 \| 0.9213 \| 43 \| 0.8694 \| 0.9310 \| 0.8992 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8782 \| 0.9514 \| 0.9133 \| 144 \| 0.9422 \| 0.9532 \| 0.9477 \| 171 \| 0.25 \| 1.0 \| 0.4 \| 3 \| 0.9258 \| 0.9786 \| 0.9515 \| 421 \| 0.8679 \| 0.9388 \| 0.9020 \| 49 \| 0.9272 \| 0.9857 \| 0.9556 \| 491 \| 0.1852 \| 0.4545 \| 0.2632 \| 11 \| 0.8837 \| 0.9465 \| 0.9140 \| 0.9875 \| \| 0.0343 \| 2.66 \| 35500 \| 0.0595 \| 0.7083 \| 0.9808 \| 0.8226 \| 52 \| 0.6667 \| 0.8571 \| 0.75 \| 7 \| 0.7949 \| 0.9538 \| 0.8671 \| 65 \| 0.7143 \| 0.7143 \| 0.7143 \| 7 \| 0.8858 \| 0.9417 \| 0.9129 \| 206 \| 0.9091 \| 0.9302 \| 0.9195 \| 43 \| 0.8556 \| 0.9110 \| 0.8824 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8616 \| 0.9514 \| 0.9043 \| 144 \| 0.9270 \| 0.9649 \| 0.9456 \| 171 \| 1.0 \| 1.0 \| 1.0 \| 3 \| 0.9388 \| 0.9834 \| 0.9606 \| 421 \| 0.8868 \| 0.9592 \| 0.9216 \| 49 \| 0.8919 \| 0.9919 \| 0.9392 \| 491 \| 0.625 \| 0.4545 \| 0.5263 \| 11 \| 0.8728 \| 0.9389 \| 0.9046 \| 0.9871 \| \| 0.0284 \| 2.7 \| 36000 \| 0.0569 \| 0.9074 \| 0.9423 \| 0.9245 \| 52 \| 0.5385 \| 1.0 \| 0.7000 \| 7 \| 0.8052 \| 0.9538 \| 0.8732 \| 65 \| 0.5556 \| 0.7143 \| 0.6250 \| 7 \| 0.9143 \| 0.9320 \| 0.9231 \| 206 \| 0.9070 \| 0.9070 \| 0.9070 \| 43 \| 0.8724 \| 0.9189 \| 0.8950 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9145 \| 0.9653 \| 0.9392 \| 144 \| 0.9540 \| 0.9708 \| 0.9623 \| 171 \| 0.15 \| 1.0 \| 0.2609 \| 3 \| 0.9605 \| 0.9810 \| 0.9706 \| 421 \| 0.8364 \| 0.9388 \| 0.8846 \| 49 \| 0.8907 \| 0.9959 \| 0.9404 \| 491 \| 0.625 \| 0.4545 \| 0.5263 \| 11 \| 0.8865 \| 0.9425 \| 0.9137 \| 0.9878 \| \| 0.0377 \| 2.74 \| 36500 \| 0.0554 \| 0.7083 \| 0.9808 \| 0.8226 \| 52 \| 0.5833 \| 1.0 \| 0.7368 \| 7 \| 0.7654 \| 0.9538 \| 0.8493 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8981 \| 0.9417 \| 0.9194 \| 206 \| 0.9091 \| 0.9302 \| 0.9195 \| 43 \| 0.8700 \| 0.9237 \| 0.8961 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9205 \| 0.9653 \| 0.9424 \| 144 \| 0.9540 \| 0.9708 \| 0.9623 \| 171 \| 0.25 \| 1.0 \| 0.4 \| 3 \| 0.9063 \| 0.9881 \| 0.9455 \| 421 \| 0.9020 \| 0.9388 \| 0.92 \| 49 \| 0.8825 \| 0.9939 \| 0.9349 \| 491 \| 0.4545 \| 0.4545 \| 0.4545 \| 11 \| 0.8755 \| 0.9477 \| 0.9101 \| 0.9883 \| \| 0.0316 \| 2.77 \| 37000 \| 0.0562 \| 0.6711 \| 0.9808 \| 0.7969 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8052 \| 0.9538 \| 0.8732 \| 65 \| 0.6667 \| 0.8571 \| 0.75 \| 7 \| 0.9143 \| 0.9320 \| 0.9231 \| 206 \| 0.9524 \| 0.9302 \| 0.9412 \| 43 \| 0.8721 \| 0.9243 \| 0.8974 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.9026 \| 0.9653 \| 0.9329 \| 144 \| 0.9653 \| 0.9766 \| 0.9709 \| 171 \| 0.2143 \| 1.0 \| 0.3529 \| 3 \| 0.9202 \| 0.9857 \| 0.9518 \| 421 \| 0.8070 \| 0.9388 \| 0.8679 \| 49 \| 0.8954 \| 0.9939 \| 0.9421 \| 491 \| 0.5 \| 0.4545 \| 0.4762 \| 11 \| 0.8801 \| 0.9471 \| 0.9123 \| 0.9885 \| \| 0.0454 \| 2.81 \| 37500 \| 0.0555 \| 0.8333 \| 0.9615 \| 0.8929 \| 52 \| 0.5 \| 1.0 \| 0.6667 \| 7 \| 0.8052 \| 0.9538 \| 0.8732 \| 65 \| 0.6667 \| 0.8571 \| 0.75 \| 7 \| 0.9023 \| 0.9417 \| 0.9216 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.8782 \| 0.9249 \| 0.9009 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8642 \| 0.9722 \| 0.9150 \| 144 \| 0.9337 \| 0.9883 \| 0.9602 \| 171 \| 0.2143 \| 1.0 \| 0.3529 \| 3 \| 0.9498 \| 0.9881 \| 0.9686 \| 421 \| 0.94 \| 0.9592 \| 0.9495 \| 49 \| 0.8954 \| 0.9939 \| 0.9421 \| 491 \| 0.3125 \| 0.4545 \| 0.3704 \| 11 \| 0.8845 \| 0.9492 \| 0.9157 \| 0.9881 \| \| 0.0445 \| 2.85 \| 38000 \| 0.0521 \| 0.8889 \| 0.9231 \| 0.9057 \| 52 \| 0.5385 \| 1.0 \| 0.7000 \| 7 \| 0.8493 \| 0.9538 \| 0.8986 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9019 \| 0.9369 \| 0.9190 \| 206 \| 0.8511 \| 0.9302 \| 0.8889 \| 43 \| 0.8769 \| 0.9183 \| 0.8971 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8797 \| 0.9653 \| 0.9205 \| 144 \| 0.9767 \| 0.9825 \| 0.9796 \| 171 \| 0.25 \| 1.0 \| 0.4 \| 3 \| 0.9243 \| 0.9857 \| 0.9540 \| 421 \| 0.8103 \| 0.9592 \| 0.8785 \| 49 \| 0.8954 \| 0.9939 \| 0.9421 \| 491 \| 0.3125 \| 0.4545 \| 0.3704 \| 11 \| 0.8845 \| 0.9443 \| 0.9134 \| 0.9884 \| \| 0.0379 \| 2.89 \| 38500 \| 0.0524 \| 0.8727 \| 0.9231 \| 0.8972 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8611 \| 0.9538 \| 0.9051 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9147 \| 0.9369 \| 0.9257 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.8903 \| 0.9183 \| 0.9041 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8742 \| 0.9653 \| 0.9175 \| 144 \| 0.9711 \| 0.9825 \| 0.9767 \| 171 \| 0.2308 \| 1.0 \| 0.375 \| 3 \| 0.9101 \| 0.9857 \| 0.9464 \| 421 \| 0.8214 \| 0.9388 \| 0.8762 \| 49 \| 0.9067 \| 0.9898 \| 0.9464 \| 491 \| 0.3571 \| 0.4545 \| 0.4 \| 11 \| 0.8932 \| 0.9434 \| 0.9176 \| 0.9885 \| \| 0.0372 \| 2.92 \| 39000 \| 0.0514 \| 0.8889 \| 0.9231 \| 0.9057 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8378 \| 0.9538 \| 0.8921 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9279 \| 0.9369 \| 0.9324 \| 206 \| 0.8333 \| 0.9302 \| 0.8791 \| 43 \| 0.8857 \| 0.9195 \| 0.9023 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8910 \| 0.9653 \| 0.9267 \| 144 \| 0.9767 \| 0.9825 \| 0.9796 \| 171 \| 0.3333 \| 1.0 \| 0.5 \| 3 \| 0.9141 \| 0.9857 \| 0.9486 \| 421 \| 0.8545 \| 0.9592 \| 0.9038 \| 49 \| 0.9120 \| 0.9919 \| 0.9502 \| 491 \| 0.3333 \| 0.4545 \| 0.3846 \| 11 \| 0.8946 \| 0.9446 \| 0.9189 \| 0.9886 \| \| 0.0263 \| 2.96 \| 39500 \| 0.0515 \| 0.8889 \| 0.9231 \| 0.9057 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8378 \| 0.9538 \| 0.8921 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9190 \| 0.9369 \| 0.9279 \| 206 \| 0.8511 \| 0.9302 \| 0.8889 \| 43 \| 0.8868 \| 0.9201 \| 0.9031 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8910 \| 0.9653 \| 0.9267 \| 144 \| 0.9825 \| 0.9825 \| 0.9825 \| 171 \| 0.3 \| 1.0 \| 0.4615 \| 3 \| 0.9326 \| 0.9857 \| 0.9584 \| 421 \| 0.8868 \| 0.9592 \| 0.9216 \| 49 \| 0.9137 \| 0.9919 \| 0.9512 \| 491 \| 0.3571 \| 0.4545 \| 0.4 \| 11 \| 0.8982 \| 0.9449 \| 0.9210 \| 0.9885 \| \| 0.0242 \| 3.0 \| 40000 \| 0.0518 \| 0.8889 \| 0.9231 \| 0.9057 \| 52 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.8267 \| 0.9538 \| 0.8857 \| 65 \| 0.875 \| 1.0 \| 0.9333 \| 7 \| 0.9190 \| 0.9369 \| 0.9279 \| 206 \| 0.8696 \| 0.9302 \| 0.8989 \| 43 \| 0.8827 \| 0.9201 \| 0.9010 \| 1652 \| 0.0 \| 0.0 \| 0.0 \| 2 \| 0.8688 \| 0.9653 \| 0.9145 \| 144 \| 0.9825 \| 0.9825 \| 0.9825 \| 171 \| 0.2727 \| 1.0 \| 0.4286 \| 3 \| 0.9220 \| 0.9834 \| 0.9517 \| 421 \| 0.9038 \| 0.9592 \| 0.9307 \| 49 \| 0.9086 \| 0.9919 \| 0.9484 \| 491 \| 0.3846 \| 0.4545 \| 0.4167 \| 11 \| 0.8933 \| 0.9446 \| 0.9183 \| 0.9885 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
pig4431/Sentiment140_ALBERT_5E	pig4431	2022-11-07T07:45:04Z	105	0	transformers	[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "dataset:sentiment140", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T07:44:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - sentiment140 metrics: - accuracy model-index: - name: Sentiment140_ALBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: sentiment140 type: sentiment140 config: sentiment140 split: train args: sentiment140 metrics: - name: Accuracy type: accuracy value: 0.8533333333333334 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Sentiment140_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.6103 - Accuracy: 0.8533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6713 \| 0.08 \| 50 \| 0.5704 \| 0.7333 \| \| 0.5742 \| 0.16 \| 100 \| 0.4620 \| 0.8 \| \| 0.5104 \| 0.24 \| 150 \| 0.5536 \| 0.74 \| \| 0.5313 \| 0.32 \| 200 \| 0.5198 \| 0.76 \| \| 0.5023 \| 0.4 \| 250 \| 0.4286 \| 0.8 \| \| 0.4871 \| 0.48 \| 300 \| 0.4294 \| 0.8267 \| \| 0.4513 \| 0.56 \| 350 \| 0.4349 \| 0.8133 \| \| 0.4647 \| 0.64 \| 400 \| 0.4046 \| 0.8333 \| \| 0.4827 \| 0.72 \| 450 \| 0.4218 \| 0.8333 \| \| 0.4517 \| 0.8 \| 500 \| 0.4093 \| 0.82 \| \| 0.4417 \| 0.88 \| 550 \| 0.3999 \| 0.84 \| \| 0.4701 \| 0.96 \| 600 \| 0.3779 \| 0.8867 \| \| 0.397 \| 1.04 \| 650 \| 0.3730 \| 0.8667 \| \| 0.3377 \| 1.12 \| 700 \| 0.3833 \| 0.8333 \| \| 0.411 \| 1.2 \| 750 \| 0.3704 \| 0.84 \| \| 0.3796 \| 1.28 \| 800 \| 0.3472 \| 0.86 \| \| 0.3523 \| 1.36 \| 850 \| 0.3512 \| 0.8733 \| \| 0.3992 \| 1.44 \| 900 \| 0.3712 \| 0.84 \| \| 0.3641 \| 1.52 \| 950 \| 0.3718 \| 0.82 \| \| 0.3973 \| 1.6 \| 1000 \| 0.3508 \| 0.84 \| \| 0.3576 \| 1.68 \| 1050 \| 0.3600 \| 0.86 \| \| 0.3701 \| 1.76 \| 1100 \| 0.3287 \| 0.8667 \| \| 0.3721 \| 1.84 \| 1150 \| 0.3794 \| 0.82 \| \| 0.3673 \| 1.92 \| 1200 \| 0.3378 \| 0.8733 \| \| 0.4223 \| 2.0 \| 1250 \| 0.3508 \| 0.86 \| \| 0.2745 \| 2.08 \| 1300 \| 0.3835 \| 0.86 \| \| 0.283 \| 2.16 \| 1350 \| 0.3500 \| 0.8533 \| \| 0.2769 \| 2.24 \| 1400 \| 0.3334 \| 0.8733 \| \| 0.2491 \| 2.32 \| 1450 \| 0.3519 \| 0.8867 \| \| 0.3237 \| 2.4 \| 1500 \| 0.3438 \| 0.86 \| \| 0.2662 \| 2.48 \| 1550 \| 0.3513 \| 0.8667 \| \| 0.2423 \| 2.56 \| 1600 \| 0.3413 \| 0.8867 \| \| 0.2655 \| 2.64 \| 1650 \| 0.3126 \| 0.8933 \| \| 0.2516 \| 2.72 \| 1700 \| 0.3333 \| 0.8733 \| \| 0.252 \| 2.8 \| 1750 \| 0.3316 \| 0.88 \| \| 0.2872 \| 2.88 \| 1800 \| 0.3227 \| 0.9 \| \| 0.306 \| 2.96 \| 1850 \| 0.3383 \| 0.8733 \| \| 0.248 \| 3.04 \| 1900 \| 0.3474 \| 0.8733 \| \| 0.1507 \| 3.12 \| 1950 \| 0.4140 \| 0.8667 \| \| 0.1994 \| 3.2 \| 2000 \| 0.3729 \| 0.8533 \| \| 0.167 \| 3.28 \| 2050 \| 0.3782 \| 0.8867 \| \| 0.1872 \| 3.36 \| 2100 \| 0.4352 \| 0.8867 \| \| 0.1611 \| 3.44 \| 2150 \| 0.4511 \| 0.8667 \| \| 0.2338 \| 3.52 \| 2200 \| 0.4244 \| 0.8533 \| \| 0.1538 \| 3.6 \| 2250 \| 0.4226 \| 0.8733 \| \| 0.1561 \| 3.68 \| 2300 \| 0.4126 \| 0.88 \| \| 0.2156 \| 3.76 \| 2350 \| 0.4382 \| 0.86 \| \| 0.1684 \| 3.84 \| 2400 \| 0.4969 \| 0.86 \| \| 0.1917 \| 3.92 \| 2450 \| 0.4439 \| 0.8667 \| \| 0.1584 \| 4.0 \| 2500 \| 0.4759 \| 0.86 \| \| 0.1038 \| 4.08 \| 2550 \| 0.5042 \| 0.8667 \| \| 0.0983 \| 4.16 \| 2600 \| 0.5527 \| 0.8533 \| \| 0.1404 \| 4.24 \| 2650 \| 0.5801 \| 0.84 \| \| 0.0844 \| 4.32 \| 2700 \| 0.5884 \| 0.86 \| \| 0.1347 \| 4.4 \| 2750 \| 0.5865 \| 0.8467 \| \| 0.1373 \| 4.48 \| 2800 \| 0.5915 \| 0.8533 \| \| 0.1506 \| 4.56 \| 2850 \| 0.5976 \| 0.8467 \| \| 0.1007 \| 4.64 \| 2900 \| 0.6678 \| 0.82 \| \| 0.1311 \| 4.72 \| 2950 \| 0.6082 \| 0.8533 \| \| 0.1402 \| 4.8 \| 3000 \| 0.6180 \| 0.8467 \| \| 0.1363 \| 4.88 \| 3050 \| 0.6107 \| 0.8533 \| \| 0.0995 \| 4.96 \| 3100 \| 0.6103 \| 0.8533 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.1
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab	ntsema	2022-11-07T07:24:16Z	132	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-07T04:24:31Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Wer type: wer value: 0.2246858832224686 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Wer: 0.2247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.7431 \| 5.71 \| 400 \| 0.2879 \| 0.4054 \| \| 0.1876 \| 11.42 \| 800 \| 0.2349 \| 0.3023 \| \| 0.0986 \| 17.14 \| 1200 \| 0.2248 \| 0.2701 \| \| 0.0737 \| 22.85 \| 1600 \| 0.2242 \| 0.2428 \| \| 0.0546 \| 28.57 \| 2000 \| 0.2143 \| 0.2247 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.14.0.dev20221105+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1
tkubotake/xlm-roberta-base-finetuned-panx-en	tkubotake	2022-11-07T05:12:03Z	6	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T03:30:35Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: train args: PAN-X.en metrics: - name: F1 type: f1 value: 0.7580275229357799 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.5430 - F1: 0.7580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1318 \| 1.0 \| 50 \| 0.4145 \| 0.7557 \| \| 0.0589 \| 2.0 \| 100 \| 0.5016 \| 0.7524 \| \| 0.0314 \| 3.0 \| 150 \| 0.5430 \| 0.7580 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
tkubotake/xlm-roberta-base-finetuned-panx-it	tkubotake	2022-11-07T04:56:08Z	8	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T03:14:56Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: train args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8602239734549979 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2762 - F1: 0.8602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1073 \| 1.0 \| 70 \| 0.2783 \| 0.8554 \| \| 0.0728 \| 2.0 \| 140 \| 0.2651 \| 0.8605 \| \| 0.0409 \| 3.0 \| 210 \| 0.2762 \| 0.8602 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
huggingtweets/mhhmmad_	huggingtweets	2022-11-07T04:41:10Z	107	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T04:41:03Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1355122703036936198/SDlJIKsr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mohammad Hassan</div> <div style="text-align: center; font-size: 14px;">@mhhmmad_</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Mohammad Hassan. \| Data \| Mohammad Hassan \| \| --- \| --- \| \| Tweets downloaded \| 3017 \| \| Retweets \| 679 \| \| Short tweets \| 201 \| \| Tweets kept \| 2137 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wifnwvu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mhhmmad_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mhhmmad_') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
rymaju/t5-small-finetuned-en-to-regex	rymaju	2022-11-07T04:31:37Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-07T01:32:59Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-en-to-regex results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-en-to-regex This model is a fine-tuned version of [rymaju/t5-small-finetuned-en-to-regex](https://huggingface.co/rymaju/t5-small-finetuned-en-to-regex) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0032 - Bleu: 12.1984 - Gen Len: 16.7502 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.0092 \| 1.0 \| 6188 \| 0.0043 \| 12.1984 \| 16.7522 \| \| 0.0069 \| 2.0 \| 12376 \| 0.0040 \| 12.2039 \| 16.7502 \| \| 0.0056 \| 3.0 \| 18564 \| 0.0034 \| 12.2091 \| 16.7483 \| \| 0.0048 \| 4.0 \| 24752 \| 0.0035 \| 12.2103 \| 16.7502 \| \| 0.0049 \| 5.0 \| 30940 \| 0.0035 \| 12.1984 \| 16.7502 \| \| 0.0046 \| 6.0 \| 37128 \| 0.0033 \| 12.1984 \| 16.7502 \| \| 0.0046 \| 7.0 \| 43316 \| 0.0035 \| 12.1984 \| 16.7502 \| \| 0.0046 \| 8.0 \| 49504 \| 0.0032 \| 12.1984 \| 16.7502 \| \| 0.0042 \| 9.0 \| 55692 \| 0.0032 \| 12.1984 \| 16.7502 \| \| 0.0043 \| 10.0 \| 61880 \| 0.0032 \| 12.1984 \| 16.7502 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
jrtec/jrtec-gpt2-text-generation-quotes-jonathan-vargas	jrtec	2022-11-07T04:26:10Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "quotes", "quote", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-06T03:21:37Z	--- license: mit tags: - text-generation - quotes - quote - generated_from_trainer model-index: - name: jrtec-gpt2-text-generation-quotes-jonathan-vargas results: [] widget: - text: "life: " example_title: "Life quote" - text: "death: " example_title: "Death quote" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # jrtec-gpt2-text-generation-quotes-jonathan-vargas This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.7033 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7463 \| 1.71 \| 500 \| 0.7033 \| \| 0.4281 \| 3.41 \| 1000 \| 0.7084 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
Marve271/BartConditionalGeneration-bart-large-finetuned-insult	Marve271	2022-11-07T04:05:25Z	182	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-06T19:15:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: BartConditionalGeneration-bart-large-finetuned-insult results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BartConditionalGeneration-bart-large-finetuned-insult This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.6217 \| 1.0 \| 568 \| 4.5864 \| \| 4.7444 \| 2.0 \| 1136 \| nan \| \| 4.2308 \| 3.0 \| 1704 \| 3.7590 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
salascorp/categorizacion_comercios_v_0.0.7	salascorp	2022-11-07T03:24:01Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-07T02:51:40Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer metrics: - accuracy model-index: - name: categorizacion_comercios_v_0.0.7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # categorizacion_comercios_v_0.0.7 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Accuracy: 0.9125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
jinhybr/OCR-LayoutLMv3-Invoice	jinhybr	2022-11-07T02:11:33Z	113	7	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:wild_receipt", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-07T01:13:54Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - wild_receipt metrics: - precision - recall - f1 - accuracy model-index: - name: OCR-LayoutLMv3-Invoice results: - task: name: Token Classification type: token-classification dataset: name: wild_receipt type: wild_receipt config: WildReceipt split: train args: WildReceipt metrics: - name: Precision type: precision value: 0.8765398302764851 - name: Recall type: recall value: 0.8812439796339617 - name: F1 type: f1 value: 0.8788856103753516 - name: Accuracy type: accuracy value: 0.92678512668641 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OCR-LayoutLMv3-Invoice This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Precision: 0.8765 - Recall: 0.8812 - F1: 0.8789 - Accuracy: 0.9268 ## Model description More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 6000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.16 \| 100 \| 1.5032 \| 0.4934 \| 0.1444 \| 0.2234 \| 0.6064 \| \| No log \| 0.32 \| 200 \| 1.0282 \| 0.5884 \| 0.4420 \| 0.5048 \| 0.7385 \| \| No log \| 0.47 \| 300 \| 0.7856 \| 0.7448 \| 0.6205 \| 0.6770 \| 0.8133 \| \| No log \| 0.63 \| 400 \| 0.6464 \| 0.7736 \| 0.6689 \| 0.7174 \| 0.8399 \| \| 1.1733 \| 0.79 \| 500 \| 0.5672 \| 0.7609 \| 0.7303 \| 0.7453 \| 0.8557 \| \| 1.1733 \| 0.95 \| 600 \| 0.5055 \| 0.7658 \| 0.7652 \| 0.7655 \| 0.8677 \| \| 1.1733 \| 1.1 \| 700 \| 0.4735 \| 0.7946 \| 0.7848 \| 0.7897 \| 0.8784 \| \| 1.1733 \| 1.26 \| 800 \| 0.4414 \| 0.7962 \| 0.7946 \| 0.7954 \| 0.8818 \| \| 1.1733 \| 1.42 \| 900 \| 0.4094 \| 0.8176 \| 0.8064 \| 0.8120 \| 0.8894 \| \| 0.5047 \| 1.58 \| 1000 \| 0.3971 \| 0.8219 \| 0.8248 \| 0.8234 \| 0.8961 \| \| 0.5047 \| 1.74 \| 1100 \| 0.4082 \| 0.7993 \| 0.8362 \| 0.8174 \| 0.8927 \| \| 0.5047 \| 1.89 \| 1200 \| 0.3797 \| 0.8240 \| 0.8317 \| 0.8278 \| 0.8962 \| \| 0.5047 \| 2.05 \| 1300 \| 0.3597 \| 0.8326 \| 0.8331 \| 0.8329 \| 0.9020 \| \| 0.5047 \| 2.21 \| 1400 \| 0.3544 \| 0.8462 \| 0.8283 \| 0.8371 \| 0.9020 \| \| 0.368 \| 2.37 \| 1500 \| 0.3374 \| 0.8428 \| 0.8435 \| 0.8432 \| 0.9056 \| \| 0.368 \| 2.52 \| 1600 \| 0.3364 \| 0.8406 \| 0.8522 \| 0.8464 \| 0.9089 \| \| 0.368 \| 2.68 \| 1700 \| 0.3404 \| 0.8467 \| 0.8536 \| 0.8501 \| 0.9107 \| \| 0.368 \| 2.84 \| 1800 \| 0.3319 \| 0.8405 \| 0.8501 \| 0.8453 \| 0.9090 \| \| 0.368 \| 3.0 \| 1900 \| 0.3324 \| 0.8584 \| 0.8492 \| 0.8538 \| 0.9117 \| \| 0.2949 \| 3.15 \| 2000 \| 0.3204 \| 0.8691 \| 0.8404 \| 0.8545 \| 0.9119 \| \| 0.2949 \| 3.31 \| 2100 \| 0.3107 \| 0.8599 \| 0.8547 \| 0.8573 \| 0.9162 \| \| 0.2949 \| 3.47 \| 2200 \| 0.3169 \| 0.8680 \| 0.8489 \| 0.8584 \| 0.9146 \| \| 0.2949 \| 3.63 \| 2300 \| 0.3190 \| 0.8683 \| 0.8519 \| 0.8600 \| 0.9152 \| \| 0.2949 \| 3.79 \| 2400 \| 0.2975 \| 0.8631 \| 0.8617 \| 0.8624 \| 0.9182 \| \| 0.2438 \| 3.94 \| 2500 \| 0.3040 \| 0.8566 \| 0.8640 \| 0.8603 \| 0.9171 \| \| 0.2438 \| 4.1 \| 2600 \| 0.3045 \| 0.8585 \| 0.8642 \| 0.8613 \| 0.9181 \| \| 0.2438 \| 4.26 \| 2700 \| 0.3139 \| 0.8498 \| 0.8748 \| 0.8621 \| 0.9160 \| \| 0.2438 \| 4.42 \| 2800 \| 0.2985 \| 0.8642 \| 0.8672 \| 0.8657 \| 0.9214 \| \| 0.2438 \| 4.57 \| 2900 \| 0.3047 \| 0.8688 \| 0.8694 \| 0.8691 \| 0.9214 \| \| 0.2028 \| 4.73 \| 3000 \| 0.2986 \| 0.8686 \| 0.8695 \| 0.8691 \| 0.9207 \| \| 0.2028 \| 4.89 \| 3100 \| 0.3135 \| 0.8628 \| 0.8755 \| 0.8691 \| 0.9197 \| \| 0.2028 \| 5.05 \| 3200 \| 0.2927 \| 0.8656 \| 0.8755 \| 0.8705 \| 0.9217 \| \| 0.2028 \| 5.21 \| 3300 \| 0.2992 \| 0.8724 \| 0.8697 \| 0.8711 \| 0.9228 \| \| 0.2028 \| 5.36 \| 3400 \| 0.2975 \| 0.8831 \| 0.8639 \| 0.8734 \| 0.9244 \| \| 0.1814 \| 5.52 \| 3500 \| 0.2897 \| 0.8736 \| 0.8788 \| 0.8762 \| 0.9250 \| \| 0.1814 \| 5.68 \| 3600 \| 0.3118 \| 0.8674 \| 0.8751 \| 0.8712 \| 0.9216 \| \| 0.1814 \| 5.84 \| 3700 \| 0.2974 \| 0.8735 \| 0.8779 \| 0.8757 \| 0.9237 \| \| 0.1814 \| 5.99 \| 3800 \| 0.2957 \| 0.8696 \| 0.8815 \| 0.8755 \| 0.9240 \| \| 0.1814 \| 6.15 \| 3900 \| 0.3120 \| 0.8698 \| 0.8817 \| 0.8757 \| 0.9250 \| \| 0.1602 \| 6.31 \| 4000 \| 0.3080 \| 0.8715 \| 0.8800 \| 0.8757 \| 0.9238 \| \| 0.1602 \| 6.47 \| 4100 \| 0.3031 \| 0.8767 \| 0.8788 \| 0.8777 \| 0.9261 \| \| 0.1602 \| 6.62 \| 4200 \| 0.3146 \| 0.8699 \| 0.8784 \| 0.8741 \| 0.9227 \| \| 0.1602 \| 6.78 \| 4300 \| 0.3085 \| 0.8717 \| 0.8788 \| 0.8752 \| 0.9248 \| \| 0.1602 \| 6.94 \| 4400 \| 0.3023 \| 0.8749 \| 0.8756 \| 0.8752 \| 0.9250 \| \| 0.1383 \| 7.1 \| 4500 \| 0.3025 \| 0.8860 \| 0.8735 \| 0.8797 \| 0.9252 \| \| 0.1383 \| 7.26 \| 4600 \| 0.3026 \| 0.8775 \| 0.8810 \| 0.8792 \| 0.9272 \| \| 0.1383 \| 7.41 \| 4700 \| 0.3146 \| 0.8715 \| 0.8832 \| 0.8773 \| 0.9251 \| \| 0.1383 \| 7.57 \| 4800 \| 0.3113 \| 0.8769 \| 0.8803 \| 0.8786 \| 0.9275 \| \| 0.1383 \| 7.73 \| 4900 \| 0.3073 \| 0.8797 \| 0.8786 \| 0.8792 \| 0.9261 \| \| 0.1306 \| 7.89 \| 5000 \| 0.3163 \| 0.8714 \| 0.8828 \| 0.8770 \| 0.9248 \| \| 0.1306 \| 8.04 \| 5100 \| 0.3163 \| 0.8753 \| 0.8810 \| 0.8781 \| 0.9250 \| \| 0.1306 \| 8.2 \| 5200 \| 0.3132 \| 0.8743 \| 0.8804 \| 0.8773 \| 0.9257 \| \| 0.1306 \| 8.36 \| 5300 \| 0.3119 \| 0.8735 \| 0.8837 \| 0.8786 \| 0.9264 \| \| 0.1306 \| 8.52 \| 5400 \| 0.3145 \| 0.8826 \| 0.8779 \| 0.8802 \| 0.9272 \| \| 0.1174 \| 8.68 \| 5500 \| 0.3166 \| 0.8776 \| 0.8811 \| 0.8794 \| 0.9261 \| \| 0.1174 \| 8.83 \| 5600 \| 0.3146 \| 0.8776 \| 0.8814 \| 0.8795 \| 0.9260 \| \| 0.1174 \| 8.99 \| 5700 \| 0.3135 \| 0.8763 \| 0.8826 \| 0.8795 \| 0.9271 \| \| 0.1174 \| 9.15 \| 5800 \| 0.3154 \| 0.8794 \| 0.8818 \| 0.8806 \| 0.9275 \| \| 0.1174 \| 9.31 \| 5900 \| 0.3152 \| 0.8788 \| 0.8817 \| 0.8802 \| 0.9274 \| \| 0.11 \| 9.46 \| 6000 \| 0.3159 \| 0.8765 \| 0.8812 \| 0.8789 \| 0.9268 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1
huggingtweets/thebuddha_3	huggingtweets	2022-11-07T00:16:25Z	107	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-07T00:16:16Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421008625095647234/Vfg52xtV_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Buddha</div> <div style="text-align: center; font-size: 14px;">@thebuddha_3</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Buddha. \| Data \| Buddha \| \| --- \| --- \| \| Tweets downloaded \| 3200 \| \| Retweets \| 138 \| \| Short tweets \| 695 \| \| Tweets kept \| 2367 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/14lqj1g8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thebuddha_3's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rpocant) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rpocant/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/thebuddha_3') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/gleampt2-h3xenbrenner2-kidddozer	huggingtweets	2022-11-06T23:40:24Z	97	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-06T23:39:09Z	--- language: en thumbnail: http://www.huggingtweets.com/gleampt2-h3xenbrenner2-kidddozer/1667778020169/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509747695795118080/Vz0be-8x_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1380052646178996227/fmYX0h3D_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">h b & Pepper Boy & gleam</div> <div style="text-align: center; font-size: 14px;">@gleampt2-h3xenbrenner2-kidddozer</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from h b & Pepper Boy & gleam. \| Data \| h b \| Pepper Boy \| gleam \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 1231 \| 2848 \| 2305 \| \| Retweets \| 75 \| 690 \| 196 \| \| Short tweets \| 155 \| 442 \| 170 \| \| Tweets kept \| 1001 \| 1716 \| 1939 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336sqi28/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gleampt2-h3xenbrenner2-kidddozer's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gleampt2-h3xenbrenner2-kidddozer') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
kaejo98/bart-base_question_generation	kaejo98	2022-11-06T23:27:56Z	8	4	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-01T22:36:20Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base_question_generation results: [] --- # BART-base Question Generation This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>. ## Model description The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions: - <b> Casual-Generation </b>: where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/> Example: <br/> \<generate_questions\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity. - <b> Context-based-Generation </b>: given a section of a passage (context), the model is tasked to generate questions from the passage about the selected section or context. The input should be follow the structure or format: \<generate_context_questions\> \<section\> put your context here \</section\> paragraph: put your passage text here'. <br/> Example: <br/> \<generate_context_questions\> \<section\> Mohorovičić discontinuity \</section\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity. The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method. ## limitations The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data. ## Training and evaluation data The dataset used to train the model comprises the training datasets from: - Reasoning Over Paragraph Effects in Situations (ROPES): https://allenai.org/data/ropes - SQUAD: - DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop - SciQ After preprocessing the data from the above listed datasets, we had 408372 examples for training the model and 25k for development and 18k for testing. ## Training procedure The model is trained (finetuned) for 5 epochs with the hyperparameters listed below: ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 5 At the end of 5 epochs, the Evaluation loss was: 1.64 and the training loss was: 0.9671. ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1
ntsema/wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab	ntsema	2022-11-06T22:55:43Z	126	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-06T05:30:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Wer type: wer value: 1.0547550432276658 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.0548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.5109 \| 8.33 \| 400 \| inf \| 1.0807 \| \| 0.4252 \| 16.66 \| 800 \| inf \| 1.0519 \| \| 0.1744 \| 24.99 \| 1200 \| inf \| 1.0548 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.14.0.dev20221105+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1
halflings/house_price_prediction_ser2	halflings	2022-11-06T21:40:14Z	0	0	mlconsole	[ "mlconsole", "tabular-regression", "dataset:house_price_prediction", "license:unknown", "model-index", "region:us" ]	tabular-regression	2022-11-06T21:40:10Z	--- license: unknown inference: false tags: - mlconsole - tabular-regression library_name: mlconsole metrics: - mae - loss datasets: - house_price_prediction model-index: - name: house_price_prediction_ser2 results: - task: type: tabular-regression name: tabular-regression dataset: type: house_price_prediction name: house_price_prediction metrics: - type: mae name: Mean absolute error value: 5.011783599853516 - type: loss name: Model loss value: 43.01755905151367 --- # regression model trained on "house_price_prediction" 🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_ser2) in one click. 🧑‍💻 [Train your own model](https://mlconsole.com) on ML Console.
halflings/house_price_prediction_dev	halflings	2022-11-06T21:34:02Z	0	0	mlconsole	[ "mlconsole", "tabular-regression", "dataset:house_price_prediction", "license:unknown", "model-index", "region:us" ]	tabular-regression	2022-11-06T21:33:58Z	--- license: unknown inference: false tags: - mlconsole - tabular-regression library_name: mlconsole metrics: - mae - loss datasets: - house_price_prediction model-index: - name: house_price_prediction_dev results: - task: type: tabular-regression name: tabular-regression dataset: type: house_price_prediction name: house_price_prediction metrics: - type: mae name: Mean absolute error value: 7.064809322357178 - type: loss name: Model loss value: 98.9962387084961 --- # regression model trained on "house_price_prediction" 🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_dev) in one click. 🧑‍💻 [Train your own model](https://mlconsole.com) on ML Console.
lewtun/distilhubert-finetuned-gtzan	lewtun	2022-11-06T21:05:59Z	31	0	transformers	[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "hf-course", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-03-14T17:10:18Z	--- license: apache-2.0 tags: - hf-course - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6694 - Accuracy: 0.82 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.99 \| 56 \| 1.9426 \| 0.5 \| \| No log \| 1.99 \| 112 \| 1.4157 \| 0.63 \| \| No log \| 2.99 \| 168 \| 1.1351 \| 0.69 \| \| No log \| 3.99 \| 224 \| 1.0285 \| 0.72 \| \| No log \| 4.99 \| 280 \| 0.8538 \| 0.79 \| \| No log \| 5.99 \| 336 \| 0.8015 \| 0.74 \| \| No log \| 6.99 \| 392 \| 0.6694 \| 0.82 \| \| No log \| 7.99 \| 448 \| 0.6779 \| 0.79 \| \| 1.0811 \| 8.99 \| 504 \| 0.6414 \| 0.81 \| \| 1.0811 \| 9.99 \| 560 \| 0.6443 \| 0.82 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.6.1 - Tokenizers 0.11.6
pig4431/amazonPolarity_DistilBERT_5E	pig4431	2022-11-06T20:58:38Z	107	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_polarity", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-06T20:54:32Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_polarity metrics: - accuracy model-index: - name: amazonPolarity_DistilBERT_5EE results: - task: name: Text Classification type: text-classification dataset: name: amazon_polarity type: amazon_polarity config: amazon_polarity split: train args: amazon_polarity metrics: - name: Accuracy type: accuracy value: 0.94 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amazonPolarity_DistilBERT_5EE This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6581 \| 0.03 \| 50 \| 0.5315 \| 0.84 \| \| 0.4321 \| 0.05 \| 100 \| 0.2897 \| 0.8933 \| \| 0.298 \| 0.08 \| 150 \| 0.3165 \| 0.8667 \| \| 0.2902 \| 0.11 \| 200 \| 0.2552 \| 0.9067 \| \| 0.2824 \| 0.13 \| 250 \| 0.2277 \| 0.9133 \| \| 0.2522 \| 0.16 \| 300 \| 0.1998 \| 0.94 \| \| 0.2781 \| 0.19 \| 350 \| 0.1933 \| 0.94 \| \| 0.2668 \| 0.21 \| 400 \| 0.2316 \| 0.92 \| \| 0.2619 \| 0.24 \| 450 \| 0.1968 \| 0.9333 \| \| 0.2446 \| 0.27 \| 500 \| 0.1846 \| 0.9467 \| \| 0.2677 \| 0.29 \| 550 \| 0.1818 \| 0.94 \| \| 0.2026 \| 0.32 \| 600 \| 0.2348 \| 0.9133 \| \| 0.2351 \| 0.35 \| 650 \| 0.2127 \| 0.92 \| \| 0.2685 \| 0.37 \| 700 \| 0.1792 \| 0.94 \| \| 0.2141 \| 0.4 \| 750 \| 0.2252 \| 0.9133 \| \| 0.2193 \| 0.43 \| 800 \| 0.2131 \| 0.9267 \| \| 0.2456 \| 0.45 \| 850 \| 0.2205 \| 0.9133 \| \| 0.2548 \| 0.48 \| 900 \| 0.1788 \| 0.94 \| \| 0.2353 \| 0.51 \| 950 \| 0.1954 \| 0.9267 \| \| 0.2546 \| 0.53 \| 1000 \| 0.1815 \| 0.9333 \| \| 0.2583 \| 0.56 \| 1050 \| 0.1654 \| 0.9333 \| \| 0.219 \| 0.59 \| 1100 \| 0.1760 \| 0.9467 \| \| 0.2241 \| 0.61 \| 1150 \| 0.2107 \| 0.92 \| \| 0.2201 \| 0.64 \| 1200 \| 0.2381 \| 0.8933 \| \| 0.1745 \| 0.67 \| 1250 \| 0.1944 \| 0.92 \| \| 0.2698 \| 0.69 \| 1300 \| 0.1971 \| 0.9267 \| \| 0.214 \| 0.72 \| 1350 \| 0.1944 \| 0.9333 \| \| 0.2436 \| 0.75 \| 1400 \| 0.2079 \| 0.92 \| \| 0.2318 \| 0.77 \| 1450 \| 0.2088 \| 0.9333 \| \| 0.2206 \| 0.8 \| 1500 \| 0.1875 \| 0.94 \| \| 0.2593 \| 0.83 \| 1550 \| 0.1797 \| 0.9267 \| \| 0.1908 \| 0.85 \| 1600 \| 0.1924 \| 0.9333 \| \| 0.2378 \| 0.88 \| 1650 \| 0.1649 \| 0.9267 \| \| 0.2332 \| 0.91 \| 1700 \| 0.1768 \| 0.94 \| \| 0.2125 \| 0.93 \| 1750 \| 0.2276 \| 0.92 \| \| 0.2174 \| 0.96 \| 1800 \| 0.2035 \| 0.9333 \| \| 0.19 \| 0.99 \| 1850 \| 0.1805 \| 0.94 \| \| 0.1515 \| 1.01 \| 1900 \| 0.1832 \| 0.94 \| \| 0.1671 \| 1.04 \| 1950 \| 0.1902 \| 0.94 \| \| 0.171 \| 1.07 \| 2000 \| 0.2468 \| 0.9267 \| \| 0.1495 \| 1.09 \| 2050 \| 0.2276 \| 0.9267 \| \| 0.1535 \| 1.12 \| 2100 \| 0.1926 \| 0.94 \| \| 0.2085 \| 1.15 \| 2150 \| 0.1878 \| 0.94 \| \| 0.1395 \| 1.17 \| 2200 \| 0.1795 \| 0.9467 \| \| 0.1556 \| 1.2 \| 2250 \| 0.1554 \| 0.9467 \| \| 0.1273 \| 1.23 \| 2300 \| 0.1707 \| 0.94 \| \| 0.1873 \| 1.25 \| 2350 \| 0.1867 \| 0.9467 \| \| 0.1589 \| 1.28 \| 2400 \| 0.2089 \| 0.9333 \| \| 0.1426 \| 1.31 \| 2450 \| 0.1797 \| 0.9467 \| \| 0.149 \| 1.33 \| 2500 \| 0.1991 \| 0.9333 \| \| 0.1535 \| 1.36 \| 2550 \| 0.2116 \| 0.94 \| \| 0.1671 \| 1.39 \| 2600 \| 0.1704 \| 0.9467 \| \| 0.1582 \| 1.41 \| 2650 \| 0.1843 \| 0.94 \| \| 0.1393 \| 1.44 \| 2700 \| 0.1831 \| 0.94 \| \| 0.1474 \| 1.47 \| 2750 \| 0.1895 \| 0.94 \| \| 0.203 \| 1.49 \| 2800 \| 0.1843 \| 0.9467 \| \| 0.1562 \| 1.52 \| 2850 \| 0.2060 \| 0.9467 \| \| 0.1886 \| 1.55 \| 2900 \| 0.1837 \| 0.94 \| \| 0.1332 \| 1.57 \| 2950 \| 0.1920 \| 0.9467 \| \| 0.1519 \| 1.6 \| 3000 \| 0.1789 \| 0.9533 \| \| 0.1354 \| 1.63 \| 3050 \| 0.1974 \| 0.9467 \| \| 0.125 \| 1.65 \| 3100 \| 0.1890 \| 0.9533 \| \| 0.2044 \| 1.68 \| 3150 \| 0.1755 \| 0.9533 \| \| 0.1746 \| 1.71 \| 3200 \| 0.1607 \| 0.9467 \| \| 0.1981 \| 1.73 \| 3250 \| 0.1613 \| 0.9533 \| \| 0.1276 \| 1.76 \| 3300 \| 0.1825 \| 0.96 \| \| 0.1935 \| 1.79 \| 3350 \| 0.1707 \| 0.9533 \| \| 0.1848 \| 1.81 \| 3400 \| 0.1697 \| 0.96 \| \| 0.1596 \| 1.84 \| 3450 \| 0.1581 \| 0.9667 \| \| 0.1797 \| 1.87 \| 3500 \| 0.1634 \| 0.96 \| \| 0.1493 \| 1.89 \| 3550 \| 0.1614 \| 0.9533 \| \| 0.1703 \| 1.92 \| 3600 \| 0.1673 \| 0.9467 \| \| 0.1951 \| 1.95 \| 3650 \| 0.1589 \| 0.9533 \| \| 0.1582 \| 1.97 \| 3700 \| 0.1761 \| 0.9467 \| \| 0.1974 \| 2.0 \| 3750 \| 0.1918 \| 0.94 \| \| 0.1056 \| 2.03 \| 3800 \| 0.2063 \| 0.94 \| \| 0.1109 \| 2.05 \| 3850 \| 0.2031 \| 0.9467 \| \| 0.113 \| 2.08 \| 3900 \| 0.2118 \| 0.9467 \| \| 0.0834 \| 2.11 \| 3950 \| 0.1974 \| 0.9533 \| \| 0.1434 \| 2.13 \| 4000 \| 0.2075 \| 0.9533 \| \| 0.0691 \| 2.16 \| 4050 \| 0.2178 \| 0.9533 \| \| 0.1144 \| 2.19 \| 4100 \| 0.2383 \| 0.9467 \| \| 0.1446 \| 2.21 \| 4150 \| 0.2207 \| 0.9533 \| \| 0.172 \| 2.24 \| 4200 \| 0.2034 \| 0.9467 \| \| 0.1026 \| 2.27 \| 4250 \| 0.2048 \| 0.9467 \| \| 0.1131 \| 2.29 \| 4300 \| 0.2334 \| 0.9467 \| \| 0.121 \| 2.32 \| 4350 \| 0.2367 \| 0.9333 \| \| 0.1144 \| 2.35 \| 4400 \| 0.2313 \| 0.9467 \| \| 0.1089 \| 2.37 \| 4450 \| 0.2352 \| 0.9533 \| \| 0.1193 \| 2.4 \| 4500 \| 0.2440 \| 0.94 \| \| 0.0689 \| 2.43 \| 4550 \| 0.2379 \| 0.9333 \| \| 0.1799 \| 2.45 \| 4600 \| 0.2354 \| 0.9467 \| \| 0.1068 \| 2.48 \| 4650 \| 0.2158 \| 0.9533 \| \| 0.0974 \| 2.51 \| 4700 \| 0.2456 \| 0.94 \| \| 0.0637 \| 2.53 \| 4750 \| 0.2191 \| 0.9333 \| \| 0.1125 \| 2.56 \| 4800 \| 0.2390 \| 0.9467 \| \| 0.1706 \| 2.59 \| 4850 \| 0.2407 \| 0.94 \| \| 0.1533 \| 2.61 \| 4900 \| 0.2242 \| 0.9533 \| \| 0.1357 \| 2.64 \| 4950 \| 0.2119 \| 0.9533 \| \| 0.1342 \| 2.67 \| 5000 \| 0.2268 \| 0.9467 \| \| 0.0796 \| 2.69 \| 5050 \| 0.2450 \| 0.9467 \| \| 0.1351 \| 2.72 \| 5100 \| 0.2499 \| 0.94 \| \| 0.1285 \| 2.75 \| 5150 \| 0.2252 \| 0.94 \| \| 0.1563 \| 2.77 \| 5200 \| 0.2191 \| 0.94 \| \| 0.1022 \| 2.8 \| 5250 \| 0.2256 \| 0.9533 \| \| 0.11 \| 2.83 \| 5300 \| 0.2365 \| 0.9467 \| \| 0.0926 \| 2.85 \| 5350 \| 0.2206 \| 0.9467 \| \| 0.1043 \| 2.88 \| 5400 \| 0.2018 \| 0.9533 \| \| 0.1041 \| 2.91 \| 5450 \| 0.2268 \| 0.9467 \| \| 0.1232 \| 2.93 \| 5500 \| 0.2164 \| 0.9467 \| \| 0.1537 \| 2.96 \| 5550 \| 0.1956 \| 0.9533 \| \| 0.1188 \| 2.99 \| 5600 \| 0.2126 \| 0.9467 \| \| 0.0749 \| 3.01 \| 5650 \| 0.2249 \| 0.9467 \| \| 0.062 \| 3.04 \| 5700 \| 0.2254 \| 0.9467 \| \| 0.0755 \| 3.07 \| 5750 \| 0.2472 \| 0.94 \| \| 0.0866 \| 3.09 \| 5800 \| 0.2569 \| 0.94 \| \| 0.0502 \| 3.12 \| 5850 \| 0.2481 \| 0.9467 \| \| 0.1158 \| 3.15 \| 5900 \| 0.2457 \| 0.94 \| \| 0.0413 \| 3.17 \| 5950 \| 0.2500 \| 0.94 \| \| 0.0966 \| 3.2 \| 6000 \| 0.2851 \| 0.9333 \| \| 0.0613 \| 3.23 \| 6050 \| 0.2717 \| 0.9467 \| \| 0.1029 \| 3.25 \| 6100 \| 0.2714 \| 0.94 \| \| 0.0833 \| 3.28 \| 6150 \| 0.2683 \| 0.94 \| \| 0.0928 \| 3.31 \| 6200 \| 0.2490 \| 0.9467 \| \| 0.0571 \| 3.33 \| 6250 \| 0.2575 \| 0.9533 \| \| 0.1252 \| 3.36 \| 6300 \| 0.2599 \| 0.9467 \| \| 0.0788 \| 3.39 \| 6350 \| 0.2522 \| 0.9467 \| \| 0.0862 \| 3.41 \| 6400 \| 0.2489 \| 0.9533 \| \| 0.112 \| 3.44 \| 6450 \| 0.2452 \| 0.9533 \| \| 0.0868 \| 3.47 \| 6500 \| 0.2438 \| 0.9533 \| \| 0.0979 \| 3.49 \| 6550 \| 0.2474 \| 0.94 \| \| 0.0739 \| 3.52 \| 6600 \| 0.2508 \| 0.94 \| \| 0.0786 \| 3.55 \| 6650 \| 0.2621 \| 0.94 \| \| 0.0872 \| 3.57 \| 6700 \| 0.2543 \| 0.9333 \| \| 0.0962 \| 3.6 \| 6750 \| 0.2347 \| 0.9467 \| \| 0.124 \| 3.63 \| 6800 \| 0.2319 \| 0.9533 \| \| 0.0747 \| 3.65 \| 6850 \| 0.2448 \| 0.9533 \| \| 0.0591 \| 3.68 \| 6900 \| 0.2379 \| 0.94 \| \| 0.1049 \| 3.71 \| 6950 \| 0.2493 \| 0.9333 \| \| 0.0772 \| 3.73 \| 7000 \| 0.2429 \| 0.94 \| \| 0.071 \| 3.76 \| 7050 \| 0.2558 \| 0.94 \| \| 0.1116 \| 3.79 \| 7100 \| 0.2600 \| 0.94 \| \| 0.1199 \| 3.81 \| 7150 \| 0.2480 \| 0.94 \| \| 0.0819 \| 3.84 \| 7200 \| 0.2506 \| 0.94 \| \| 0.1054 \| 3.87 \| 7250 \| 0.2431 \| 0.94 \| \| 0.09 \| 3.89 \| 7300 \| 0.2582 \| 0.9333 \| \| 0.0936 \| 3.92 \| 7350 \| 0.2460 \| 0.94 \| \| 0.0469 \| 3.95 \| 7400 \| 0.2509 \| 0.94 \| \| 0.1101 \| 3.97 \| 7450 \| 0.2545 \| 0.9467 \| \| 0.1077 \| 4.0 \| 7500 \| 0.2640 \| 0.9467 \| \| 0.0777 \| 4.03 \| 7550 \| 0.2709 \| 0.94 \| \| 0.0777 \| 4.05 \| 7600 \| 0.2842 \| 0.94 \| \| 0.0847 \| 4.08 \| 7650 \| 0.2649 \| 0.94 \| \| 0.0462 \| 4.11 \| 7700 \| 0.2702 \| 0.9467 \| \| 0.0572 \| 4.13 \| 7750 \| 0.2628 \| 0.94 \| \| 0.0435 \| 4.16 \| 7800 \| 0.2689 \| 0.9467 \| \| 0.0566 \| 4.19 \| 7850 \| 0.2727 \| 0.9467 \| \| 0.1149 \| 4.21 \| 7900 \| 0.2635 \| 0.9467 \| \| 0.0557 \| 4.24 \| 7950 \| 0.2665 \| 0.9467 \| \| 0.061 \| 4.27 \| 8000 \| 0.2680 \| 0.9467 \| \| 0.0664 \| 4.29 \| 8050 \| 0.2767 \| 0.9467 \| \| 0.0481 \| 4.32 \| 8100 \| 0.2662 \| 0.9467 \| \| 0.0893 \| 4.35 \| 8150 \| 0.2677 \| 0.9467 \| \| 0.0855 \| 4.37 \| 8200 \| 0.2733 \| 0.9467 \| \| 0.0552 \| 4.4 \| 8250 \| 0.2589 \| 0.94 \| \| 0.0469 \| 4.43 \| 8300 \| 0.2733 \| 0.94 \| \| 0.0633 \| 4.45 \| 8350 \| 0.2799 \| 0.94 \| \| 0.0629 \| 4.48 \| 8400 \| 0.2838 \| 0.94 \| \| 0.0854 \| 4.51 \| 8450 \| 0.2837 \| 0.94 \| \| 0.0596 \| 4.53 \| 8500 \| 0.2808 \| 0.94 \| \| 0.0579 \| 4.56 \| 8550 \| 0.2839 \| 0.94 \| \| 0.0508 \| 4.59 \| 8600 \| 0.2844 \| 0.94 \| \| 0.0557 \| 4.61 \| 8650 \| 0.2833 \| 0.94 \| \| 0.0383 \| 4.64 \| 8700 \| 0.2878 \| 0.94 \| \| 0.0554 \| 4.67 \| 8750 \| 0.2924 \| 0.94 \| \| 0.0681 \| 4.69 \| 8800 \| 0.2868 \| 0.94 \| \| 0.065 \| 4.72 \| 8850 \| 0.2888 \| 0.94 \| \| 0.0731 \| 4.75 \| 8900 \| 0.2946 \| 0.94 \| \| 0.0638 \| 4.77 \| 8950 \| 0.2886 \| 0.94 \| \| 0.043 \| 4.8 \| 9000 \| 0.2867 \| 0.94 \| \| 0.0658 \| 4.83 \| 9050 \| 0.2872 \| 0.94 \| \| 0.0249 \| 4.85 \| 9100 \| 0.2882 \| 0.94 \| \| 0.0612 \| 4.88 \| 9150 \| 0.2902 \| 0.94 \| \| 0.0271 \| 4.91 \| 9200 \| 0.2890 \| 0.94 \| \| 0.0308 \| 4.93 \| 9250 \| 0.2897 \| 0.94 \| \| 0.0896 \| 4.96 \| 9300 \| 0.2898 \| 0.94 \| \| 0.1172 \| 4.99 \| 9350 \| 0.2899 \| 0.94 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
thaonguyen274/vit-base-patch16-224-finetuned-imageclassification	thaonguyen274	2022-11-06T20:55:55Z	28	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-06T16:57:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-imageclassification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9501779359430605 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-finetuned-imageclassification This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1790 - Accuracy: 0.9502 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 9 \| 0.5791 \| 0.9004 \| \| 1.4122 \| 2.0 \| 18 \| 0.2002 \| 0.9359 \| \| 0.3147 \| 3.0 \| 27 \| 0.1717 \| 0.9502 \| \| 0.1907 \| 4.0 \| 36 \| 0.1632 \| 0.9466 \| \| 0.158 \| 5.0 \| 45 \| 0.1822 \| 0.9466 \| \| 0.1169 \| 6.0 \| 54 \| 0.1778 \| 0.9502 \| \| 0.0984 \| 7.0 \| 63 \| 0.1552 \| 0.9573 \| \| 0.0971 \| 8.0 \| 72 \| 0.1835 \| 0.9502 \| \| 0.0965 \| 9.0 \| 81 \| 0.1878 \| 0.9484 \| \| 0.0766 \| 10.0 \| 90 \| 0.1790 \| 0.9502 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

giulio86/65

giulio86

2022-11-08T20:18:43Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-11-08T20:18:43Z

--- license: creativeml-openrail-m ---

huggingtweets/big___oven-codeinecucumber

huggingtweets

2022-11-08T19:32:56Z

103

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-25T19:41:48Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1579203041764442116/RSLookYD_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1571653458972794884/eaxhUsib_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Gutted & oskcar</div> <div style="text-align: center; font-size: 14px;">@big___oven-codeinecucumber</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Gutted & oskcar. | Data | Gutted | oskcar | | --- | --- | --- | | Tweets downloaded | 1761 | 2669 | | Retweets | 243 | 635 | | Short tweets | 326 | 308 | | Tweets kept | 1192 | 1726 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1qyf2pl5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @big___oven-codeinecucumber's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2rr9twhn) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2rr9twhn/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/big___oven-codeinecucumber') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base

gary109

2022-11-08T19:17:51Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "dataset:ai_light_dance", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-07T10:38:40Z

--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer datasets: - ai_light_dance model-index: - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base results: [] ---  # ai-light-dance_drums_ft_pretrain_wav2vec2-base This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base) on the GARY109/AI_LIGHT_DANCE - ONSET-DRUMS dataset. It achieves the following results on the evaluation set: - Loss: 1.8991 - Wer: 0.6046 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.9 | 8 | 2.0434 | 0.6226 | | 0.4739 | 1.9 | 16 | 2.1024 | 0.6247 | | 0.4693 | 2.9 | 24 | 1.9824 | 0.6211 | | 0.5139 | 3.9 | 32 | 2.2962 | 0.6429 | | 0.5081 | 4.9 | 40 | 2.2201 | 0.6292 | | 0.5081 | 5.9 | 48 | 2.1399 | 0.6208 | | 0.5785 | 6.9 | 56 | 2.1451 | 0.6417 | | 0.533 | 7.9 | 64 | 2.1184 | 0.6330 | | 0.5141 | 8.9 | 72 | 2.0230 | 0.6342 | | 0.4971 | 9.9 | 80 | 2.2137 | 0.6381 | | 0.4971 | 10.9 | 88 | 2.1159 | 0.6253 | | 0.5645 | 11.9 | 96 | 2.0966 | 0.6247 | | 0.4932 | 12.9 | 104 | 1.9249 | 0.6223 | | 0.4918 | 13.9 | 112 | 2.0445 | 0.6235 | | 0.5053 | 14.9 | 120 | 2.1317 | 0.6304 | | 0.5053 | 15.9 | 128 | 2.0723 | 0.6256 | | 0.5565 | 16.9 | 136 | 2.1390 | 0.6402 | | 0.4819 | 17.9 | 144 | 1.9556 | 0.6321 | | 0.5131 | 18.9 | 152 | 1.9886 | 0.6333 | | 0.4798 | 19.9 | 160 | 1.9700 | 0.6259 | | 0.4798 | 20.9 | 168 | 1.9771 | 0.6295 | | 0.5221 | 21.9 | 176 | 1.9880 | 0.6235 | | 0.4862 | 22.9 | 184 | 2.0994 | 0.6298 | | 0.4831 | 23.9 | 192 | 2.0521 | 0.6205 | | 0.4952 | 24.9 | 200 | 1.9838 | 0.6064 | | 0.4952 | 25.9 | 208 | 2.0319 | 0.6103 | | 0.5119 | 26.9 | 216 | 2.0419 | 0.6160 | | 0.4996 | 27.9 | 224 | 2.0073 | 0.6178 | | 0.488 | 28.9 | 232 | 2.1740 | 0.6304 | | 0.4978 | 29.9 | 240 | 2.2731 | 0.6163 | | 0.4978 | 30.9 | 248 | 2.2420 | 0.6205 | | 0.5259 | 31.9 | 256 | 2.0561 | 0.6184 | | 0.47 | 32.9 | 264 | 1.9455 | 0.6136 | | 0.5132 | 33.9 | 272 | 1.9307 | 0.6043 | | 0.4972 | 34.9 | 280 | 2.0536 | 0.6127 | | 0.4972 | 35.9 | 288 | 1.9113 | 0.6223 | | 0.5147 | 36.9 | 296 | 1.9317 | 0.6286 | | 0.4914 | 37.9 | 304 | 2.1810 | 0.6241 | | 0.472 | 38.9 | 312 | 2.1403 | 0.6160 | | 0.4825 | 39.9 | 320 | 2.1141 | 0.6094 | | 0.4825 | 40.9 | 328 | 2.2870 | 0.6031 | | 0.5138 | 41.9 | 336 | 2.1404 | 0.6181 | | 0.48 | 42.9 | 344 | 2.0243 | 0.6265 | | 0.4598 | 43.9 | 352 | 2.1117 | 0.6199 | | 0.474 | 44.9 | 360 | 2.0378 | 0.6321 | | 0.474 | 45.9 | 368 | 2.1919 | 0.6211 | | 0.4933 | 46.9 | 376 | 2.3645 | 0.6109 | | 0.4692 | 47.9 | 384 | 2.1920 | 0.6076 | | 0.4716 | 48.9 | 392 | 2.3663 | 0.6034 | | 0.4601 | 49.9 | 400 | 2.2838 | 0.6280 | | 0.4601 | 50.9 | 408 | 2.0287 | 0.6148 | | 0.4891 | 51.9 | 416 | 2.1346 | 0.6130 | | 0.4506 | 52.9 | 424 | 2.1556 | 0.6181 | | 0.4581 | 53.9 | 432 | 2.0560 | 0.6229 | | 0.4485 | 54.9 | 440 | 1.9944 | 0.5971 | | 0.4485 | 55.9 | 448 | 1.9791 | 0.6097 | | 0.4942 | 56.9 | 456 | 2.1166 | 0.6070 | | 0.4748 | 57.9 | 464 | 2.0271 | 0.6124 | | 0.4229 | 58.9 | 472 | 2.0437 | 0.6229 | | 0.45 | 59.9 | 480 | 2.1012 | 0.6142 | | 0.45 | 60.9 | 488 | 1.9151 | 0.6049 | | 0.4936 | 61.9 | 496 | 1.8991 | 0.6046 | | 0.4602 | 62.9 | 504 | 1.9813 | 0.6112 | | 0.4626 | 63.9 | 512 | 1.9372 | 0.6136 | | 0.445 | 64.9 | 520 | 1.9060 | 0.6154 | | 0.445 | 65.9 | 528 | 1.9574 | 0.6151 | | 0.4907 | 66.9 | 536 | 2.0947 | 0.6022 | | 0.4723 | 67.9 | 544 | 2.0061 | 0.6010 | | 0.4103 | 68.9 | 552 | 1.9557 | 0.6094 | | 0.4808 | 69.9 | 560 | 2.1042 | 0.6088 | | 0.4808 | 70.9 | 568 | 2.1360 | 0.6073 | | 0.4682 | 71.9 | 576 | 2.1290 | 0.6013 | | 0.4472 | 72.9 | 584 | 1.9454 | 0.5989 | | 0.4259 | 73.9 | 592 | 2.0937 | 0.6043 | | 0.4464 | 74.9 | 600 | 2.0822 | 0.6058 | | 0.4464 | 75.9 | 608 | 2.0128 | 0.6058 | | 0.4775 | 76.9 | 616 | 1.9744 | 0.6094 | | 0.4394 | 77.9 | 624 | 1.9992 | 0.6010 | | 0.418 | 78.9 | 632 | 2.1693 | 0.5947 | | 0.4384 | 79.9 | 640 | 2.1326 | 0.5923 | | 0.4384 | 80.9 | 648 | 2.1151 | 0.5950 | | 0.4971 | 81.9 | 656 | 2.1581 | 0.5923 | | 0.4176 | 82.9 | 664 | 2.0876 | 0.6013 | | 0.4312 | 83.9 | 672 | 2.1316 | 0.5935 | | 0.4408 | 84.9 | 680 | 2.2627 | 0.5971 | | 0.4408 | 85.9 | 688 | 2.2799 | 0.6112 | | 0.4678 | 86.9 | 696 | 2.1239 | 0.5989 | | 0.4288 | 87.9 | 704 | 2.1574 | 0.5983 | | 0.4157 | 88.9 | 712 | 2.2125 | 0.5908 | | 0.444 | 89.9 | 720 | 2.0542 | 0.5986 | | 0.444 | 90.9 | 728 | 2.0899 | 0.5920 | | 0.4694 | 91.9 | 736 | 2.1122 | 0.6076 | | 0.4314 | 92.9 | 744 | 2.0634 | 0.5950 | | 0.4348 | 93.9 | 752 | 2.0333 | 0.6046 | | 0.4558 | 94.9 | 760 | 2.1188 | 0.5956 | | 0.4558 | 95.9 | 768 | 2.0606 | 0.5995 | | 0.461 | 96.9 | 776 | 2.0600 | 0.5971 | | 0.4258 | 97.9 | 784 | 2.0479 | 0.6040 | | 0.4395 | 98.9 | 792 | 2.1282 | 0.6055 | | 0.4282 | 99.9 | 800 | 2.0593 | 0.6043 | | 0.4282 | 100.9 | 808 | 2.0592 | 0.5920 | | 0.4623 | 101.9 | 816 | 2.0852 | 0.5944 | | 0.4392 | 102.9 | 824 | 2.2024 | 0.5920 | | 0.4308 | 103.9 | 832 | 2.1786 | 0.5935 | | 0.4375 | 104.9 | 840 | 2.1085 | 0.5911 | | 0.4375 | 105.9 | 848 | 2.0724 | 0.5974 | | 0.4501 | 106.9 | 856 | 2.1306 | 0.5881 | | 0.4273 | 107.9 | 864 | 2.1340 | 0.5899 | | 0.4234 | 108.9 | 872 | 2.1125 | 0.5980 | | 0.4289 | 109.9 | 880 | 2.0526 | 0.6007 | | 0.4289 | 110.9 | 888 | 2.0955 | 0.5884 | | 0.478 | 111.9 | 896 | 2.1146 | 0.5872 | | 0.4143 | 112.9 | 904 | 2.2310 | 0.5899 | | 0.4193 | 113.9 | 912 | 2.2165 | 0.5899 | | 0.4159 | 114.9 | 920 | 2.1631 | 0.5941 | | 0.4159 | 115.9 | 928 | 2.1371 | 0.5938 | | 0.4776 | 116.9 | 936 | 2.0972 | 0.5935 | | 0.4143 | 117.9 | 944 | 2.1248 | 0.5917 | | 0.4022 | 118.9 | 952 | 2.1317 | 0.5956 | | 0.4346 | 119.9 | 960 | 2.1237 | 0.5992 | | 0.4346 | 120.9 | 968 | 2.0684 | 0.5935 | | 0.4564 | 121.9 | 976 | 2.0722 | 0.5947 | | 0.4243 | 122.9 | 984 | 2.1361 | 0.5884 | | 0.413 | 123.9 | 992 | 2.1207 | 0.5893 | | 0.4113 | 124.9 | 1000 | 2.0697 | 0.5837 | | 0.4113 | 125.9 | 1008 | 2.1005 | 0.5875 | | 0.4426 | 126.9 | 1016 | 2.0822 | 0.5870 | | 0.4255 | 127.9 | 1024 | 2.0572 | 0.5959 | | 0.4214 | 128.9 | 1032 | 2.0343 | 0.5935 | | 0.4042 | 129.9 | 1040 | 2.0282 | 0.5902 | | 0.4042 | 130.9 | 1048 | 2.0314 | 0.5846 | | 0.4515 | 131.9 | 1056 | 2.0621 | 0.5870 | | 0.4138 | 132.9 | 1064 | 2.0704 | 0.5938 | | 0.4289 | 133.9 | 1072 | 2.0222 | 0.5896 | | 0.3908 | 134.9 | 1080 | 2.0879 | 0.5855 | | 0.3908 | 135.9 | 1088 | 2.1068 | 0.5822 | | 0.4489 | 136.9 | 1096 | 2.0702 | 0.5837 | | 0.4191 | 137.9 | 1104 | 2.1093 | 0.5881 | | 0.4149 | 138.9 | 1112 | 2.1046 | 0.5819 | | 0.4127 | 139.9 | 1120 | 2.1729 | 0.5777 | | 0.4127 | 140.9 | 1128 | 2.1636 | 0.5810 | | 0.4449 | 141.9 | 1136 | 2.1515 | 0.5786 | | 0.3977 | 142.9 | 1144 | 2.1531 | 0.5774 | | 0.4121 | 143.9 | 1152 | 2.0857 | 0.5816 | | 0.4363 | 144.9 | 1160 | 2.1372 | 0.5822 | | 0.4363 | 145.9 | 1168 | 2.1902 | 0.5828 | | 0.4318 | 146.9 | 1176 | 2.1465 | 0.5831 | | 0.4112 | 147.9 | 1184 | 2.0697 | 0.5858 | | 0.4292 | 148.9 | 1192 | 2.0850 | 0.5837 | | 0.4182 | 149.9 | 1200 | 2.1171 | 0.5846 | | 0.4182 | 150.9 | 1208 | 2.1020 | 0.5867 | | 0.4381 | 151.9 | 1216 | 2.1052 | 0.5849 | | 0.4235 | 152.9 | 1224 | 2.1430 | 0.5864 | | 0.4173 | 153.9 | 1232 | 2.1131 | 0.5834 | | 0.3927 | 154.9 | 1240 | 2.1134 | 0.5846 | | 0.3927 | 155.9 | 1248 | 2.1173 | 0.5846 | | 0.4492 | 156.9 | 1256 | 2.0772 | 0.5801 | | 0.4313 | 157.9 | 1264 | 2.0309 | 0.5861 | | 0.4015 | 158.9 | 1272 | 2.0887 | 0.5819 | | 0.4268 | 159.9 | 1280 | 2.1812 | 0.5849 | | 0.4268 | 160.9 | 1288 | 2.1568 | 0.5881 | | 0.4496 | 161.9 | 1296 | 2.0805 | 0.5801 | | 0.4121 | 162.9 | 1304 | 2.0461 | 0.5872 | | 0.401 | 163.9 | 1312 | 2.0377 | 0.5864 | | 0.4192 | 164.9 | 1320 | 2.0183 | 0.5872 | | 0.4192 | 165.9 | 1328 | 2.0107 | 0.5855 | | 0.4466 | 166.9 | 1336 | 2.0528 | 0.5881 | | 0.3981 | 167.9 | 1344 | 2.0511 | 0.5878 | | 0.3967 | 168.9 | 1352 | 2.0374 | 0.5867 | | 0.4072 | 169.9 | 1360 | 2.0554 | 0.5867 | | 0.4072 | 170.9 | 1368 | 2.0388 | 0.5858 | | 0.4581 | 171.9 | 1376 | 2.0188 | 0.5914 | | 0.3937 | 172.9 | 1384 | 1.9999 | 0.5852 | | 0.4074 | 173.9 | 1392 | 1.9738 | 0.5840 | | 0.4085 | 174.9 | 1400 | 2.0090 | 0.5843 | | 0.4085 | 175.9 | 1408 | 1.9990 | 0.5864 | | 0.4224 | 176.9 | 1416 | 2.0391 | 0.5852 | | 0.4471 | 177.9 | 1424 | 2.0262 | 0.5855 | | 0.4233 | 178.9 | 1432 | 2.0621 | 0.5801 | | 0.409 | 179.9 | 1440 | 2.0486 | 0.5846 | | 0.409 | 180.9 | 1448 | 2.0508 | 0.5807 | | 0.4518 | 181.9 | 1456 | 2.0241 | 0.5887 | | 0.4077 | 182.9 | 1464 | 2.0169 | 0.5843 | | 0.4197 | 183.9 | 1472 | 2.0014 | 0.5896 | | 0.4237 | 184.9 | 1480 | 2.0189 | 0.5843 | | 0.4237 | 185.9 | 1488 | 2.0095 | 0.5867 | | 0.4394 | 186.9 | 1496 | 1.9993 | 0.5884 | | 0.4299 | 187.9 | 1504 | 2.0097 | 0.5899 | | 0.4198 | 188.9 | 1512 | 2.0049 | 0.5870 | | 0.4116 | 189.9 | 1520 | 1.9899 | 0.5875 | | 0.4116 | 190.9 | 1528 | 1.9814 | 0.5881 | | 0.445 | 191.9 | 1536 | 1.9820 | 0.5887 | | 0.4198 | 192.9 | 1544 | 1.9838 | 0.5881 | | 0.4065 | 193.9 | 1552 | 1.9849 | 0.5884 | | 0.3917 | 194.9 | 1560 | 1.9803 | 0.5867 | | 0.3917 | 195.9 | 1568 | 1.9777 | 0.5881 | | 0.4239 | 196.9 | 1576 | 1.9752 | 0.5875 | | 0.4183 | 197.9 | 1584 | 1.9766 | 0.5872 | | 0.3965 | 198.9 | 1592 | 1.9773 | 0.5872 | | 0.4144 | 199.9 | 1600 | 1.9781 | 0.5872 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

mrahusain/q-FrozenLake-v1-4x4-noSlippery

mrahusain

2022-11-08T18:29:40Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-11-08T18:29:34Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="mrahusain/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

ArafatBHossain/bert_uncased_fine_tuned_emotion_dataset

ArafatBHossain

2022-11-08T18:17:50Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-14T05:38:48Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_uncased_fine_tuned_emotion_dataset results: [] ---  # bert_uncased_fine_tuned_emotion_dataset This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1870 - Accuracy: 0.943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2321 | 1.0 | 2000 | 0.2690 | 0.924 | | 0.1483 | 2.0 | 4000 | 0.1683 | 0.9415 | | 0.0954 | 3.0 | 6000 | 0.1870 | 0.943 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

philschmid/pyannote-segmentation

philschmid

2022-11-08T17:15:47Z

1,078

8

pyannote-audio

[ "pyannote-audio", "pytorch", "pyannote", "pyannote-audio-model", "audio", "voice", "speech", "speaker", "speaker-segmentation", "voice-activity-detection", "overlapped-speech-detection", "resegmentation", "dataset:ami", "dataset:dihard", "dataset:voxconverse", "arxiv:2104.04045", "license:mit", "region:us" ]

voice-activity-detection

2022-11-08T17:13:14Z

--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-segmentation - voice-activity-detection - overlapped-speech-detection - resegmentation datasets: - ami - dihard - voxconverse license: mit inference: false --- # 🎹 Speaker segmentation ![Example](example.png) Model from *[End-to-end speaker segmentation for overlap-aware resegmentation](http://arxiv.org/abs/2104.04045)*, by Hervé Bredin and Antoine Laurent. [Online demo](https://huggingface.co/spaces/pyannote/pretrained-pipelines) is available as a Hugging Face Space. ## Support For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]). For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository. ## Usage Relies on pyannote.audio 2.0 currently in development: see [installation instructions](https://github.com/pyannote/pyannote-audio/tree/develop#installation). ### Voice activity detection ```python from pyannote.audio.pipelines import VoiceActivityDetection pipeline = VoiceActivityDetection(segmentation="pyannote/segmentation") HYPER_PARAMETERS = { # onset/offset activation thresholds "onset": 0.5, "offset": 0.5, # remove speech regions shorter than that many seconds. "min_duration_on": 0.0, # fill non-speech regions shorter than that many seconds. "min_duration_off": 0.0 } pipeline.instantiate(HYPER_PARAMETERS) vad = pipeline("audio.wav") # `vad` is a pyannote.core.Annotation instance containing speech regions ``` ### Overlapped speech detection ```python from pyannote.audio.pipelines import OverlappedSpeechDetection pipeline = OverlappedSpeechDetection(segmentation="pyannote/segmentation") pipeline.instantiate(HYPER_PARAMETERS) osd = pipeline("audio.wav") # `osd` is a pyannote.core.Annotation instance containing overlapped speech regions ``` ### Resegmentation ```python from pyannote.audio.pipelines import Resegmentation pipeline = Resegmentation(segmentation="pyannote/segmentation", diarization="baseline") pipeline.instantiate(HYPER_PARAMETERS) resegmented_baseline = pipeline({"audio": "audio.wav", "baseline": baseline}) # where `baseline` should be provided as a pyannote.core.Annotation instance ``` ### Raw scores ```python from pyannote.audio import Inference inference = Inference("pyannote/segmentation") segmentation = inference("audio.wav") # `segmentation` is a pyannote.core.SlidingWindowFeature # instance containing raw segmentation scores like the # one pictured above (output) ``` ## Reproducible research In order to reproduce the results of the paper ["End-to-end speaker segmentation for overlap-aware resegmentation "](https://arxiv.org/abs/2104.04045), use `pyannote/segmentation@Interspeech2021` with the following hyper-parameters: | Voice activity detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` | | ------------------------ | ------- | -------- | ----------------- | ------------------ | | AMI Mix-Headset | 0.684 | 0.577 | 0.181 | 0.037 | | DIHARD3 | 0.767 | 0.377 | 0.136 | 0.067 | | VoxConverse | 0.767 | 0.713 | 0.182 | 0.501 | | Overlapped speech detection | `onset` | `offset` | `min_duration_on` | `min_duration_off` | | --------------------------- | ------- | -------- | ----------------- | ------------------ | | AMI Mix-Headset | 0.448 | 0.362 | 0.116 | 0.187 | | DIHARD3 | 0.430 | 0.320 | 0.091 | 0.144 | | VoxConverse | 0.587 | 0.426 | 0.337 | 0.112 | | Resegmentation of VBx | `onset` | `offset` | `min_duration_on` | `min_duration_off` | | --------------------- | ------- | -------- | ----------------- | ------------------ | | AMI Mix-Headset | 0.542 | 0.527 | 0.044 | 0.705 | | DIHARD3 | 0.592 | 0.489 | 0.163 | 0.182 | | VoxConverse | 0.537 | 0.724 | 0.410 | 0.563 | Expected outputs (and VBx baseline) are also provided in the `/reproducible_research` sub-directories. ## Citation ```bibtex @inproceedings{Bredin2021, Title = {{End-to-end speaker segmentation for overlap-aware resegmentation}}, Author = {{Bredin}, Herv{\'e} and {Laurent}, Antoine}, Booktitle = {Proc. Interspeech 2021}, Address = {Brno, Czech Republic}, Month = {August}, Year = {2021}, ``` ```bibtex @inproceedings{Bredin2020, Title = {{pyannote.audio: neural building blocks for speaker diarization}}, Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe}, Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing}, Address = {Barcelona, Spain}, Month = {May}, Year = {2020}, } ```

aorhan/ddpm-butterflies-128

aorhan

2022-11-08T17:09:51Z

2

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-11-08T16:38:49Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/aorhan/ddpm-butterflies-128/tensorboard?#scalars)

harmonai/maestro-150k

harmonai

2022-11-08T16:40:37Z

30

17

diffusers

[ "diffusers", "audio-generation", "license:mit", "diffusers:DanceDiffusionPipeline", "region:us" ]

null

2022-10-20T12:20:47Z

--- license: mit tags: - audio-generation --- [Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers. ## FP32 ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/maestro-150k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ``` ## FP16 Faster at a small loss of quality ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/maestro-150k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ```

espnet/iam_handwriting_ocr

espnet

2022-11-08T16:28:56Z

4

7

espnet

[ "espnet", "image-to-text", "ocr", "handwriting-recognition", "en", "dataset:iam", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]

image-to-text

2022-11-04T17:05:39Z

--- tags: - espnet - image-to-text - ocr - handwriting-recognition language: en datasets: - iam license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/iam_handwriting_ocr` This model was trained by kenzheng99 using iam recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 2169367022b8939d22005e8cf45a65bb20bc0768 pip install -e . cd egs2/iam/ocr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/iam_handwriting_ocr ```  # RESULTS ## Environments - date: `Mon Nov 7 13:40:17 EST 2022` - python version: `3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.10.0` - Git hash: `2169367022b8939d22005e8cf45a65bb20bc0768` - Commit date: `Thu Nov 3 20:38:03 2022 -0400` ## asr_train_asr_conformer_extracted_en_char ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave/test|2915|25932|80.5|17.3|2.2|0.8|20.3|72.8| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave/test|2915|125616|94.0|4.2|1.8|0.7|6.7|72.8| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_extracted_en_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 35197 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 200 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 64 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_extracted_en_char/train/speech_shape - exp/asr_stats_extracted_en_char/train/text_shape.char valid_shape_file: - exp/asr_stats_extracted_en_char/valid/speech_shape - exp/asr_stats_extracted_en_char/valid/text_shape.char batch_type: folded valid_batch_type: null fold_length: - 800 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/extracted/train/feats.scp - speech - kaldi_ark - - dump/extracted/train/text - text - text valid_data_path_and_name_and_type: - - dump/extracted/valid/feats.scp - speech - kaldi_ark - - dump/extracted/valid/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - <space> - e - t - a - o - n - i - r - s - h - l - d - c - u - m - f - p - g - y - w - b - . - ',' - v - k - '-' - T - '''' - M - I - A - '"' - S - P - H - B - C - W - N - G - x - R - E - L - F - '0' - D - '1' - j - O - q - U - K - '!' - '3' - '9' - ( - z - ) - ':' - V - ; - '5' - '2' - J - '8' - Y - '4' - '6' - '?' - '#' - '&' - '7' - / - '*' - Q - X - Z - + - <sos/eos> init: xavier_uniform input_size: 100 ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: null frontend_conf: {} specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_extracted_en_char/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: '202209' distributed: true ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

PaulaAlfy/xlm-roberta-base-finetuned-panx-de-fr

PaulaAlfy

2022-11-08T15:55:07Z

107

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-08T15:16:21Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1907 - F1: 0.8682 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2901 | 1.0 | 715 | 0.1864 | 0.8211 | | 0.1576 | 2.0 | 1430 | 0.1667 | 0.8441 | | 0.1038 | 3.0 | 2145 | 0.1710 | 0.8452 | | 0.0701 | 4.0 | 2860 | 0.1787 | 0.8636 | | 0.0449 | 5.0 | 3575 | 0.1907 | 0.8682 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

bigmorning/whisper_end22

bigmorning

2022-11-08T15:15:27Z

62

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-08T15:15:16Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_end22 results: [] ---  # whisper_end22 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1061 - Train Accuracy: 0.0341 - Validation Loss: 0.5635 - Validation Accuracy: 0.0314 - Epoch: 22 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 | | 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 | | 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 | | 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 | | 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 | | 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 | | 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 | | 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 | | 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 | | 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 | | 0.5498 | 0.0307 | 0.6854 | 0.0306 | 10 | | 0.4804 | 0.0312 | 0.6518 | 0.0307 | 11 | | 0.4214 | 0.0316 | 0.6200 | 0.0310 | 12 | | 0.3713 | 0.0319 | 0.5947 | 0.0311 | 13 | | 0.3281 | 0.0322 | 0.5841 | 0.0311 | 14 | | 0.2891 | 0.0325 | 0.5700 | 0.0313 | 15 | | 0.2550 | 0.0328 | 0.5614 | 0.0313 | 16 | | 0.2237 | 0.0331 | 0.5572 | 0.0313 | 17 | | 0.1959 | 0.0333 | 0.5563 | 0.0314 | 18 | | 0.1698 | 0.0335 | 0.5530 | 0.0314 | 19 | | 0.1455 | 0.0337 | 0.5590 | 0.0314 | 20 | | 0.1242 | 0.0339 | 0.5743 | 0.0313 | 21 | | 0.1061 | 0.0341 | 0.5635 | 0.0314 | 22 | ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

bigmorning/whisper_0020

bigmorning

2022-11-08T15:05:11Z

59

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-08T15:05:01Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: whisper_0020 results: [] ---  # whisper_0020 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1698 - Train Accuracy: 0.0335 - Validation Loss: 0.5530 - Validation Accuracy: 0.0314 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 | | 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 | | 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 | | 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 | | 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 | | 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 | | 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 | | 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 | | 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 | | 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 | | 0.5498 | 0.0307 | 0.6854 | 0.0306 | 10 | | 0.4804 | 0.0312 | 0.6518 | 0.0307 | 11 | | 0.4214 | 0.0316 | 0.6200 | 0.0310 | 12 | | 0.3713 | 0.0319 | 0.5947 | 0.0311 | 13 | | 0.3281 | 0.0322 | 0.5841 | 0.0311 | 14 | | 0.2891 | 0.0325 | 0.5700 | 0.0313 | 15 | | 0.2550 | 0.0328 | 0.5614 | 0.0313 | 16 | | 0.2237 | 0.0331 | 0.5572 | 0.0313 | 17 | | 0.1959 | 0.0333 | 0.5563 | 0.0314 | 18 | | 0.1698 | 0.0335 | 0.5530 | 0.0314 | 19 | ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Tokenizers 0.13.2

rosamondthalken/t5-base-sci-names

rosamondthalken

2022-11-08T14:39:36Z

8

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "scientific names", "text generation", "en", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-09-16T15:00:05Z

--- language: - en tags: - scientific names - text generation license: cc-by-sa-4.0 --- # t5-base-sci-names Biodiversity literature is dedicated to the identification, documentation, and categorization of plants, fungi, animals, and other living organisms. Correctly extracting the name of an organism within these documents involves finding the entire scientific name–including the genus, specific epithet, and author name. Extracting these names allows biologists to access documents about a species more comprehensively, and to track an organism’s history of documentation, which includes biological changes and changes in how scientists describe them. **t5-base-sci-names** uses advances in text-to-text generation to generate scientific names and authors from biodiversity literature. This model was trained on hand-labeled biodiversity texts, including labeled information about a mentioned organism's genus (abbreviated and expanded), specific epithet, and author. This model was trained to output 0-N scientific names with specific prefixes (e.g. "genus = " or "epithet = ") and performs best with anywhere from 20-120 words. You can also use the model in this tutorial for [scientific names generation](https://colab.research.google.com/drive/1GEpnCaMJYiPIhuZiDJ1X1pZsGtGSm8Ds?usp=sharing). Thanks to Damon Little and Nelson Salinas at the New York Botanical Gardens for their support. *Note that this model is still a work in progress. Any feedback is welcome.*

google/ddpm-ema-cat-256

google

2022-11-08T13:42:16Z

1,133

2

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-07-19T10:45:53Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-cat-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-cat-256/resolve/main/images/generated_image_3.png)

google/ddpm-church-256

google

2022-11-08T13:41:58Z

850

9

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-07-19T10:42:51Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-church-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_3.png)

google/ddpm-ema-bedroom-256

google

2022-11-08T13:41:41Z

392

2

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-07-18T19:49:13Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-bedroom-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-bedroom-256/resolve/main/images/generated_image_3.png)

google/ddpm-ema-celebahq-256

google

2022-11-08T13:41:29Z

10,679

6

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "arxiv:2006.11239", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2022-07-19T10:42:32Z

--- license: apache-2.0 tags: - pytorch - diffusers - unconditional-image-generation --- # Denoising Diffusion Probabilistic Models (DDPM) **Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) **Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel **Abstract**: *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* ## Inference **DDPM** models can use *discrete noise schedulers* such as: - [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) - [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) - [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. See the following code: ```python # !pip install diffusers from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline model_id = "google/ddpm-ema-celebahq-256" # load model and scheduler ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference # run pipeline in inference (sample random noise and denoise) image = ddpm().images[0] # save image image.save("ddpm_generated_image.png") ``` For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) ## Training If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) # <- TODO(PVP) add link ## Samples 1. ![sample_1](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_0.png) 2. ![sample_2](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_1.png) 3. ![sample_3](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_2.png) 4. ![sample_4](https://huggingface.co/google/ddpm-ema-celebahq-256/resolve/main/images/generated_image_3.png)

ShinCore/MMDv1-18

ShinCore

2022-11-08T10:51:00Z

0

61

null

[ "stable-diffusion", "stable diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2022-11-05T10:51:25Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable diffusion - text-to-image --- MEGA MERGE DIFF (MMD) VERSION 1-18. 18 MERGED MODELS IN ONE ANNOUNCEMENT: - DUE TO THE FACT THAT I CANNOT SEEM TO CATCH A BREAK AND GET SOME TIME TO ASSUAGE MY OWN INSECURITIES ABOUT THE QUALITY OF THIS MODEL, I AM JUST GOING TO RELEASE IT. FIRST MODEL RELEASE: MMD V1-18 MODEL MERGE ALPHA: - DISCORD INVITE: https://discord.gg/WdmejvKCDG (EDIT 11/6, WILL NOT EXPIRE ANYMORE) - MODEL FAQ, MERGING METHODOLOGY, BORING CHARACTER BACKSTORY: https://discord.com/channels/900672465276116994/1035853968225615922 - LIST OF MERGED MODELS (DUE TO THE NATURE OF SOME OF THE MERGED MODELS, I CANNOT LIST THEM HERE): https://discord.com/channels/900672465276116994/1035895704377368687 - DOWNLOAD LINK: https://huggingface.co/ShinCore/MMDv1-18/tree/main SUMMARY: MMD V1-18 A MEGA MERGE OF SD 1.5 AND 17 OTHER MODELS. IT IS INTENDED TO BE A GENERALIST MODEL, NOT FOCUSED ON ANY SINGLE GENRE OR CATEGORY OR STYLE OR SUBJECT. THERE ARE ALREADY A PROLIFERATION OF GREAT MODELS OUT THERE, COVERING A BROAD SPECTRUM OF CONTENT. HOWEVER, THAT ALSO CAUSES A PROBLEM IN THAT WE HAVE A PROFLIERATION OF GREAT MODELS OUT THERE THAT DO ONE OR TWO THINGS REALLY WELL, BUT THATS KINDA IT. OTHER THAN THOSE ONE OR TWO THINGS THAT HAVE BEEN ADDED TO THE BASE SD MODEL, ITS NO DIFFERENT THAN ANY OF THE OTHER MODELS. MMD WAS CREATED TO ADDRESS THE ISSUE OF DISORGANIZED CONTENT FRAGMENTATION ACROSS HUGGINGFACE, DISCORD, REDDIT, RENTRY.ORG, 4CHAN, AND THE REMAINDER OF THE INTERNET. IT ALSO TRIES TO ADDRESS THE ISSUES INHERENT WITH THE BASE SD 1.5 MODEL. NAMELY, PROBLEMATIC ANATOMY, LACK OF RESPONSIVENESS TO PROMPT ENGINEERING, BLAND OUTPUTS, ETC. THE CURRENT SET OF MERGED MODELS ARE A CROSS SECTION OF MODELS THAT I FEEL IMPROVE AND ENRICH THE BASE MODEL. IN MY TESTS (WHICH YOU CAN TAKE A LOOK AT THE LOGS IN MY #EXPERIMENTS CHANNEL IN THE DISCORD I WORK OUT OF), THE MERGING OF A SPECIFIC SET OF MODELS HAS SHOWN TO IMPROVE HUMAN ANATOMY COHERENCY, INCREASE CREATIVITY AND DETAIL IN BOTH FORE/BACKGROUNDS, AND CAN BE MORE RESPONSIVE TO PROMPTING (PLEASE SEE PROMPTING NOTE BELOW). - DOWNSIDE: TRIGGER TERMS ASSOCIATED WITH SPECIFIC MODELS HAVE A DRASTICALLY REDUCED EFFECT. IF USING A TRIGGER TERM ASSOCIATED WITH A SPECIFIC MODEL, YOU MUST INCREASE THE STR TO SEE ANY EFFECT. THE MODEL CAN ALSO BE MUCH MORE SENSITIVE TO THE SETTINGS THAT YOU USE. I HAVE LISTED SOME RECOMMENDATIONS BELOW. IMPORTANT: I DO NOT, IN ANY WAY, SHAPE OR FORM, CLAIM THAT THIS MODEL IS SUPERIOR TO ANY OTHER MODEL OUT THERE. NOR DO I FEEL THAT I AM SOMEHOW SOME KIND OF SD GURU AND AM AN EXPERT OF ANY KIND. MY INTENTIONS IN CREATING THIS MODEL IS FOR MY OWN PERSONAL GOAL OF USING IT, AS WELL AS OTHER AI TOOLS, TO CREATE A STREAMLINED WORKFLOW PIPELINE THAT WILL ENABLE INDIE SOLO GAME DEVS TO CREATE GAMES WITH GREATER EASE AND EFFICIENCY. I DREAM OF SOMEDAY BEING A SOLO INDIE GAME DEV, AND THIS IS MY WAY OF HEADING TOWARDS THAT GOAL IN AN INDIRECT FASHION. I AM NOT A GENIUS. I AM NOT EVEN A GODDAMN CODER/PROGRAMMER/MATHMETICIAN. I AM COMPLETELY OUT OF MY DEPTH. I SOMETIMES FEEL LIKE THAT ZOOLANDER MEME, HOOTING AND POKING AT A COMPUTER THAT IS BEYOND MY LIMITED COMPREHENSION. I AM NOTHING MORE THAN AN OLD, TIRED, LAZY, AND GRUMPY BASTARD WHO IS SPENDING WHAT LITTLE FREE TIME I HAVE TRYING TO FIGURE THIS CRAP OUT SO THAT I DONT HAVE TO LEARN HOW TO CREATE A GAME FROM SCRATCH. I JUST WANT AN AI TO DO IT FOR ME. NOTES ABOUT USAGE: - MODEL CAN BE A BIT HARSH AND RIGID, BUT PUT OUT SOME AMAZING GENS. 2ND RELEASE WILL BE LESS EXTREME. RECOMMENDED SETTINGS: - IMG HEIGHT/WIDTH MUST BE SET TO A MULTIPLE OF 128 - SET HIRES FIX TO ON. SET FIRST PASS HEIGHT/WIDTH TO HALF OF IMG HEIGHT/WIDTH. - PLAY WITH DENOISING STR, I SET MINE TO .69, YMMV. - MODEL IS SENSITIVE TO CFG. I USUALLY USE 12.5, BUT OTHERS HAVE REPORTED BETTER OUTPUTS AT LOWER/HIGHER VALUES. TRY THEM OUT. DONT BE AFRAID OF GOING REALLY HIGH OR REALLY LOW. - SET RESIZE SEED OPTION TO 512X512. (DDIM RESPONDS BETTER TO THIS SETTING. OTHER SAMPLERS MAY NOT. TURN ON AND OFF AND COMPARE) PROMPTING: - MERGED MODELS USE A COMBO OF STANDARD SD BLIP/CLIP AND DANBOORU TAGS. I USE BOTH IN MY PROMPTS. TRY USING BOTH CLIP AND DANBOORU INTERROGATOR ON IMAGES, GET THE RESULTS FROM BOTH, AND USE THEM IN YOUR OWN PROMPTING. THEY SEEM TO REINFORCE EACH OTHER, THOUGH THIS IS DIFFICULT TO SCIENTIFICALLY VERIFY. I HAVE SEVERAL THEORIES THAT I INTEND TO TEST OUT, AS TIME PERMITS - USE NEGATIVE PROMPTING WITHOUT RESERVATIONS. YES, I HAVE SEEN STATEMENTS TO THE EFFECT THAT NEGATIVE PROMPTING IS LIKE A PLACEBO. ALL I CAN TELL YOU IS THAT, WHEN I REMOVED ALL OF MY NEGATIVE PROMPTS, MY GENS TURNED INTO A HORRORSHOW. I COULD NOT PUT THEM BACK ON FAST ENOUGH. 2ND RELEASE, MMD V1-18 MODEL MERGE (TONED DOWN) ALPHA: - THIS IS THE SAME AS THE FIRST RELEASE,BUT I MERGED BACK IN 25% OF SD 1.5. MORE FORGIVING, LESS EXTREME EDITION. EVERYTHING ELSE IS THE SAME. FINAL NOTE: IT HAS BEEN POINTED OUT TO ME THAT I AM USING ALL CAPS, AS IF I WAS SOMEHOW NOT ALREADY AWARE. AS I HAVE PREVIOUSLY POINTED OUT, I AM AN OLD, TIRED, LAZY AND GRUMPY BASTARD. MY PREVIOUS AND CURRENT PROFESSION HAS GOTTEN ME INTO THE HABIT OF USING ALL CAPS WHEN WORKING. DONT ASK ME THE REASONS, THEY ARE STUPID. AS IN, OTHER PEOPLE THAT I DEAL WITH. I AM PERFECTLY CAPABLE OF USING PROPER CAPITIALIZATION. I CHOOSE NOT TO WHEN I AM FOCUSED, GRINDING AWAY AT SOMETHING, HASTILY TRYING TO FINISH SOMETHING, OR AM IN A BAD MOOD BECAUSE SOMETHING IS IRRITATING ME. SO BASICALLY, PRETTY MUCH ALL THE TIME. I APOLOGIZE, BUT YOUR ARE GOING TO HAVE TO DEAL WITH IT.

bthomas/setfit_bench_bert-base-uncased_finetuned_for_seqclassif

bthomas

2022-11-08T10:23:54Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "SetFitbench", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-08T10:20:10Z

--- license: apache-2.0 tags: - SetFitbench - generated_from_trainer model-index: - name: setfit_bench_bert-base-uncased_finetuned_for_seqclassif results: [] ---  # setfit_bench_bert-base-uncased_finetuned_for_seqclassif This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3437 | 1.0 | 189 | 0.2666 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

troesy/hateBERT_3epoch

troesy

2022-11-08T10:21:20Z

16

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-08T10:07:36Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: hateBERT_3epoch results: [] ---  # hateBERT_3epoch This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2174 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9174 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 174 | 0.2301 | 0.0 | 0.0 | 0.0 | 0.9112 | | No log | 2.0 | 348 | 0.2192 | 0.0 | 0.0 | 0.0 | 0.9148 | | 0.2311 | 3.0 | 522 | 0.2174 | 0.0 | 0.0 | 0.0 | 0.9174 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

dreaming-tree/rl_class

dreaming-tree

2022-11-08T10:17:43Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-08T10:16:56Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 73.61 +/- 70.22 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

troesy/toxicBERT_3epoch

troesy

2022-11-08T10:00:48Z

20

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-04T16:27:22Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: toxicBERT_3epoch results: [] ---  # toxicBERT_3epoch This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co/GroNLP/hateBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2162 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9173 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 174 | 0.2301 | 0.0 | 0.0 | 0.0 | 0.9110 | | No log | 2.0 | 348 | 0.2186 | 0.0 | 0.0 | 0.0 | 0.9134 | | 0.2312 | 3.0 | 522 | 0.2162 | 0.0 | 0.0 | 0.0 | 0.9173 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

tkubotake/xlm-roberta-base-finetuned-panx-all

tkubotake

2022-11-08T09:07:09Z

6

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T03:46:39Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2290 - F1: 0.8629 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1259 | 1.0 | 835 | 0.1879 | 0.8478 | | 0.078 | 2.0 | 1670 | 0.2121 | 0.8582 | | 0.0439 | 3.0 | 2505 | 0.2290 | 0.8629 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

sd-concepts-library/kodakvision500t

sd-concepts-library

2022-11-08T08:19:22Z

0

14

null

[ "license:mit", "region:us" ]

null

2022-11-08T07:57:07Z

--- license: mit --- ### KodakVision500T on Stable Diffusion This is the `<kodakvision_500T>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). This concept was trained on **6** photographs taken with **Kodak Vision 3 500T**, through **1800** steps. Here are some generated images from the concept that you will be able to use as a `style`: ![<kodakvision_500T> 4](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_4.png) ![<kodakvision_500T> 3](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_3.png) ![<kodakvision_500T> 2](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_2.png) ![<kodakvision_500T> 1](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_1.png)

Sushanti123/layoutxlm-finetuned-xfund-fr

Sushanti123

2022-11-08T07:26:56Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "layoutlmv2", "token-classification", "generated_from_trainer", "dataset:xfun", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-01T08:40:16Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - xfun model-index: - name: layoutxlm-finetuned-xfund-fr results: [] ---  # layoutxlm-finetuned-xfund-fr This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsoft/layoutxlm-base) on the xfun dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.10.0+cu111 - Datasets 2.6.1 - Tokenizers 0.13.2

GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan

GuiGel

2022-11-08T07:19:25Z

6

0

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]

token-classification

2022-11-08T07:16:36Z

--- tags: - flair - token-classification - sequence-tagger-model --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("GuiGel/beto-uncased-flert-context-we-lstm-crf-meddocan") # make example sentence sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ```

Meow412/finetuning-sentiment-BERTmodel-A3-allcontents

Meow412

2022-11-08T06:40:42Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-08T05:51:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-BERTmodel-A3-allcontents results: [] ---  # finetuning-sentiment-BERTmodel-A3-allcontents This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2951 - Accuracy: 0.8814 - F1: 0.4138 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

bguan/Reinforce-CartPole-v1

bguan

2022-11-08T05:55:49Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-11-08T02:43:41Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 440.00 +/- 88.54 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

Meow412/finetuning-sentiment-BERTmodel-A3

Meow412

2022-11-08T05:36:32Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-08T05:15:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-BERTmodel-A3 results: [] ---  # finetuning-sentiment-BERTmodel-A3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3307 - Accuracy: 0.8656 - F1: 0.3576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

RamAnanth1/positive-reframing

RamAnanth1

2022-11-08T04:47:00Z

34

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "arxiv:2204.02952", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-01T20:50:51Z

# Positive Perspectives with Text Reframing Based on the paper [Inducing Positive Perspectives with Text Reframing](https://arxiv.org/abs/2204.02952), this model focuses on the positive reframing task. The purpose of the model is to neutralize a negative point of view and generate a more positive perspective without changing the original meaning. The model provided is obtained from this [HuggingFace Space](https://huggingface.co/spaces/Ella2323/Positive-Reframing) and stored in a separate repository to increase ease of use. All credits go to the original contributors of the abovementioned HuggingFace Space. ### Available strategies for positive reframing: **growth**: viewing a challenging event as an opportunity for the author to specifically grow or improve himself. **impermanence**: Saying that bad things don't last forever, will get better soon, and/or that other people have had similar difficulties. **neutralizing**: Replacing a negative word with a neutral word. For example, “This was a terrible day” becomes “This was a long day”. **optimism**: Focusing on things about the situation itself, at that moment, that are good (not just predicting a better future). **self_affirmation**: Talking about what strengths the author already has, or values he admires, such as love, courage, perseverance, etc. **thankfulness**: Expressing gratitude or gratitude with keywords like appreciate, happy for it, grateful for, good thing, etc.

kit-nlp/bert-base-japanese-sentiment-irony

kit-nlp

2022-11-08T04:23:27Z

481

4

transformers

[ "transformers", "pytorch", "bert", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T06:29:21Z

--- language: ja license: cc-by-sa-4.0 --- # BERT Base Japanese for Irony This is a BERT Base model for sentiment analysis in Japanese additionally finetuned for automatic irony detection. The model was based on [bert-base-japanese-sentiment](https://huggingface.co/daigo/bert-base-japanese-sentiment), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony02, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (daigo ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-sentiment-irony" } ```

kit-nlp/yacis-electra-small-japanese-irony

kit-nlp

2022-11-08T04:16:30Z

5

0

transformers

[ "transformers", "pytorch", "electra", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T07:05:34Z

--- language: ja license: cc-by-sa-4.0 --- # YACIS ELECTRA Small Japanese for Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [YACIS ELECTRA small Japanese](https://huggingface.co/ptaszynski/yacis-electra-small-japanese), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022yaciselectra-small-irony, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/yacis-electra-small-japanese-irony" } ```

kit-nlp/electra-small-japanese-discriminator-irony

kit-nlp

2022-11-08T04:11:04Z

4

0

transformers

[ "transformers", "pytorch", "electra", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T07:14:32Z

--- language: ja license: cc-by-sa-4.0 --- # ELECTRA small Japanese discriminator for Irony This is an [ELECTRA](https://github.com/google-research/electra) Base model for the Japanese language finetuned for automatic irony detection. The model was based on [ELECTRA small Japanese discriminator](https://huggingface.co/izumi-lab/electra-small-japanese-discriminator), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022electra-base-irony, title={北見工業大学テキスト情報処理研究室 ELECTRA Base 皮肉検出モデル (Izumi Labs ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/electra-small-japanese-discriminator-irony" } ```

bigmorning/bigmorning_whisper

bigmorning

2022-11-08T03:44:46Z

61

0

transformers

[ "transformers", "tf", "whisper", "automatic-speech-recognition", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-08T03:13:01Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: bigmorning_whisper results: [] ---  # bigmorning_whisper This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.0.dev0 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.2

dhshin/ddpm-butterflies-128

dhshin

2022-11-08T03:16:58Z

3

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-10-25T01:06:18Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/dhshin/ddpm-butterflies-128/tensorboard?#scalars)

QianMolloy/distilbert-base-uncased-finetuned-emotion

QianMolloy

2022-11-08T03:06:18Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T02:51:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.928851862350588 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2178 - Accuracy: 0.9285 - F1: 0.9289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8227 | 1.0 | 250 | 0.3212 | 0.8985 | 0.8932 | | 0.2463 | 2.0 | 500 | 0.2178 | 0.9285 | 0.9289 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.10.0 - Datasets 2.6.1 - Tokenizers 0.13.1

BigSalmon/InformalToFormalLincoln90Paraphrase

BigSalmon

2022-11-08T03:06:10Z

163

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-08T02:37:14Z

data: https://github.com/BigSalmon2/InformalToFormalDataset Text Generation Informal Formal ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln90Paraphrase") ``` ``` Demo: https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy ``` ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" input_ids = tokenizer.encode(prompt, return_tensors='pt') outputs = model.generate(input_ids=input_ids, max_length=10 + len(prompt), temperature=1.0, top_k=50, top_p=0.95, do_sample=True, num_return_sequences=5, early_stopping=True) for i in range(5): print(tokenizer.decode(outputs[i])) ``` Most likely outputs (Disclaimer: I highly recommend using this over just generating): ``` prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:""" text = tokenizer.encode(prompt) myinput, past_key_values = torch.tensor([text]), None myinput = myinput myinput= myinput.to(device) logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False) logits = logits[0,-1] probabilities = torch.nn.functional.softmax(logits) best_logits, best_indices = logits.topk(250) best_words = [tokenizer.decode([idx.item()]) for idx in best_indices] text.append(best_indices[0].item()) best_probabilities = probabilities[best_indices].tolist() words = [] print(best_words) ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` original: microsoft word's [MASK] pricing invites competition. Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition. *** original: the library’s quiet atmosphere encourages visitors to [blank] in their work. Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work. ``` ``` Essay Intro (Warriors vs. Rockets in Game 7): text: eagerly anticipated by fans, game 7's are the highlight of the post-season. text: ever-building in suspense, game 7's have the crowd captivated. *** Essay Intro (South Korean TV Is Becoming Popular): text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ). text: increasingly held in critical esteem, south korean television continues to impress. text: at the forefront of quality content, south korea is quickly achieving celebrity status. *** Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ``` ``` - nebraska - unicamerical legislature - different from federal house and senate text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate. *** - penny has practically no value - should be taken out of circulation - just as other coins have been in us history - lost use - value not enough - to make environmental consequences worthy text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness. *** - ``` ``` original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick. infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick. *** original: ``` ``` wordy: classical music is becoming less popular more and more. Translate into Concise Text: interest in classic music is fading. *** wordy: ``` ``` sweet: savvy voters ousted him. longer: voters who were informed delivered his defeat. *** sweet: ``` ``` 1: commercial space company spacex plans to launch a whopping 52 flights in 2022. 2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022. 3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights. 4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company. 5: a commercial space company, spacex aims to conduct 52 flights in 2022. *** 1: ``` Keywords to sentences or sentence. ``` ngos are characterized by: □ voluntary citizens' group that is organized on a local, national or international level □ encourage political participation □ often serve humanitarian functions □ work for social, economic, or environmental change *** what are the drawbacks of living near an airbnb? □ noise □ parking □ traffic □ security □ strangers *** ``` ``` original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung. adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung. *** original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark. adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark. *** original: ``` ``` original: had trouble deciding. translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation. *** original: ``` ``` input: not loyal 1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ). *** input: ``` ``` first: ( was complicit in / was involved in ). antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ). *** first: ( have no qualms about / see no issue with ). antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ). *** first: ( do not see eye to eye / disagree often ). antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ). *** first: ``` ``` stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground. *** languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo. *** dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia. *** embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons. ``` Infill / Infilling / Masking / Phrase Masking (Works pretty decently actually, especially when you use logprobs code from above): ``` his contention [blank] by the evidence [sep] was refuted [answer] *** few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer] *** when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer] *** the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer] *** the joy of sport is that no two games are alike. for every exhilarating experience, however, there is an interminable one. the national pastime, unfortunately, has a penchant for the latter. what begins as a summer evening at the ballpark can quickly devolve into a game of tedium. the primary culprit is the [blank] of play. from batters readjusting their gloves to fielders spitting on their mitts, the action is [blank] unnecessary interruptions. the sport's future is [blank] if these tendencies are not addressed [sep] plodding pace [answer] riddled with [answer] bleak [answer] *** microsoft word's [blank] pricing [blank] competition [sep] unconscionable [answer] invites [answer] *** ``` ``` original: microsoft word's [MASK] pricing invites competition. Translated into the Style of Abraham Lincoln: microsoft word's unconscionable pricing invites competition. *** original: the library’s quiet atmosphere encourages visitors to [blank] in their work. Translated into the Style of Abraham Lincoln: the library’s quiet atmosphere encourages visitors to immerse themselves in their work. ``` Backwards ``` Essay Intro (National Parks): text: tourists are at ease in the national parks, ( swept up in the beauty of their natural splendor ). *** Essay Intro (D.C. Statehood): washington, d.c. is a city of outsize significance, ( ground zero for the nation's political life / center stage for the nation's political machinations ). ``` ``` topic: the Golden State Warriors. characterization 1: the reigning kings of the NBA. characterization 2: possessed of a remarkable cohesion. characterization 3: helmed by superstar Stephen Curry. characterization 4: perched atop the league’s hierarchy. characterization 5: boasting a litany of hall-of-famers. *** topic: emojis. characterization 1: shorthand for a digital generation. characterization 2: more versatile than words. characterization 3: the latest frontier in language. characterization 4: a form of self-expression. characterization 5: quintessentially millennial. characterization 6: reflective of a tech-centric world. *** topic: ``` ``` regular: illinois went against the census' population-loss prediction by getting more residents. VBG: defying the census' prediction of population loss, illinois experienced growth. *** regular: microsoft word’s high pricing increases the likelihood of competition. VBG: extortionately priced, microsoft word is inviting competition. *** regular: ``` ``` source: badminton should be more popular in the US. QUERY: Based on the given topic, can you develop a story outline? target: (1) games played with racquets are popular, (2) just look at tennis and ping pong, (3) but badminton underappreciated, (4) fun, fast-paced, competitive, (5) needs to be marketed more text: the sporting arena is dominated by games that are played with racquets. tennis and ping pong, in particular, are immensely popular. somewhat curiously, however, badminton is absent from this pantheon. exciting, fast-paced, and competitive, it is an underappreciated pastime. all that it lacks is more effective marketing. *** source: movies in theaters should be free. QUERY: Based on the given topic, can you develop a story outline? target: (1) movies provide vital life lessons, (2) many venues charge admission, (3) those without much money text: the lessons that movies impart are far from trivial. the vast catalogue of cinematic classics is replete with inspiring sagas of friendship, bravery, and tenacity. it is regrettable, then, that admission to theaters is not free. in their current form, the doors of this most vital of institutions are closed to those who lack the means to pay. *** source: ``` ``` in the private sector, { transparency } is vital to the business’s credibility. the { disclosure of information } can be the difference between success and failure. *** the labor market is changing, with { remote work } now the norm. this { flexible employment } allows the individual to design their own schedule. *** the { cubicle } is the locus of countless grievances. many complain that the { enclosed workspace } restricts their freedom of movement. *** ``` ``` it would be natural to assume that americans, as a people whose ancestors { immigrated to this country }, would be sympathetic to those seeking to do likewise. question: what does “do likewise” mean in the above context? (a) make the same journey (b) share in the promise of the american dream (c) start anew in the land of opportunity (d) make landfall on the united states *** in the private sector, { transparency } is vital to the business’s credibility. this orientation can be the difference between success and failure. question: what does “this orientation” mean in the above context? (a) visible business practices (b) candor with the public (c) open, honest communication (d) culture of accountability ``` ``` example: suppose you are a teacher. further suppose you want to tell an accurate telling of history. then suppose a parent takes offense. they do so in the name of name of their kid. this happens a lot. text: educators' responsibility to remain true to the historical record often clashes with the parent's desire to shelter their child from uncomfortable realities. *** example: suppose you are a student at college. now suppose you have to buy textbooks. that is going to be worth hundreds of dollars. given how much you already spend on tuition, that is going to hard cost to bear. text: the exorbitant cost of textbooks, which often reaches hundreds of dollars, imposes a sizable financial burden on the already-strapped college student. ``` ``` <Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle> *** <Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle> ``` ``` accustomed to having its name uttered ______, harvard university is weathering a rare spell of reputational tumult (a) in reverential tones (b) with great affection (c) in adulatory fashion (d) in glowing terms ``` ``` clarify: international ( {working together} / cooperation ) is called for when ( {issue go beyond lots of borders} / an issue transcends borders / a given matter has transnational implications ). ``` ``` description: when someone thinks that their view is the only right one. synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous. *** description: when you put something off. synonyms: shelve, defer, table, postpone. ``` ``` organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea. rewrite phrases: meritocratic, viability, vision rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability. ``` *Note* Of all the masking techniques, this one works the best. ``` <Prefix> the atlanta hawks may attribute <Prefix> <Suffix> trae young <Suffix> <Middle> their robust season to <Middle> *** <Prefix> the nobel prize in literature <Prefix> <Suffix> honor <Suffix> <Middle> is a singularly prestigious <Middle> ``` ``` essence: when someone's views are keeping within reasonable. refine: the senator's voting record is ( moderate / centrist / pragmatic / balanced / fair-minded / even-handed ). *** essence: when things are worked through in a petty way. refine: the propensity of the u.s. congress to settle every dispute by way of ( mudslinging / bickering / demagoguery / name-calling / finger-pointing / vilification ) is appalling. ``` ``` description: when someone thinks that their view is the only right one. synonyms: intolerant, opinionated, narrow-minded, insular, self-righteous. *** description: when you put something off. synonyms: shelve, defer, table, postpone. ``` ``` organic sentence: crowdfunding is about winner of best ideas and it can test an entrepreneur’s idea. rewrite phrases: meritocratic, viability, vision rewritten with phrases: the meritocratic nature of crowdfunding empowers entrepreneurs to test their vision's viability. ``` ``` music before bedtime [makes for being able to relax] -> is a recipe for relaxation. ``` ``` [people wanting entertainment love traveling new york city] -> travelers flock to new york city in droves, drawn to its iconic entertainment scene. [cannot blame them] -> one cannot fault them [broadway so fun] -> when it is home to such thrilling fare as Broadway. ``` ``` in their ( ‖ when you are rushing because you want to get there on time ‖ / haste to arrive punctually / mad dash to be timely ), morning commuters are too rushed to whip up their own meal. *** politicians prefer to author vague plans rather than ( ‖ when you can make a plan without many unknowns ‖ / actionable policies / concrete solutions ). ``` ``` Q: What is whistleblower protection? A: Whistleblower protection is a form of legal immunity granted to employees who expose the unethical practices of their employer. Q: Why are whistleblower protections important? A: Absent whistleblower protections, employees would be deterred from exposing their employer’s wrongdoing for fear of retribution. Q: Why would an employer engage in retribution? A: An employer who has acted unethically stands to suffer severe financial and reputational damage were their transgressions to become public. To safeguard themselves from these consequences, they might seek to dissuade employees from exposing their wrongdoing. ``` ``` original: the meritocratic nature of crowdfunding [MASK] into their vision's viability. infill: the meritocratic nature of crowdfunding [gives investors idea of how successful] -> ( offers entrepreneurs a window ) into their vision's viability. ``` ``` Leadership | Lecture 17: Worker Morale What Workers Look for in Companies: • Benefits o Tuition reimbursement o Paid parental leave o 401K matching o Profit sharing o Pension plans o Free meals • Social responsibility o Environmental stewardship o Charitable contributions o Diversity • Work-life balance o Telecommuting o Paid holidays and vacation o Casual dress • Growth opportunities • Job security • Competitive compensation • Recognition o Open-door policies o Whistleblower protection o Employee-of-the-month awards o Positive performance reviews o Bonuses ``` ``` description: business keywords: for-profit, fiduciary duty, monopolistic, bottom line, return on investment, short-term thinking, capital-intensive, self-interested, risk-taking, fiduciary duty, merger, speculation, profiteering, oversight, capitalism, diversification ``` ``` 3. In this task, you are given a company name and you need to find its industry. McDonalds -- Restaurant Facebook -- Social Network IKEA -- Furniture American Express -- Credit Services Nokia -- Telecom Nintendo -- Entertainment 4. In this task, you are given a Month and you need to convert it to its corresponding season April -- Spring December -- Winter July -- Summer October -- Fall February -- Winter 5. In this task, you are given a sentence with a missing word and you need to predict the correct word. Managers should set an _____ for their employees. -- example Some people spend more than four _____ in the gym. -- hours The police were on the _____ of arresting the suspect. -- verge They were looking for _____ on how to solve the problem. -- guidance What is the _____ of the coffee? -- price 6. In this task, you are given a paragraph and you need to reorder it to make it logical. It was first proposed in 1987. The total length of the bridge is 1,828 meters. The idea of a bridge connects Hong Kong to Macau. -- The idea of bridge connecting Hong Kong and Macau was first proposed in 1987. The total length of the bridge is 1,828 meters. It is a movie about a brave and noble policeman. The film was produced by Americans. They were Kevin Lima and Chris Buck. They are directors. The movie is called Tarzan. -- Produced by Americans Kevin Lima and Chris Buck, Tarzan is a movie about a brave and noble policeman. It was first discovered in the mountains of India. The active ingredients in this plant can stimulate hair growth. The plant is called "Hair Plus." -- First discovered in the mountains of India, Hair Plus is a plant whose active ingredients can stimulate hair growth. ``` ``` trivia: What is the population of South Korea? response: 51 million. *** trivia: What is the minimum voting age in the US? response: 18. *** trivia: What are the first ten amendments of the US constitution called? response: Bill of Rights. ```

huggingtweets/sensanders

huggingtweets

2022-11-08T02:38:49Z

109

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-28T15:31:16Z

--- language: en thumbnail: http://www.huggingtweets.com/sensanders/1667875118330/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/794619281271033856/Fs0QQaH7_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Bernie Sanders</div> <div style="text-align: center; font-size: 14px;">@sensanders</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Bernie Sanders. | Data | Bernie Sanders | | --- | --- | | Tweets downloaded | 3249 | | Retweets | 276 | | Short tweets | 4 | | Tweets kept | 2969 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ghx24nl1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sensanders's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/31jf3liz) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/31jf3liz/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sensanders') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

tomrb/bettercallbloom-3b

tomrb

2022-11-08T01:12:59Z

9

5

transformers

[ "transformers", "pytorch", "bloom", "text-generation", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-20T17:17:16Z

--- language: en license: mit --- # BetterCallBloom-3b Finetuned Bloom-3b model on the r/legaladvice subreddit from pileoflaw ## Model description BLOOM-3B is a 3,002,557,440 parameters model pretrained by the BigScience initiative. ## Intended uses & limitations ### How to use ### Limitations and bias ## Training data ## Training procedure ### Preprocessing ## Evaluation results ### BibTeX entry and citation info

kit-nlp/bert-base-japanese-basic-char-v2-irony

kit-nlp

2022-11-08T00:10:26Z

105

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "ja", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T07:33:23Z

--- language: ja license: cc-by-sa-4.0 --- # bert-base-irony This is a BERT Base model for the Japanese language finetuned for automatic irony detection. The model was based on [BERT base Japanese](https://huggingface.co/hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2), and later finetuned on a dataset containing ironic and sarcastic tweets. ## Licenses The finetuned model with all attached files is licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/), or Creative Commons Attribution-ShareAlike 4.0 International License. <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> ## Citations Please, cite this model using the following citation. ``` @inproceedings{dan2022bert-base-irony, title={北見工業大学テキスト情報処理研究室 BERT Base 皮肉検出モデル (RIT ver.)}, author={団　俊輔　and プタシンスキミハウ and ジェプカラファウ and 桝井文人}, publisher={HuggingFace}, year={2022}, url = "https://huggingface.co/kit-nlp/bert-base-japanese-basic-char-v2-irony" } ```

yongauh/distilbert-base-uncased-finetuned-emotion

yongauh

2022-11-07T23:48:42Z

5

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T23:35:23Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.921 - name: F1 type: f1 value: 0.9211554013340549 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.921 - F1: 0.9212 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8473 | 1.0 | 250 | 0.3167 | 0.908 | 0.9061 | | 0.2561 | 2.0 | 500 | 0.2208 | 0.921 | 0.9212 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.11.0

Devarshi/Brain_Tumor_Detector_swin

Devarshi

2022-11-07T22:28:14Z

50

4

transformers

[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-07T06:37:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - f1 - recall - precision model-index: - name: Brain_Tumor_Detector_swin results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9981308411214953 - name: F1 type: f1 value: 0.9985111662531018 - name: Recall type: recall value: 0.9990069513406157 - name: Precision type: precision value: 0.998015873015873 ---  # Brain_Tumor_Detector_swin This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0054 - Accuracy: 0.9981 - F1: 0.9985 - Recall: 0.9990 - Precision: 0.9980 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.079 | 1.0 | 113 | 0.0283 | 0.9882 | 0.9906 | 0.9930 | 0.9881 | | 0.0575 | 2.0 | 226 | 0.0121 | 0.9956 | 0.9965 | 0.9950 | 0.9980 | | 0.0312 | 3.0 | 339 | 0.0054 | 0.9981 | 0.9985 | 0.9990 | 0.9980 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

AlekseyKorshuk/amazon-reviews-input-output-6.7b-best

AlekseyKorshuk

2022-11-07T22:14:21Z

7

1

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/amazon-reviews-input-output", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T21:47:46Z

--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/amazon-reviews-input-output metrics: - accuracy model-index: - name: amazon-reviews-input-output-6.7b-best results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/amazon-reviews-input-output type: AlekseyKorshuk/amazon-reviews-input-output metrics: - name: Accuracy type: accuracy value: 0.040325203252032524 ---  # amazon-reviews-input-output-6.7b-best This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set: - Loss: 2.6953 - Accuracy: 0.0403 - Samples: 100 - Perplexity: 14.8101 - Table: <wandb.data_types.Table object at 0x7fc684448b50> ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.9912 | 0.06 | 1 | 2.7441 | 0.0404 | | 2.9329 | 0.12 | 2 | 2.7441 | 0.0404 | | 2.9138 | 0.19 | 3 | 2.8262 | 0.0389 | | 2.9395 | 0.25 | 4 | 2.8262 | 0.0389 | | 2.9109 | 0.31 | 5 | 2.7949 | 0.0399 | | 2.8391 | 0.38 | 6 | 2.7461 | 0.0403 | | 2.9368 | 0.44 | 7 | 2.7207 | 0.0398 | | 2.7583 | 0.5 | 8 | 2.7070 | 0.0403 | | 2.9756 | 0.56 | 9 | 2.6836 | 0.0408 | | 2.8442 | 0.62 | 10 | 2.6738 | 0.0403 | | 2.7312 | 0.69 | 11 | 2.6680 | 0.0405 | | 2.7439 | 0.75 | 12 | 2.6699 | 0.0404 | | 2.9075 | 0.81 | 13 | 2.6797 | 0.0403 | | 2.8518 | 0.88 | 14 | 2.6797 | 0.0403 | | 2.8579 | 0.94 | 15 | 2.6777 | 0.0404 | | 2.8916 | 1.0 | 16 | 2.6953 | 0.0403 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

bishalbaaniya/bishalbaaniya-finetuned-myaamia-to-english

bishalbaaniya

2022-11-07T21:15:33Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-27T03:24:09Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: bishalbaaniya-finetuned-myaamia-to-english results: [] ---  # bishalbaaniya-finetuned-myaamia-to-english This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.0090 - Bleu: 0.1637 - Gen Len: 7.977 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 4.1712 | 1.0 | 1082 | 4.0090 | 0.1637 | 7.977 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

ilevs/distilrubert-tiny-cased-conversational-finetuned

ilevs

2022-11-07T21:06:23Z

109

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-06T14:54:50Z

--- tags: - generated_from_trainer model-index: - name: distilrubert-tiny-cased-conversational-finetuned results: [] ---  # distilrubert-tiny-cased-conversational-finetuned This model is a fine-tuned version of [DeepPavlov/distilrubert-tiny-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

AlekseyKorshuk/amazon-reviews-input-output-1.3b

AlekseyKorshuk

2022-11-07T20:45:36Z

5

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "dataset:AlekseyKorshuk/amazon-reviews-input-output", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T20:26:17Z

--- license: other tags: - generated_from_trainer datasets: - AlekseyKorshuk/amazon-reviews-input-output metrics: - accuracy model-index: - name: amazon-reviews-input-output-1.3b results: - task: name: Causal Language Modeling type: text-generation dataset: name: AlekseyKorshuk/amazon-reviews-input-output type: AlekseyKorshuk/amazon-reviews-input-output metrics: - name: Accuracy type: accuracy value: 0.03550813008130081 ---  # amazon-reviews-input-output-1.3b This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set: - Loss: 3.5488 - Accuracy: 0.0355 - Samples: 100 - Perplexity: 34.7725 - Table: <wandb.data_types.Table object at 0x7ffa3c3fd700> ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.2024 | 0.06 | 1 | 2.9121 | 0.0385 | | 3.1226 | 0.12 | 2 | 2.9121 | 0.0385 | | 3.1321 | 0.19 | 3 | 2.8477 | 0.0394 | | 2.9875 | 0.25 | 4 | 2.8477 | 0.0394 | | 2.9717 | 0.31 | 5 | 2.8555 | 0.0391 | | 2.9341 | 0.38 | 6 | 2.8438 | 0.0392 | | 3.0376 | 0.44 | 7 | 2.8184 | 0.0396 | | 2.8164 | 0.5 | 8 | 2.7988 | 0.0395 | | 3.0857 | 0.56 | 9 | 2.7988 | 0.0394 | | 2.9492 | 0.62 | 10 | 2.7969 | 0.0395 | | 2.8633 | 0.69 | 11 | 2.7969 | 0.0395 | | 2.8994 | 0.75 | 12 | 2.7910 | 0.0398 | | 3.0024 | 0.81 | 13 | 2.7812 | 0.0401 | | 2.937 | 0.88 | 14 | 2.7812 | 0.0399 | | 2.9963 | 0.94 | 15 | 2.7812 | 0.0399 | | 3.0168 | 1.0 | 16 | 2.7754 | 0.04 | | 2.2589 | 1.06 | 17 | 2.7715 | 0.0397 | | 2.2568 | 1.12 | 18 | 2.7793 | 0.0395 | | 2.3138 | 1.19 | 19 | 2.8027 | 0.0393 | | 2.2759 | 1.25 | 20 | 2.8184 | 0.0393 | | 2.5137 | 1.31 | 21 | 2.8262 | 0.0390 | | 2.2997 | 1.38 | 22 | 2.8320 | 0.0388 | | 2.2693 | 1.44 | 23 | 2.8359 | 0.0392 | | 2.204 | 1.5 | 24 | 2.8379 | 0.0387 | | 2.3713 | 1.56 | 25 | 2.8359 | 0.0391 | | 2.3448 | 1.62 | 26 | 2.8340 | 0.0391 | | 2.217 | 1.69 | 27 | 2.8359 | 0.0391 | | 2.3082 | 1.75 | 28 | 2.8379 | 0.0385 | | 2.2878 | 1.81 | 29 | 2.8379 | 0.0386 | | 2.2429 | 1.88 | 30 | 2.8379 | 0.0385 | | 2.2838 | 1.94 | 31 | 2.8359 | 0.0385 | | 2.4038 | 2.0 | 32 | 2.8379 | 0.0387 | | 1.8481 | 2.06 | 33 | 2.8555 | 0.0384 | | 1.657 | 2.12 | 34 | 2.8965 | 0.0382 | | 1.6996 | 2.19 | 35 | 2.9590 | 0.0380 | | 1.6741 | 2.25 | 36 | 3.0312 | 0.0379 | | 1.594 | 2.31 | 37 | 3.0410 | 0.0380 | | 1.5201 | 2.38 | 38 | 3.0156 | 0.0381 | | 1.5149 | 2.44 | 39 | 3.0137 | 0.0380 | | 1.5521 | 2.5 | 40 | 3.0176 | 0.0379 | | 1.5364 | 2.56 | 41 | 3.0273 | 0.0378 | | 1.5385 | 2.62 | 42 | 3.0391 | 0.0380 | | 1.4794 | 2.69 | 43 | 3.0488 | 0.0380 | | 1.4313 | 2.75 | 44 | 3.0527 | 0.0378 | | 1.5071 | 2.81 | 45 | 3.0469 | 0.0378 | | 1.4799 | 2.88 | 46 | 3.0449 | 0.0378 | | 1.521 | 2.94 | 47 | 3.0371 | 0.0380 | | 1.4603 | 3.0 | 48 | 3.0410 | 0.0379 | | 1.25 | 3.06 | 49 | 3.0859 | 0.0381 | | 1.0411 | 3.12 | 50 | 3.1797 | 0.0375 | | 1.0385 | 3.19 | 51 | 3.2969 | 0.0371 | | 1.0254 | 3.25 | 52 | 3.3613 | 0.0367 | | 0.9656 | 3.31 | 53 | 3.3633 | 0.0368 | | 1.036 | 3.38 | 54 | 3.3359 | 0.0366 | | 0.9366 | 3.44 | 55 | 3.2949 | 0.0366 | | 0.9712 | 3.5 | 56 | 3.2695 | 0.0367 | | 1.0066 | 3.56 | 57 | 3.2676 | 0.0366 | | 0.9952 | 3.62 | 58 | 3.2773 | 0.0368 | | 1.0352 | 3.69 | 59 | 3.2891 | 0.0367 | | 1.0212 | 3.75 | 60 | 3.3164 | 0.0362 | | 0.9468 | 3.81 | 61 | 3.3203 | 0.0360 | | 0.9155 | 3.88 | 62 | 3.3223 | 0.0366 | | 0.8552 | 3.94 | 63 | 3.3262 | 0.0370 | | 0.9575 | 4.0 | 64 | 3.3340 | 0.0370 | | 0.6384 | 4.06 | 65 | 3.375 | 0.0370 | | 0.6436 | 4.12 | 66 | 3.4453 | 0.0364 | | 0.5752 | 4.19 | 67 | 3.5391 | 0.0358 | | 0.6542 | 4.25 | 68 | 3.6016 | 0.0354 | | 0.6724 | 4.31 | 69 | 3.6016 | 0.0354 | | 0.591 | 4.38 | 70 | 3.5938 | 0.0359 | | 0.5346 | 4.44 | 71 | 3.5801 | 0.0361 | | 0.5112 | 4.5 | 72 | 3.5762 | 0.0361 | | 0.5443 | 4.56 | 73 | 3.5840 | 0.0362 | | 0.5689 | 4.62 | 74 | 3.6152 | 0.0358 | | 0.5667 | 4.69 | 75 | 3.6328 | 0.0358 | | 0.554 | 4.75 | 76 | 3.6348 | 0.0357 | | 0.6087 | 4.81 | 77 | 3.625 | 0.0355 | | 0.5236 | 4.88 | 78 | 3.6152 | 0.0355 | | 0.5458 | 4.94 | 79 | 3.5781 | 0.0355 | | 0.5702 | 5.0 | 80 | 3.5488 | 0.0355 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

okho0653/distilbert-base-zero-shot

okho0653

2022-11-07T20:44:16Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T20:40:31Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-zero-shot results: [] ---  # distilbert-base-zero-shot This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7147 - eval_accuracy: 0.0741 - eval_f1: 0.1379 - eval_runtime: 1.1794 - eval_samples_per_second: 22.894 - eval_steps_per_second: 1.696 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

Meow412/finetuning-sentiment-model-A3

Meow412

2022-11-07T20:39:27Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T20:30:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-A3 results: [] ---  # finetuning-sentiment-model-A3 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: 0.3212 - Accuracy: 0.8760 - F1: 0.3516 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

edbeeching/atari_zaxxon_3333

edbeeching

2022-11-07T20:31:59Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:30:50Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_zaxxon type: atari_zaxxon metrics: - type: mean_reward value: 12600.00 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_zaxxon** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_videopinball_3333

edbeeching

2022-11-07T20:27:56Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:26:42Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_videopinball type: atari_videopinball metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_videopinball** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_venture_3333

edbeeching

2022-11-07T20:26:22Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:25:21Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_venture type: atari_venture metrics: - type: mean_reward value: 1650.00 +/- 250.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_venture** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_upndown_3333

edbeeching

2022-11-07T20:25:01Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:23:31Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_upndown type: atari_upndown metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_upndown** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_tutankham_3333

edbeeching

2022-11-07T20:23:10Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:22:04Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tutankham type: atari_tutankham metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_tutankham** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_timepilot_3333

edbeeching

2022-11-07T20:21:45Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:20:54Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_timepilot type: atari_timepilot metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_timepilot** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_tennis_3333

edbeeching

2022-11-07T20:20:35Z

3

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:19:22Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tennis type: atari_tennis metrics: - type: mean_reward value: 24.00 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_tennis** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_spaceinvaders_3333

edbeeching

2022-11-07T20:17:41Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:16:43Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_spaceinvaders type: atari_spaceinvaders metrics: - type: mean_reward value: 2212.50 +/- 2.50 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_spaceinvaders** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_skiing_3333

edbeeching

2022-11-07T20:14:49Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:13:46Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_skiing type: atari_skiing metrics: - type: mean_reward value: -10184.29 +/- 133.20 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_skiing** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_seaquest_3333

edbeeching

2022-11-07T20:13:26Z

3

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:12:29Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_seaquest type: atari_seaquest metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_seaquest** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_roadrunner_3333

edbeeching

2022-11-07T20:10:27Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:09:13Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_roadrunner type: atari_roadrunner metrics: - type: mean_reward value: 84000.00 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_roadrunner** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

Ananjas/AwooAI

Ananjas

2022-11-07T20:08:29Z

105

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T19:37:57Z

--- tags: - conversational ---

edbeeching/atari_qbert_3333

edbeeching

2022-11-07T20:07:22Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-11-07T20:06:16Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_qbert type: atari_qbert metrics: - type: mean_reward value: nan +/- nan name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_qbert** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

artemnech/dialoT5-base

artemnech

2022-11-07T18:58:36Z

7

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-08-29T10:37:48Z

How to use: ``` from collections import deque from bs4 import BeautifulSoup import requests from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer import torch model_name = 'artemnech/dialoT5-base' model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) def generate(text, **kwargs): model.eval() inputs = tokenizer(text, return_tensors='pt').to(model.device) with torch.no_grad(): hypotheses = model.generate(**inputs, **kwargs) return tokenizer.decode(hypotheses[0], skip_special_tokens=True) def dialog(context): keyword = generate('keyword: ' + ' '.join(context), num_beams=2,) knowlege = '' if keyword != 'no_keywords': resp = requests.get(f"https://en.wikipedia.org/wiki/{keyword}") root = BeautifulSoup(resp.content, "html.parser") knowlege ="knowlege: " + " ".join([_.text.strip() for _ in root.find("div", class_="mw-body-content mw-content-ltr").find_all("p", limit=2)]) answ = generate(f'dialog: ' + knowlege + ' '.join(context), num_beams=3, do_sample=True, temperature=1.1, encoder_no_repeat_ngram_size=5, no_repeat_ngram_size=5, max_new_tokens = 30) return answ context =deque([], maxlen=4) while True: text = input() text = 'user1>>: ' + text context.append(text) answ = dialog(context) context.append('user2>>: ' + answ) print('bot: ', answ) ```

azuresonance/bert-finetuned-ner

azuresonance

2022-11-07T18:08:45Z

15

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T17:58:09Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9351422898742554 - name: Recall type: recall value: 0.9511948838774823 - name: F1 type: f1 value: 0.943100283664275 - name: Accuracy type: accuracy value: 0.9867251427562254 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9351 - Recall: 0.9512 - F1: 0.9431 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0861 | 1.0 | 1756 | 0.0691 | 0.9094 | 0.9322 | 0.9206 | 0.9809 | | 0.034 | 2.0 | 3512 | 0.0605 | 0.9303 | 0.9482 | 0.9392 | 0.9861 | | 0.0162 | 3.0 | 5268 | 0.0604 | 0.9351 | 0.9512 | 0.9431 | 0.9867 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

GuiGel/xlm-roberta-large-flert-finetune-meddocan

GuiGel

2022-11-07T17:36:11Z

3

0

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]

token-classification

2022-11-07T17:32:35Z

--- tags: - flair - token-classification - sequence-tagger-model --- ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("GuiGel/xlm-roberta-large-flert-finetune-meddocan") # make example sentence sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ```

mqymmayy/mt5-small-finetuned-amazon-en-es

mqymmayy

2022-11-07T16:44:48Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-11-07T14:21:59Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es results: [] ---  # mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0294 - Rouge1: 16.5993 - Rouge2: 8.0138 - Rougel: 16.1315 - Rougelsum: 16.2931 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 6.5928 | 1.0 | 1209 | 3.3005 | 14.7775 | 6.4604 | 14.2574 | 14.3422 | | 3.9024 | 2.0 | 2418 | 3.1399 | 16.8632 | 8.6474 | 16.065 | 16.2114 | | 3.5806 | 3.0 | 3627 | 3.0869 | 18.2422 | 9.2647 | 17.6227 | 17.7649 | | 3.4201 | 4.0 | 4836 | 3.0590 | 17.7826 | 8.9742 | 16.9951 | 17.1804 | | 3.3202 | 5.0 | 6045 | 3.0598 | 17.7808 | 8.6038 | 17.2243 | 17.4125 | | 3.2436 | 6.0 | 7254 | 3.0409 | 16.8469 | 8.2339 | 16.3935 | 16.5818 | | 3.2079 | 7.0 | 8463 | 3.0332 | 16.8148 | 8.2115 | 16.3166 | 16.4832 | | 3.1801 | 8.0 | 9672 | 3.0294 | 16.5993 | 8.0138 | 16.1315 | 16.2931 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

zhiguoxu/chinese-macbert-base-finetuned-ner

zhiguoxu

2022-11-07T15:45:01Z

13

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-23T12:03:17Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: chinese-macbert-base-finetuned-ner results: [] ---  # chinese-macbert-base-finetuned-ner This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2420 - F1: 0.9224 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 57 - eval_batch_size: 57 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6141 | 1.0 | 1 | 2.6454 | 0.0 | | 2.7076 | 2.0 | 2 | 2.0034 | 0.0 | | 2.0979 | 3.0 | 3 | 1.6276 | 0.0 | | 1.7264 | 4.0 | 4 | 1.3419 | 0.3522 | | 1.4691 | 5.0 | 5 | 1.1239 | 0.4091 | | 1.2504 | 6.0 | 6 | 0.9532 | 0.5514 | | 1.0798 | 7.0 | 7 | 0.8129 | 0.5895 | | 0.9279 | 8.0 | 8 | 0.6987 | 0.625 | | 0.8179 | 9.0 | 9 | 0.6081 | 0.6392 | | 0.7202 | 10.0 | 10 | 0.5346 | 0.6667 | | 0.6377 | 11.0 | 11 | 0.4731 | 0.7451 | | 0.5751 | 12.0 | 12 | 0.4226 | 0.7925 | | 0.5202 | 13.0 | 13 | 0.3804 | 0.7685 | | 0.4733 | 14.0 | 14 | 0.3447 | 0.7928 | | 0.44 | 15.0 | 15 | 0.3145 | 0.8509 | | 0.4047 | 16.0 | 16 | 0.2899 | 0.8918 | | 0.3773 | 17.0 | 17 | 0.2707 | 0.8966 | | 0.353 | 18.0 | 18 | 0.2563 | 0.9052 | | 0.3413 | 19.0 | 19 | 0.2468 | 0.9224 | | 0.3314 | 20.0 | 20 | 0.2420 | 0.9224 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0+cu102 - Datasets 1.18.4 - Tokenizers 0.12.1

Rundstedtz/distilbert-base-uncased-letters-from-jenny

Rundstedtz

2022-11-07T15:35:42Z

5

1

transformers

[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-07T15:27:32Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Rundstedtz/distilbert-base-uncased-letters-from-jenny results: [] ---  # Rundstedtz/distilbert-base-uncased-letters-from-jenny This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5319 - Validation Loss: 2.9614 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -988, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.5319 | 2.9614 | 0 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.2

kevinbror/Svenskmodell

kevinbror

2022-11-07T15:20:39Z

62

0

transformers

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "endpoints_compatible", "region:us" ]

question-answering

2022-11-07T15:20:15Z

--- tags: - generated_from_keras_callback model-index: - name: Svenskmodell results: [] ---  # Svenskmodell This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3411 - Train End Logits Accuracy: 0.8882 - Train Start Logits Accuracy: 0.8838 - Validation Loss: 1.4988 - Validation End Logits Accuracy: 0.6713 - Validation Start Logits Accuracy: 0.6669 - Epoch: 3 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 28792, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 1.2392 | 0.6241 | 0.6197 | 1.0970 | 0.6588 | 0.6543 | 0 | | 0.7549 | 0.7615 | 0.7569 | 1.1185 | 0.6673 | 0.6665 | 1 | | 0.5005 | 0.8376 | 0.8323 | 1.3299 | 0.6571 | 0.6503 | 2 | | 0.3411 | 0.8882 | 0.8838 | 1.4988 | 0.6713 | 0.6669 | 3 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.6.4 - Datasets 2.1.0 - Tokenizers 0.12.1

jasonsheih/bert-base-uncased-finetuned-vr-comfort-2125

jasonsheih

2022-11-07T13:58:05Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-05T13:26:15Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-base-uncased-finetuned-vr-comfort-2125 results: [] ---  # bert-base-uncased-finetuned-vr-comfort-2125 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0440 - Accuracy: 0.8431 - F1: 0.8437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7159 | 1.0 | 157 | 0.6408 | 0.7401 | 0.6612 | | 0.5559 | 2.0 | 314 | 0.5362 | 0.7952 | 0.7684 | | 0.362 | 3.0 | 471 | 0.5135 | 0.8204 | 0.8132 | | 0.1918 | 4.0 | 628 | 0.6109 | 0.8407 | 0.8388 | | 0.1192 | 5.0 | 785 | 0.6947 | 0.8347 | 0.8316 | | 0.0661 | 6.0 | 942 | 0.7843 | 0.8455 | 0.8467 | | 0.0507 | 7.0 | 1099 | 0.9312 | 0.8168 | 0.8271 | | 0.0406 | 8.0 | 1256 | 0.8616 | 0.8467 | 0.8488 | | 0.0268 | 9.0 | 1413 | 0.8403 | 0.8443 | 0.8478 | | 0.0251 | 10.0 | 1570 | 0.8662 | 0.8467 | 0.8472 | | 0.0188 | 11.0 | 1727 | 0.9418 | 0.8563 | 0.8530 | | 0.0195 | 12.0 | 1884 | 0.9541 | 0.8479 | 0.8469 | | 0.0172 | 13.0 | 2041 | 0.9372 | 0.8407 | 0.8413 | | 0.0142 | 14.0 | 2198 | 0.9883 | 0.8491 | 0.8469 | | 0.0156 | 15.0 | 2355 | 1.0150 | 0.8419 | 0.8428 | | 0.0138 | 16.0 | 2512 | 1.0035 | 0.8479 | 0.8466 | | 0.013 | 17.0 | 2669 | 1.0909 | 0.8299 | 0.8355 | | 0.0115 | 18.0 | 2826 | 1.0278 | 0.8515 | 0.8490 | | 0.0107 | 19.0 | 2983 | 1.0419 | 0.8431 | 0.8437 | | 0.0101 | 20.0 | 3140 | 1.0440 | 0.8431 | 0.8437 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

t-bank-ai/ruDialoGPT-medium

t-bank-ai

2022-11-07T13:34:43Z

1,169

35

transformers

[ "transformers", "pytorch", "gpt2", "conversational", "ru", "arxiv:2001.09977", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-07-12T14:52:19Z

--- license: mit widget: - text: "@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@" example_title: "how r u" - text: "@@ПЕРВЫЙ@@ что ты делал на выходных? @@ВТОРОЙ@@" example_title: "wyd" language: - ru tags: - conversational --- This generation model is based on [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents The model was trained with context size 3 On a private validation set we calculated metrics introduced in [this paper](https://arxiv.org/pdf/2001.09977.pdf): - Sensibleness: Crowdsourcers were asked whether model's response makes sense given the context - Specificity: Crowdsourcers were asked whether model's response is specific for given context, in other words we don't want our model to give general and boring responses - SSA which is the average of two metrics above (Sensibleness Specificity Average) | | sensibleness | specificity | SSA | |:----------------------------------------------------|---------------:|--------------:|------:| | [tinkoff-ai/ruDialoGPT-small](https://huggingface.co/tinkoff-ai/ruDialoGPT-small) | 0.64 | 0.5 | 0.57 | | [tinkoff-ai/ruDialoGPT-medium](https://huggingface.co/tinkoff-ai/ruDialoGPT-medium) | 0.78 | 0.69 | 0.735 | How to use: ```python import torch from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-medium') model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium') inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt') generated_token_ids = model.generate( **inputs, top_k=10, top_p=0.95, num_beams=3, num_return_sequences=3, do_sample=True, no_repeat_ngram_size=2, temperature=1.2, repetition_penalty=1.2, length_penalty=1.0, eos_token_id=50257, max_new_tokens=40 ) context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids] context_with_response ```

FacVain/turkish-sentiment-XMLRoBERTa

FacVain

2022-11-07T11:19:48Z

0

null

[ "tr", "region:us" ]

null

2022-11-07T09:57:24Z

--- language: tr tag: text-classification widget: - text: "Oldukça kullanışlı bir ürün." --- This repository contains two models that has been finetuned on twitter-XMLRoBERTa https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base. 3_Label model can classify text as positive, neutral and negative. 2_Label_Twitter is finetuned with tweets and can predict tweets as positive and negative.

tatakof/testpyramidsrnd

tatakof

2022-11-07T11:00:36Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2022-11-07T11:00:28Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: franfram/testpyramidsrnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

ronanki/xlm-r-multilingual-v1-2022-11-07

ronanki

2022-11-07T10:50:15Z

5

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-11-07T10:49:57Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ronanki/xlm-r-multilingual-v1-2022-11-07 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## 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('ronanki/xlm-r-multilingual-v1-2022-11-07') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ronanki/xlm-r-multilingual-v1-2022-11-07') model = AutoModel.from_pretrained('ronanki/xlm-r-multilingual-v1-2022-11-07') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ronanki/xlm-r-multilingual-v1-2022-11-07) ## Training The model was trained with the parameters: **DataLoader**: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 348 with parameters: ``` {'batch_size': 64} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1044, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

silveto/distilbert-base-uncased-finetuned-squad

silveto

2022-11-07T10:44:30Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-11-02T17:43:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # 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: 1.1531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2297 | 1.0 | 5533 | 1.1547 | | 0.9688 | 2.0 | 11066 | 1.1278 | | 0.763 | 3.0 | 16599 | 1.1531 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.10.3

Shyam-311/distilroberta-base-finetuned-wikitext2

Shyam-311

2022-11-07T10:34:57Z

164

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-07T10:01:02Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] ---  # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8340 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0843 | 1.0 | 2406 | 1.9226 | | 1.9913 | 2.0 | 4812 | 1.8820 | | 1.9597 | 3.0 | 7218 | 1.8214 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

nguyenkhoa2407/bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07

nguyenkhoa2407

2022-11-07T10:30:30Z

9

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:favsbot", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T10:23:28Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - favsbot metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07 results: - task: name: Token Classification type: token-classification dataset: name: favsbot type: favsbot config: default split: train args: default metrics: - name: Precision type: precision value: 0.8275862068965517 - name: Recall type: recall value: 0.96 - name: F1 type: f1 value: 0.888888888888889 - name: Accuracy type: accuracy value: 0.9444444444444444 ---  # bert-base-cased-NER-favsbot-no-apostrophe-2022-11-07 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1169 - Precision: 0.8276 - Recall: 0.96 - F1: 0.8889 - Accuracy: 0.9444 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 1.6302 | 0.0 | 0.0 | 0.0 | 0.5972 | | No log | 2.0 | 20 | 1.0453 | 0.6667 | 0.08 | 0.1429 | 0.6389 | | No log | 3.0 | 30 | 0.7286 | 0.8421 | 0.64 | 0.7273 | 0.8472 | | No log | 4.0 | 40 | 0.5296 | 0.8 | 0.8 | 0.8000 | 0.8889 | | No log | 5.0 | 50 | 0.3960 | 0.8214 | 0.92 | 0.8679 | 0.9306 | | No log | 6.0 | 60 | 0.2987 | 0.8214 | 0.92 | 0.8679 | 0.9306 | | No log | 7.0 | 70 | 0.2424 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 8.0 | 80 | 0.2151 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 9.0 | 90 | 0.1815 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 10.0 | 100 | 0.1675 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 11.0 | 110 | 0.1504 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 12.0 | 120 | 0.1410 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 13.0 | 130 | 0.1350 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 14.0 | 140 | 0.1281 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 15.0 | 150 | 0.1239 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 16.0 | 160 | 0.1190 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 17.0 | 170 | 0.1187 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 18.0 | 180 | 0.1180 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 19.0 | 190 | 0.1170 | 0.8276 | 0.96 | 0.8889 | 0.9444 | | No log | 20.0 | 200 | 0.1169 | 0.8276 | 0.96 | 0.8889 | 0.9444 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

julien-c/pokemon-predict-hp

julien-c

2022-11-07T10:11:46Z

0

1

mlconsole

[ "mlconsole", "tabular-regression", "dataset:julien-c/kaggle-rounakbanik-pokemon", "license:apache-2.0", "model-index", "region:us" ]

tabular-regression

2022-10-10T08:28:52Z

--- license: apache-2.0 tags: - mlconsole - tabular-regression library_name: mlconsole inference: false datasets: - julien-c/kaggle-rounakbanik-pokemon metrics: - mae - loss model-index: - name: pokemon-predict-hp results: - task: type: tabular-regression name: tabular-regression dataset: type: julien-c/kaggle-rounakbanik-pokemon name: pokemon.csv metrics: - type: mae name: Mean absolute error value: 15.908513069152832 - type: loss name: Model loss value: 647.6045532226562 --- # pokemon.csv (#0) Trained on [ML Console](https://mlconsole.com) on the [julien-c/kaggle-rounakbanik-pokemon](https://huggingface.co/datasets/julien-c/kaggle-rounakbanik-pokemon). [Load the model on ML Console](https://mlconsole.com/model/hf/julien-c/pokemon-predict-hp). ### Screenshots of training ![](screenshots/training-curve.png) ![](screenshots/predict.png)

Shyam-311/distilgpt2-finetuned-wikitext2

Shyam-311

2022-11-07T09:55:19Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T09:08:03Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6421 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.653 | 2.0 | 4668 | 3.6472 | | 3.6006 | 3.0 | 7002 | 3.6421 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

thisisHJLee/wav2vec2-large-xls-r-300m-korean-convsen3

thisisHJLee

2022-11-07T09:31:00Z

108

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-07T05:04:25Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-convsen3 results: [] ---  # wav2vec2-large-xls-r-300m-korean-convsen3 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: 0.0089 - Cer: 0.0010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5769 | 1.0 | 1762 | 0.0593 | 0.0124 | | 0.0927 | 2.0 | 3524 | 0.0106 | 0.0014 | | 0.0571 | 3.0 | 5286 | 0.0089 | 0.0010 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.11.0

pig4431/Sentiment140_BERT_5E

pig4431

2022-11-07T08:46:38Z

10

1

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:sentiment140", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T08:39:06Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - sentiment140 metrics: - accuracy model-index: - name: Sentiment140_BERT_5E results: - task: name: Text Classification type: text-classification dataset: name: sentiment140 type: sentiment140 config: sentiment140 split: train args: sentiment140 metrics: - name: Accuracy type: accuracy value: 0.82 ---  # Sentiment140_BERT_5E This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.7061 - Accuracy: 0.82 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6882 | 0.08 | 50 | 0.6047 | 0.7 | | 0.6223 | 0.16 | 100 | 0.5137 | 0.8067 | | 0.5463 | 0.24 | 150 | 0.4573 | 0.8067 | | 0.4922 | 0.32 | 200 | 0.4790 | 0.8 | | 0.4821 | 0.4 | 250 | 0.4207 | 0.8267 | | 0.4985 | 0.48 | 300 | 0.4267 | 0.8067 | | 0.4455 | 0.56 | 350 | 0.4301 | 0.8133 | | 0.469 | 0.64 | 400 | 0.4294 | 0.82 | | 0.4906 | 0.72 | 450 | 0.4059 | 0.8067 | | 0.4006 | 0.8 | 500 | 0.4181 | 0.8133 | | 0.445 | 0.88 | 550 | 0.3948 | 0.8267 | | 0.4302 | 0.96 | 600 | 0.3976 | 0.84 | | 0.4442 | 1.04 | 650 | 0.3887 | 0.8533 | | 0.3424 | 1.12 | 700 | 0.4119 | 0.8267 | | 0.3589 | 1.2 | 750 | 0.4083 | 0.8533 | | 0.3737 | 1.28 | 800 | 0.4253 | 0.8333 | | 0.334 | 1.36 | 850 | 0.4147 | 0.86 | | 0.3637 | 1.44 | 900 | 0.3926 | 0.8533 | | 0.3388 | 1.52 | 950 | 0.4084 | 0.8267 | | 0.3375 | 1.6 | 1000 | 0.4132 | 0.8467 | | 0.3725 | 1.68 | 1050 | 0.3965 | 0.8467 | | 0.3649 | 1.76 | 1100 | 0.3956 | 0.8333 | | 0.3799 | 1.84 | 1150 | 0.3923 | 0.8333 | | 0.3695 | 1.92 | 1200 | 0.4266 | 0.84 | | 0.3233 | 2.0 | 1250 | 0.4225 | 0.8333 | | 0.2313 | 2.08 | 1300 | 0.4672 | 0.8333 | | 0.231 | 2.16 | 1350 | 0.5212 | 0.8133 | | 0.2526 | 2.24 | 1400 | 0.5392 | 0.8067 | | 0.2721 | 2.32 | 1450 | 0.4895 | 0.82 | | 0.2141 | 2.4 | 1500 | 0.5258 | 0.8133 | | 0.2658 | 2.48 | 1550 | 0.5046 | 0.8267 | | 0.2386 | 2.56 | 1600 | 0.4873 | 0.8267 | | 0.2493 | 2.64 | 1650 | 0.4950 | 0.8333 | | 0.2692 | 2.72 | 1700 | 0.5080 | 0.8267 | | 0.2226 | 2.8 | 1750 | 0.5016 | 0.8467 | | 0.2522 | 2.88 | 1800 | 0.5068 | 0.8267 | | 0.2556 | 2.96 | 1850 | 0.4937 | 0.8267 | | 0.2311 | 3.04 | 1900 | 0.5103 | 0.8267 | | 0.1703 | 3.12 | 1950 | 0.5680 | 0.82 | | 0.1744 | 3.2 | 2000 | 0.5501 | 0.82 | | 0.1667 | 3.28 | 2050 | 0.6142 | 0.82 | | 0.1863 | 3.36 | 2100 | 0.6355 | 0.82 | | 0.2543 | 3.44 | 2150 | 0.6000 | 0.8133 | | 0.1565 | 3.52 | 2200 | 0.6618 | 0.8267 | | 0.1531 | 3.6 | 2250 | 0.6595 | 0.8133 | | 0.1915 | 3.68 | 2300 | 0.6647 | 0.8267 | | 0.1601 | 3.76 | 2350 | 0.6729 | 0.8267 | | 0.176 | 3.84 | 2400 | 0.6699 | 0.82 | | 0.1815 | 3.92 | 2450 | 0.6819 | 0.8067 | | 0.1987 | 4.0 | 2500 | 0.6543 | 0.8333 | | 0.1236 | 4.08 | 2550 | 0.6686 | 0.8333 | | 0.1599 | 4.16 | 2600 | 0.6583 | 0.8267 | | 0.1256 | 4.24 | 2650 | 0.6871 | 0.8267 | | 0.1291 | 4.32 | 2700 | 0.6855 | 0.82 | | 0.1198 | 4.4 | 2750 | 0.6901 | 0.82 | | 0.1245 | 4.48 | 2800 | 0.7152 | 0.8267 | | 0.1784 | 4.56 | 2850 | 0.7053 | 0.82 | | 0.1705 | 4.64 | 2900 | 0.7016 | 0.82 | | 0.1265 | 4.72 | 2950 | 0.7013 | 0.82 | | 0.1192 | 4.8 | 3000 | 0.7084 | 0.82 | | 0.174 | 4.88 | 3050 | 0.7062 | 0.82 | | 0.1328 | 4.96 | 3100 | 0.7061 | 0.82 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

cynthiachan/finetuned-bert-base

cynthiachan

2022-11-07T07:56:55Z

12

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:cynthiachan/FeedRef2022", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T06:51:03Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cynthiachan/FeedRef2022 model-index: - name: training results: [] ---  # training This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef2022 dataset. It achieves the following results on the evaluation set: - Loss: 0.0514 - Attackid Precision: 0.8889 - Attackid Recall: 0.9231 - Attackid F1: 0.9057 - Attackid Number: 52 - Bitcoinaddr Precision: 0.875 - Bitcoinaddr Recall: 1.0 - Bitcoinaddr F1: 0.9333 - Bitcoinaddr Number: 7 - Cve Precision: 0.8378 - Cve Recall: 0.9538 - Cve F1: 0.8921 - Cve Number: 65 - Defenderthreat Precision: 0.875 - Defenderthreat Recall: 1.0 - Defenderthreat F1: 0.9333 - Defenderthreat Number: 7 - Domain Precision: 0.9279 - Domain Recall: 0.9369 - Domain F1: 0.9324 - Domain Number: 206 - Email Precision: 0.8333 - Email Recall: 0.9302 - Email F1: 0.8791 - Email Number: 43 - Filepath Precision: 0.8857 - Filepath Recall: 0.9195 - Filepath F1: 0.9023 - Filepath Number: 1652 - Fingerprint Precision: 0.0 - Fingerprint Recall: 0.0 - Fingerprint F1: 0.0 - Fingerprint Number: 2 - Hostname Precision: 0.8910 - Hostname Recall: 0.9653 - Hostname F1: 0.9267 - Hostname Number: 144 - Ipv4 Precision: 0.9767 - Ipv4 Recall: 0.9825 - Ipv4 F1: 0.9796 - Ipv4 Number: 171 - Ipv6 Precision: 0.3333 - Ipv6 Recall: 1.0 - Ipv6 F1: 0.5 - Ipv6 Number: 3 - Md5 Precision: 0.9141 - Md5 Recall: 0.9857 - Md5 F1: 0.9486 - Md5 Number: 421 - Sha1 Precision: 0.8545 - Sha1 Recall: 0.9592 - Sha1 F1: 0.9038 - Sha1 Number: 49 - Sha256 Precision: 0.9120 - Sha256 Recall: 0.9919 - Sha256 F1: 0.9502 - Sha256 Number: 491 - Uri Precision: 0.3333 - Uri Recall: 0.4545 - Uri F1: 0.3846 - Uri Number: 11 - Overall Precision: 0.8946 - Overall Recall: 0.9446 - Overall F1: 0.9189 - Overall Accuracy: 0.9886 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Bitcoinaddr Precision | Bitcoinaddr Recall | Bitcoinaddr F1 | Bitcoinaddr Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Fingerprint Precision | Fingerprint Recall | Fingerprint F1 | Fingerprint Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Ipv6 Precision | Ipv6 Recall | Ipv6 F1 | Ipv6 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3691 | 0.04 | 500 | 0.3054 | 0.0 | 0.0 | 0.0 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 206 | 0.0 | 0.0 | 0.0 | 43 | 0.1917 | 0.5975 | 0.2903 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 144 | 0.5747 | 0.5848 | 0.5797 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.4160 | 0.7648 | 0.5389 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.5131 | 0.9145 | 0.6574 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.2665 | 0.5590 | 0.3610 | 0.9297 | | 0.2388 | 0.07 | 1000 | 0.2124 | 0.0 | 0.0 | 0.0 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7467 | 0.8615 | 0.8 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 206 | 0.0 | 0.0 | 0.0 | 43 | 0.3846 | 0.4661 | 0.4215 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.3534 | 0.6528 | 0.4585 | 144 | 0.6667 | 0.5614 | 0.6095 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5275 | 0.9097 | 0.6678 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8787 | 0.9002 | 0.8893 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.4932 | 0.5539 | 0.5218 | 0.9491 | | 0.1817 | 0.11 | 1500 | 0.2025 | 0.4433 | 0.8269 | 0.5772 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7941 | 0.8308 | 0.8120 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.2241 | 0.6602 | 0.3346 | 206 | 0.1538 | 0.2326 | 0.1852 | 43 | 0.4561 | 0.6816 | 0.5465 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.0042 | 0.0069 | 0.0052 | 144 | 0.6522 | 0.7018 | 0.6761 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5671 | 0.8527 | 0.6812 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7623 | 0.9470 | 0.8447 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.4654 | 0.6961 | 0.5579 | 0.9563 | | 0.1552 | 0.15 | 2000 | 0.1581 | 0.6119 | 0.7885 | 0.6891 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8235 | 0.8615 | 0.8421 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.4979 | 0.5680 | 0.5306 | 206 | 0.4795 | 0.8140 | 0.6034 | 43 | 0.4876 | 0.7960 | 0.6047 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5682 | 0.6944 | 0.625 | 144 | 0.4692 | 0.8012 | 0.5918 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5321 | 0.9240 | 0.6753 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7951 | 0.9328 | 0.8585 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.5345 | 0.7966 | 0.6398 | 0.9622 | | 0.1567 | 0.19 | 2500 | 0.1619 | 0.6032 | 0.7308 | 0.6609 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8133 | 0.9385 | 0.8714 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6257 | 0.5680 | 0.5954 | 206 | 0.1379 | 0.1860 | 0.1584 | 43 | 0.5788 | 0.7512 | 0.6538 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.4981 | 0.9097 | 0.6437 | 144 | 0.7233 | 0.8713 | 0.7905 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7723 | 0.9264 | 0.8423 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7523 | 0.9837 | 0.8526 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6308 | 0.7876 | 0.7006 | 0.9628 | | 0.1588 | 0.22 | 3000 | 0.1409 | 0.4050 | 0.9423 | 0.5665 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.5962 | 0.9538 | 0.7337 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6805 | 0.7961 | 0.7338 | 206 | 0.5821 | 0.9070 | 0.7091 | 43 | 0.6291 | 0.7712 | 0.6930 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6902 | 0.8819 | 0.7744 | 144 | 0.5737 | 0.8421 | 0.6825 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5678 | 0.9454 | 0.7094 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8582 | 0.9735 | 0.9122 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6300 | 0.8228 | 0.7136 | 0.9664 | | 0.1257 | 0.26 | 3500 | 0.1417 | 0.5541 | 0.7885 | 0.6508 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6854 | 0.9385 | 0.7922 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6828 | 0.7524 | 0.7159 | 206 | 0.5217 | 0.8372 | 0.6429 | 43 | 0.6314 | 0.7155 | 0.6708 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5261 | 0.9097 | 0.6667 | 144 | 0.7562 | 0.8889 | 0.8172 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7435 | 0.9501 | 0.8342 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7325 | 0.9817 | 0.8390 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6627 | 0.7942 | 0.7225 | 0.9658 | | 0.1229 | 0.3 | 4000 | 0.1455 | 0.6567 | 0.8462 | 0.7395 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6858 | 0.7524 | 0.7176 | 206 | 0.4321 | 0.8140 | 0.5645 | 43 | 0.6740 | 0.7809 | 0.7235 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6452 | 0.8333 | 0.7273 | 144 | 0.5455 | 0.5614 | 0.5533 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7697 | 0.8575 | 0.8112 | 421 | 0.3645 | 0.7959 | 0.5 | 49 | 0.6948 | 0.9735 | 0.8109 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6684 | 0.8029 | 0.7295 | 0.9667 | | 0.1323 | 0.34 | 4500 | 0.1323 | 0.6719 | 0.8269 | 0.7414 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7910 | 0.8154 | 0.8030 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6064 | 0.7330 | 0.6637 | 206 | 0.74 | 0.8605 | 0.7957 | 43 | 0.6802 | 0.7391 | 0.7084 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5935 | 0.5069 | 0.5468 | 144 | 0.7826 | 0.7368 | 0.7590 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7783 | 0.8171 | 0.7972 | 421 | 0.3810 | 0.8163 | 0.5195 | 49 | 0.8368 | 0.9715 | 0.8992 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7049 | 0.7717 | 0.7368 | 0.9680 | | 0.1379 | 0.37 | 5000 | 0.1088 | 0.5930 | 0.9808 | 0.7391 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.725 | 0.8923 | 0.8 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7619 | 0.6990 | 0.7291 | 206 | 0.5556 | 0.9302 | 0.6957 | 43 | 0.6551 | 0.8360 | 0.7346 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7127 | 0.8958 | 0.7938 | 144 | 0.7989 | 0.8596 | 0.8282 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7665 | 0.9359 | 0.8428 | 421 | 0.3729 | 0.4490 | 0.4074 | 49 | 0.7278 | 0.9695 | 0.8314 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6886 | 0.8550 | 0.7629 | 0.9738 | | 0.1162 | 0.41 | 5500 | 0.1205 | 0.5765 | 0.9423 | 0.7153 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8026 | 0.9385 | 0.8652 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7960 | 0.7767 | 0.7862 | 206 | 0.6032 | 0.8837 | 0.7170 | 43 | 0.6724 | 0.8099 | 0.7348 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6791 | 0.8819 | 0.7674 | 144 | 0.8041 | 0.9123 | 0.8548 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7188 | 0.9287 | 0.8104 | 421 | 0.5714 | 0.8163 | 0.6723 | 49 | 0.8088 | 0.9735 | 0.8835 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7033 | 0.8538 | 0.7713 | 0.9711 | | 0.1128 | 0.45 | 6000 | 0.1165 | 0.6575 | 0.9231 | 0.768 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7143 | 0.9231 | 0.8054 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7703 | 0.7816 | 0.7759 | 206 | 0.6724 | 0.9070 | 0.7723 | 43 | 0.6634 | 0.7706 | 0.7130 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6580 | 0.8819 | 0.7537 | 144 | 0.8434 | 0.8187 | 0.8309 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8032 | 0.9596 | 0.8745 | 421 | 0.6066 | 0.7551 | 0.6727 | 49 | 0.8554 | 0.9756 | 0.9115 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7201 | 0.8327 | 0.7723 | 0.9736 | | 0.11 | 0.49 | 6500 | 0.1374 | 0.7167 | 0.8269 | 0.7679 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7273 | 0.8615 | 0.7887 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7592 | 0.7039 | 0.7305 | 206 | 0.725 | 0.6744 | 0.6988 | 43 | 0.6129 | 0.7524 | 0.6755 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7151 | 0.8542 | 0.7785 | 144 | 0.7919 | 0.8012 | 0.7965 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7821 | 0.9549 | 0.8599 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.6880 | 0.9837 | 0.8097 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6710 | 0.8005 | 0.7300 | 0.9680 | | 0.1152 | 0.52 | 7000 | 0.1152 | 0.6933 | 1.0 | 0.8189 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6374 | 0.8923 | 0.7436 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6103 | 0.6311 | 0.6205 | 206 | 0.6739 | 0.7209 | 0.6966 | 43 | 0.6969 | 0.7960 | 0.7431 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7721 | 0.7292 | 0.75 | 144 | 0.8526 | 0.7778 | 0.8135 | 171 | 0.0192 | 0.3333 | 0.0364 | 3 | 0.8549 | 0.9097 | 0.8815 | 421 | 0.4706 | 0.8163 | 0.5970 | 49 | 0.8625 | 0.9837 | 0.9191 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7271 | 0.8216 | 0.7715 | 0.9722 | | 0.1084 | 0.56 | 7500 | 0.1073 | 0.75 | 0.8077 | 0.7778 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6484 | 0.9077 | 0.7564 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7313 | 0.8058 | 0.7667 | 206 | 0.6452 | 0.9302 | 0.7619 | 43 | 0.6933 | 0.8196 | 0.7512 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6818 | 0.9375 | 0.7895 | 144 | 0.6872 | 0.9123 | 0.7839 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8789 | 0.9477 | 0.9120 | 421 | 0.7451 | 0.7755 | 0.76 | 49 | 0.8374 | 0.9857 | 0.9055 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7277 | 0.8643 | 0.7902 | 0.9741 | | 0.0789 | 0.6 | 8000 | 0.0958 | 0.7719 | 0.8462 | 0.8073 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7403 | 0.8769 | 0.8028 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7731 | 0.8107 | 0.7915 | 206 | 0.74 | 0.8605 | 0.7957 | 43 | 0.7408 | 0.7924 | 0.7657 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6749 | 0.9514 | 0.7896 | 144 | 0.8011 | 0.8480 | 0.8239 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8299 | 0.9620 | 0.8911 | 421 | 0.5686 | 0.5918 | 0.58 | 49 | 0.8770 | 0.9878 | 0.9291 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7700 | 0.8469 | 0.8066 | 0.9760 | | 0.1149 | 0.64 | 8500 | 0.1334 | 1.0 | 0.7692 | 0.8696 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6795 | 0.8154 | 0.7413 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7336 | 0.7621 | 0.7476 | 206 | 0.3824 | 0.6047 | 0.4685 | 43 | 0.6318 | 0.5454 | 0.5854 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8227 | 0.8056 | 0.8140 | 144 | 0.7707 | 0.7076 | 0.7378 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8776 | 0.9026 | 0.8899 | 421 | 0.6129 | 0.7755 | 0.6847 | 49 | 0.8339 | 0.9817 | 0.9018 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7231 | 0.6961 | 0.7094 | 0.9673 | | 0.1155 | 0.67 | 9000 | 0.1052 | 0.6267 | 0.9038 | 0.7402 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7294 | 0.9538 | 0.8267 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7232 | 0.7864 | 0.7535 | 206 | 0.7391 | 0.7907 | 0.7640 | 43 | 0.7494 | 0.7312 | 0.7402 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7531 | 0.8472 | 0.7974 | 144 | 0.8708 | 0.9064 | 0.8883 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8340 | 0.9667 | 0.8955 | 421 | 0.5714 | 0.5714 | 0.5714 | 49 | 0.8709 | 0.9756 | 0.9203 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7664 | 0.8135 | 0.7893 | 0.9742 | | 0.0926 | 0.71 | 9500 | 0.1048 | 0.6438 | 0.9038 | 0.752 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6610 | 0.6 | 0.6290 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7541 | 0.6699 | 0.7095 | 206 | 0.7308 | 0.8837 | 0.8 | 43 | 0.6768 | 0.8456 | 0.7519 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7119 | 0.875 | 0.7850 | 144 | 0.8343 | 0.8830 | 0.8580 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8712 | 0.9477 | 0.9078 | 421 | 0.7193 | 0.8367 | 0.7736 | 49 | 0.8476 | 0.9857 | 0.9115 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7322 | 0.8604 | 0.7911 | 0.9760 | | 0.0982 | 0.75 | 10000 | 0.0985 | 0.6533 | 0.9423 | 0.7717 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7027 | 0.8 | 0.7482 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7671 | 0.8155 | 0.7906 | 206 | 0.7143 | 0.9302 | 0.8081 | 43 | 0.7465 | 0.8039 | 0.7741 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6507 | 0.9444 | 0.7705 | 144 | 0.9106 | 0.9532 | 0.9314 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8008 | 0.9264 | 0.8590 | 421 | 0.5641 | 0.8980 | 0.6929 | 49 | 0.8460 | 0.9735 | 0.9053 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7633 | 0.8568 | 0.8074 | 0.9769 | | 0.085 | 0.79 | 10500 | 0.0972 | 0.6184 | 0.9038 | 0.7344 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8154 | 0.8154 | 0.8154 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7236 | 0.8641 | 0.7876 | 206 | 0.7755 | 0.8837 | 0.8261 | 43 | 0.7544 | 0.8105 | 0.7814 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7081 | 0.9097 | 0.7964 | 144 | 0.8778 | 0.9240 | 0.9003 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8976 | 0.9572 | 0.9264 | 421 | 0.8039 | 0.8367 | 0.8200 | 49 | 0.8432 | 0.9857 | 0.9089 | 491 | 0.1111 | 0.0909 | 0.1000 | 11 | 0.7852 | 0.8643 | 0.8229 | 0.9779 | | 0.0981 | 0.82 | 11000 | 0.1092 | 0.6944 | 0.9615 | 0.8065 | 52 | 0.2 | 0.1429 | 0.1667 | 7 | 0.7262 | 0.9385 | 0.8188 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6842 | 0.8835 | 0.7712 | 206 | 0.6667 | 0.7907 | 0.7234 | 43 | 0.7117 | 0.8251 | 0.7642 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7159 | 0.875 | 0.7875 | 144 | 0.9337 | 0.9064 | 0.9199 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7175 | 0.9715 | 0.8254 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8620 | 0.9796 | 0.9171 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7399 | 0.8610 | 0.7959 | 0.9737 | | 0.0892 | 0.86 | 11500 | 0.0969 | 0.6049 | 0.9423 | 0.7368 | 52 | 0.4545 | 0.7143 | 0.5556 | 7 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8 | 0.8155 | 0.8077 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.6975 | 0.8571 | 0.7691 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7397 | 0.75 | 0.7448 | 144 | 0.8841 | 0.8480 | 0.8657 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8821 | 0.9596 | 0.9192 | 421 | 0.9474 | 0.7347 | 0.8276 | 49 | 0.8251 | 0.9511 | 0.8836 | 491 | 0.25 | 0.1818 | 0.2105 | 11 | 0.7557 | 0.8544 | 0.8020 | 0.9759 | | 0.0924 | 0.9 | 12000 | 0.0971 | 0.7059 | 0.9231 | 0.8000 | 52 | 0.4615 | 0.8571 | 0.6 | 7 | 0.8108 | 0.9231 | 0.8633 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7331 | 0.8932 | 0.8053 | 206 | 0.8 | 0.9302 | 0.8602 | 43 | 0.7544 | 0.8535 | 0.8009 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7697 | 0.8819 | 0.8220 | 144 | 0.8947 | 0.8947 | 0.8947 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7758 | 0.9454 | 0.8522 | 421 | 0.4516 | 0.8571 | 0.5915 | 49 | 0.8618 | 0.9776 | 0.9160 | 491 | 0.08 | 0.1818 | 0.1111 | 11 | 0.7664 | 0.8875 | 0.8225 | 0.9782 | | 0.0784 | 0.94 | 12500 | 0.1113 | 0.6623 | 0.9808 | 0.7907 | 52 | 0.6667 | 0.8571 | 0.75 | 7 | 0.8406 | 0.8923 | 0.8657 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6865 | 0.8398 | 0.7555 | 206 | 0.7547 | 0.9302 | 0.8333 | 43 | 0.7858 | 0.7863 | 0.7861 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8026 | 0.8472 | 0.8243 | 144 | 0.8629 | 0.8830 | 0.8728 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8462 | 0.9406 | 0.8909 | 421 | 0.56 | 0.8571 | 0.6774 | 49 | 0.9119 | 0.9695 | 0.9398 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.8022 | 0.8466 | 0.8238 | 0.9774 | | 0.1063 | 0.97 | 13000 | 0.0932 | 0.6538 | 0.9808 | 0.7846 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7838 | 0.8923 | 0.8345 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7738 | 0.8301 | 0.8009 | 206 | 0.75 | 0.8372 | 0.7912 | 43 | 0.6979 | 0.8529 | 0.7676 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7086 | 0.8611 | 0.7774 | 144 | 0.8703 | 0.9415 | 0.9045 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.6184 | 0.8931 | 0.7308 | 421 | 0.2424 | 0.1633 | 0.1951 | 49 | 0.8511 | 0.9776 | 0.9100 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7176 | 0.8646 | 0.7843 | 0.9760 | | 0.0765 | 1.01 | 13500 | 0.0892 | 0.6806 | 0.9423 | 0.7903 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6714 | 0.7231 | 0.6963 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8416 | 0.8252 | 0.8333 | 206 | 0.7917 | 0.8837 | 0.8352 | 43 | 0.7330 | 0.8559 | 0.7897 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7105 | 0.9375 | 0.8084 | 144 | 0.8757 | 0.9474 | 0.9101 | 171 | 0.125 | 1.0 | 0.2222 | 3 | 0.8769 | 0.9810 | 0.9260 | 421 | 0.5970 | 0.8163 | 0.6897 | 49 | 0.8761 | 0.9796 | 0.925 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7696 | 0.8881 | 0.8246 | 0.9790 | | 0.0677 | 1.05 | 14000 | 0.0804 | 0.6667 | 0.9231 | 0.7742 | 52 | 0.3333 | 0.7143 | 0.4545 | 7 | 0.7941 | 0.8308 | 0.8120 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8112 | 0.7718 | 0.7910 | 206 | 0.7234 | 0.7907 | 0.7556 | 43 | 0.7725 | 0.8487 | 0.8088 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7949 | 0.8611 | 0.8267 | 144 | 0.9401 | 0.9181 | 0.9290 | 171 | 0.1765 | 1.0 | 0.3 | 3 | 0.8613 | 0.9739 | 0.9142 | 421 | 0.4868 | 0.7551 | 0.592 | 49 | 0.8881 | 0.9857 | 0.9344 | 491 | 0.2222 | 0.1818 | 0.2000 | 11 | 0.7978 | 0.8782 | 0.8360 | 0.9805 | | 0.0544 | 1.09 | 14500 | 0.0924 | 0.9216 | 0.9038 | 0.9126 | 52 | 0.1875 | 0.4286 | 0.2609 | 7 | 0.7973 | 0.9077 | 0.8489 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7511 | 0.8641 | 0.8036 | 206 | 0.78 | 0.9070 | 0.8387 | 43 | 0.7361 | 0.8747 | 0.7994 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6569 | 0.9306 | 0.7701 | 144 | 0.9253 | 0.9415 | 0.9333 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9146 | 0.9667 | 0.9400 | 421 | 0.6308 | 0.8367 | 0.7193 | 49 | 0.8121 | 0.9857 | 0.8905 | 491 | 0.0833 | 0.1818 | 0.1143 | 11 | 0.7679 | 0.9025 | 0.8298 | 0.9793 | | 0.0797 | 1.12 | 15000 | 0.0851 | 0.9057 | 0.9231 | 0.9143 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7294 | 0.9538 | 0.8267 | 65 | 0.5 | 0.5714 | 0.5333 | 7 | 0.7909 | 0.8447 | 0.8169 | 206 | 0.8125 | 0.9070 | 0.8571 | 43 | 0.8104 | 0.8432 | 0.8265 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7 | 0.8264 | 0.7580 | 144 | 0.8804 | 0.9474 | 0.9127 | 171 | 0.2222 | 0.6667 | 0.3333 | 3 | 0.8834 | 0.9359 | 0.9089 | 421 | 0.5056 | 0.9184 | 0.6522 | 49 | 0.8436 | 0.9776 | 0.9057 | 491 | 0.0625 | 0.0909 | 0.0741 | 11 | 0.8077 | 0.8794 | 0.8420 | 0.9793 | | 0.0544 | 1.16 | 15500 | 0.0905 | 0.7 | 0.9423 | 0.8033 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6421 | 0.9385 | 0.7625 | 65 | 0.25 | 0.2857 | 0.2667 | 7 | 0.8018 | 0.8447 | 0.8227 | 206 | 0.7273 | 0.9302 | 0.8163 | 43 | 0.7642 | 0.8571 | 0.8080 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8098 | 0.9167 | 0.8599 | 144 | 0.9261 | 0.9532 | 0.9395 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.6976 | 0.9810 | 0.8154 | 421 | 0.6066 | 0.7551 | 0.6727 | 49 | 0.8948 | 0.9878 | 0.9390 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.7664 | 0.8953 | 0.8259 | 0.9793 | | 0.0815 | 1.2 | 16000 | 0.0799 | 0.9804 | 0.9615 | 0.9709 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6593 | 0.9231 | 0.7692 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.825 | 0.8010 | 0.8128 | 206 | 0.6667 | 0.9302 | 0.7767 | 43 | 0.7140 | 0.8523 | 0.7770 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7016 | 0.9306 | 0.8000 | 144 | 0.9096 | 0.9415 | 0.9253 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.7203 | 0.9359 | 0.8140 | 421 | 0.3193 | 0.7755 | 0.4524 | 49 | 0.8548 | 0.9470 | 0.8986 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.7339 | 0.8794 | 0.8001 | 0.9780 | | 0.0647 | 1.24 | 16500 | 0.0739 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7692 | 0.9231 | 0.8392 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8077 | 0.8155 | 0.8116 | 206 | 0.8 | 0.9302 | 0.8602 | 43 | 0.7750 | 0.8717 | 0.8205 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8467 | 0.8819 | 0.8639 | 144 | 0.92 | 0.9415 | 0.9306 | 171 | 0.0682 | 1.0 | 0.1277 | 3 | 0.8515 | 0.9810 | 0.9117 | 421 | 0.9318 | 0.8367 | 0.8817 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.1875 | 0.2727 | 0.2222 | 11 | 0.8066 | 0.8998 | 0.8507 | 0.9820 | | 0.0532 | 1.27 | 17000 | 0.0870 | 0.8491 | 0.8654 | 0.8571 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8657 | 0.8923 | 0.8788 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.7404 | 0.8447 | 0.7891 | 206 | 0.8163 | 0.9302 | 0.8696 | 43 | 0.8296 | 0.8547 | 0.8420 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8217 | 0.8958 | 0.8571 | 144 | 0.8931 | 0.8304 | 0.8606 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8369 | 0.9382 | 0.8847 | 421 | 0.9574 | 0.9184 | 0.9375 | 49 | 0.9026 | 0.9817 | 0.9405 | 491 | 0.5714 | 0.3636 | 0.4444 | 11 | 0.8367 | 0.8815 | 0.8585 | 0.9810 | | 0.0673 | 1.31 | 17500 | 0.0851 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.7024 | 0.9077 | 0.7919 | 65 | 0.4 | 0.2857 | 0.3333 | 7 | 0.7817 | 0.8689 | 0.8230 | 206 | 0.7959 | 0.9070 | 0.8478 | 43 | 0.8198 | 0.8511 | 0.8352 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7738 | 0.9028 | 0.8333 | 144 | 0.9162 | 0.9591 | 0.9371 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8655 | 0.9786 | 0.9186 | 421 | 0.775 | 0.6327 | 0.6966 | 49 | 0.8377 | 0.9776 | 0.9023 | 491 | 0.2143 | 0.2727 | 0.2400 | 11 | 0.8231 | 0.8902 | 0.8553 | 0.9816 | | 0.0715 | 1.35 | 18000 | 0.0821 | 0.8868 | 0.9038 | 0.8952 | 52 | 0.1 | 1.0 | 0.1818 | 7 | 0.6778 | 0.9385 | 0.7871 | 65 | 0.8 | 0.5714 | 0.6667 | 7 | 0.7653 | 0.7913 | 0.7780 | 206 | 0.78 | 0.9070 | 0.8387 | 43 | 0.7410 | 0.8989 | 0.8124 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7458 | 0.9167 | 0.8224 | 144 | 0.8713 | 0.8713 | 0.8713 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.8008 | 0.9644 | 0.875 | 421 | 0.4333 | 0.7959 | 0.5612 | 49 | 0.8920 | 0.9756 | 0.9319 | 491 | 0.8333 | 0.4545 | 0.5882 | 11 | 0.7578 | 0.9082 | 0.8262 | 0.9793 | | 0.0778 | 1.39 | 18500 | 0.0661 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.0714 | 0.1429 | 0.0952 | 7 | 0.8 | 0.9231 | 0.8571 | 65 | 1.0 | 0.2857 | 0.4444 | 7 | 0.8757 | 0.7864 | 0.8286 | 206 | 0.7547 | 0.9302 | 0.8333 | 43 | 0.7831 | 0.8674 | 0.8231 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8323 | 0.9306 | 0.8787 | 144 | 0.8859 | 0.9532 | 0.9183 | 171 | 0.1875 | 1.0 | 0.3158 | 3 | 0.9138 | 0.9572 | 0.9350 | 421 | 0.7963 | 0.8776 | 0.8350 | 49 | 0.8544 | 0.9919 | 0.9180 | 491 | 0.2 | 0.1818 | 0.1905 | 11 | 0.8172 | 0.8971 | 0.8553 | 0.9829 | | 0.0672 | 1.42 | 19000 | 0.0841 | 0.6538 | 0.9808 | 0.7846 | 52 | 0.2593 | 1.0 | 0.4118 | 7 | 0.6703 | 0.9385 | 0.7821 | 65 | 0.4 | 0.2857 | 0.3333 | 7 | 0.8162 | 0.7330 | 0.7724 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.7510 | 0.8747 | 0.8082 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7432 | 0.9444 | 0.8318 | 144 | 0.8477 | 0.9766 | 0.9076 | 171 | 0.1579 | 1.0 | 0.2727 | 3 | 0.8103 | 0.9739 | 0.8846 | 421 | 0.6327 | 0.6327 | 0.6327 | 49 | 0.7970 | 0.9674 | 0.8740 | 491 | 0.1190 | 0.4545 | 0.1887 | 11 | 0.7558 | 0.8977 | 0.8207 | 0.9787 | | 0.0802 | 1.46 | 19500 | 0.0682 | 0.8276 | 0.9231 | 0.8727 | 52 | 0.4615 | 0.8571 | 0.6 | 7 | 0.7468 | 0.9077 | 0.8194 | 65 | 0.3333 | 0.2857 | 0.3077 | 7 | 0.7621 | 0.8398 | 0.7991 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.7958 | 0.8801 | 0.8359 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7735 | 0.9722 | 0.8615 | 144 | 0.9357 | 0.9357 | 0.9357 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.8385 | 0.9620 | 0.8960 | 421 | 0.5556 | 0.9184 | 0.6923 | 49 | 0.8845 | 0.9674 | 0.9241 | 491 | 0.2778 | 0.4545 | 0.3448 | 11 | 0.8074 | 0.9070 | 0.8543 | 0.9819 | | 0.0886 | 1.5 | 20000 | 0.0633 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7568 | 0.8615 | 0.8058 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.8980 | 0.8544 | 0.8756 | 206 | 0.9302 | 0.9302 | 0.9302 | 43 | 0.8470 | 0.8916 | 0.8688 | 1652 | 0.25 | 1.0 | 0.4 | 2 | 0.8373 | 0.9653 | 0.8968 | 144 | 0.9032 | 0.9825 | 0.9412 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9044 | 0.9667 | 0.9346 | 421 | 0.7931 | 0.9388 | 0.8598 | 49 | 0.8342 | 0.9939 | 0.9071 | 491 | 0.1053 | 0.3636 | 0.1633 | 11 | 0.8471 | 0.9185 | 0.8814 | 0.9833 | | 0.0525 | 1.54 | 20500 | 0.0632 | 0.8197 | 0.9615 | 0.8850 | 52 | 0.7 | 1.0 | 0.8235 | 7 | 0.6742 | 0.9231 | 0.7792 | 65 | 0.4444 | 0.5714 | 0.5 | 7 | 0.7819 | 0.9223 | 0.8463 | 206 | 0.6721 | 0.9535 | 0.7885 | 43 | 0.8220 | 0.8723 | 0.8464 | 1652 | 0.0909 | 0.5 | 0.1538 | 2 | 0.7812 | 0.8681 | 0.8224 | 144 | 0.9180 | 0.9825 | 0.9492 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8838 | 0.9572 | 0.9190 | 421 | 0.5 | 0.9592 | 0.6573 | 49 | 0.8173 | 0.9837 | 0.8928 | 491 | 0.25 | 0.3636 | 0.2963 | 11 | 0.8092 | 0.9097 | 0.8565 | 0.9828 | | 0.0664 | 1.57 | 21000 | 0.0671 | 0.8197 | 0.9615 | 0.8850 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.6778 | 0.9385 | 0.7871 | 65 | 0.375 | 0.4286 | 0.4000 | 7 | 0.7932 | 0.9126 | 0.8488 | 206 | 0.72 | 0.8372 | 0.7742 | 43 | 0.7546 | 0.8935 | 0.8182 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7571 | 0.9306 | 0.8349 | 144 | 0.8777 | 0.9649 | 0.9192 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8867 | 0.9667 | 0.9250 | 421 | 0.8846 | 0.9388 | 0.9109 | 49 | 0.8199 | 0.9919 | 0.8977 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.7829 | 0.9221 | 0.8468 | 0.9830 | | 0.0524 | 1.61 | 21500 | 0.0674 | 0.8305 | 0.9423 | 0.8829 | 52 | 0.5833 | 1.0 | 0.7368 | 7 | 0.7763 | 0.9077 | 0.8369 | 65 | 0.375 | 0.4286 | 0.4000 | 7 | 0.8889 | 0.8544 | 0.8713 | 206 | 0.7692 | 0.9302 | 0.8421 | 43 | 0.8235 | 0.8838 | 0.8526 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9041 | 0.9167 | 0.9103 | 144 | 0.9527 | 0.9415 | 0.9471 | 171 | 0.4286 | 1.0 | 0.6 | 3 | 0.9470 | 0.9762 | 0.9614 | 421 | 0.7857 | 0.8980 | 0.8381 | 49 | 0.8857 | 0.9939 | 0.9367 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8555 | 0.9140 | 0.8838 | 0.9844 | | 0.0603 | 1.65 | 22000 | 0.0735 | 0.7812 | 0.9615 | 0.8621 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.9206 | 0.8923 | 0.9062 | 65 | 0.8 | 0.5714 | 0.6667 | 7 | 0.8062 | 0.8883 | 0.8453 | 206 | 0.6721 | 0.9535 | 0.7885 | 43 | 0.8402 | 0.8051 | 0.8223 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8036 | 0.9375 | 0.8654 | 144 | 0.9167 | 0.9649 | 0.9402 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9249 | 0.9359 | 0.9303 | 421 | 0.7077 | 0.9388 | 0.8070 | 49 | 0.9198 | 0.9817 | 0.9498 | 491 | 0.6667 | 0.5455 | 0.6 | 11 | 0.8558 | 0.8715 | 0.8636 | 0.9822 | | 0.0674 | 1.69 | 22500 | 0.0639 | 0.8103 | 0.9038 | 0.8545 | 52 | 0.2 | 0.2857 | 0.2353 | 7 | 0.7838 | 0.8923 | 0.8345 | 65 | 1.0 | 0.5714 | 0.7273 | 7 | 0.8852 | 0.8981 | 0.8916 | 206 | 0.8163 | 0.9302 | 0.8696 | 43 | 0.8393 | 0.8759 | 0.8572 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8618 | 0.9097 | 0.8851 | 144 | 0.8771 | 0.9181 | 0.8971 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9400 | 0.9667 | 0.9532 | 421 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9030 | 0.9857 | 0.9426 | 491 | 0.3846 | 0.4545 | 0.4167 | 11 | 0.8633 | 0.9064 | 0.8844 | 0.9843 | | 0.0693 | 1.72 | 23000 | 0.0773 | 0.7143 | 0.9615 | 0.8197 | 52 | 0.8571 | 0.8571 | 0.8571 | 7 | 0.8356 | 0.9385 | 0.8841 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8009 | 0.8786 | 0.8380 | 206 | 0.7119 | 0.9767 | 0.8235 | 43 | 0.7847 | 0.9001 | 0.8385 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7640 | 0.9444 | 0.8447 | 144 | 0.8836 | 0.9766 | 0.9278 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7143 | 0.9501 | 0.8155 | 421 | 0.3780 | 0.9796 | 0.5455 | 49 | 0.8134 | 0.9674 | 0.8837 | 491 | 0.5714 | 0.3636 | 0.4444 | 11 | 0.7688 | 0.9212 | 0.8381 | 0.9808 | | 0.0383 | 1.76 | 23500 | 0.0667 | 0.6410 | 0.9615 | 0.7692 | 52 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.7692 | 0.9231 | 0.8392 | 65 | 0.8333 | 0.7143 | 0.7692 | 7 | 0.8326 | 0.8689 | 0.8504 | 206 | 0.7636 | 0.9767 | 0.8571 | 43 | 0.8580 | 0.8777 | 0.8677 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8571 | 0.9167 | 0.8859 | 144 | 0.9405 | 0.9240 | 0.9322 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8901 | 0.9810 | 0.9333 | 421 | 0.88 | 0.8980 | 0.8889 | 49 | 0.9112 | 0.9817 | 0.9451 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.8628 | 0.9097 | 0.8856 | 0.9845 | | 0.0496 | 1.8 | 24000 | 0.0712 | 0.8 | 0.9231 | 0.8571 | 52 | 0.8571 | 0.8571 | 0.8571 | 7 | 0.7262 | 0.9385 | 0.8188 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8390 | 0.8350 | 0.8370 | 206 | 0.8889 | 0.9302 | 0.9091 | 43 | 0.8522 | 0.8692 | 0.8607 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8867 | 0.9236 | 0.9048 | 144 | 0.9598 | 0.9766 | 0.9681 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8963 | 0.9857 | 0.9389 | 421 | 0.7015 | 0.9592 | 0.8103 | 49 | 0.9412 | 0.9776 | 0.9590 | 491 | 0.25 | 0.5455 | 0.3429 | 11 | 0.8659 | 0.9073 | 0.8861 | 0.9848 | | 0.0465 | 1.84 | 24500 | 0.0612 | 0.6667 | 0.9615 | 0.7874 | 52 | 0.75 | 0.8571 | 0.8000 | 7 | 0.7625 | 0.9385 | 0.8414 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8287 | 0.8689 | 0.8483 | 206 | 0.7407 | 0.9302 | 0.8247 | 43 | 0.8236 | 0.8904 | 0.8557 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7919 | 0.9514 | 0.8644 | 144 | 0.9326 | 0.9708 | 0.9513 | 171 | 0.0513 | 0.6667 | 0.0952 | 3 | 0.9079 | 0.9834 | 0.9441 | 421 | 0.8958 | 0.8776 | 0.8866 | 49 | 0.9186 | 0.9878 | 0.9519 | 491 | 0.1765 | 0.2727 | 0.2143 | 11 | 0.8355 | 0.9212 | 0.8762 | 0.9853 | | 0.0446 | 1.87 | 25000 | 0.0662 | 0.6410 | 0.9615 | 0.7692 | 52 | 0.6364 | 1.0 | 0.7778 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.8333 | 0.7143 | 0.7692 | 7 | 0.9378 | 0.8786 | 0.9073 | 206 | 0.8333 | 0.9302 | 0.8791 | 43 | 0.8362 | 0.8747 | 0.8550 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8447 | 0.9444 | 0.8918 | 144 | 0.9598 | 0.9766 | 0.9681 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.92 | 0.9834 | 0.9506 | 421 | 0.9070 | 0.7959 | 0.8478 | 49 | 0.9186 | 0.9878 | 0.9519 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.8659 | 0.9131 | 0.8889 | 0.9851 | | 0.0496 | 1.91 | 25500 | 0.0653 | 0.7612 | 0.9808 | 0.8571 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8472 | 0.9385 | 0.8905 | 65 | 0.5 | 0.5714 | 0.5333 | 7 | 0.9158 | 0.8981 | 0.9069 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8487 | 0.8729 | 0.8606 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8889 | 0.9444 | 0.9158 | 144 | 0.9586 | 0.9474 | 0.9529 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9077 | 0.9810 | 0.9429 | 421 | 0.7895 | 0.9184 | 0.8491 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.5 | 0.2727 | 0.3529 | 11 | 0.8714 | 0.9137 | 0.8921 | 0.9854 | | 0.0689 | 1.95 | 26000 | 0.0689 | 0.8596 | 0.9423 | 0.8991 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7887 | 0.8615 | 0.8235 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.9064 | 0.8932 | 0.8998 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8217 | 0.9122 | 0.8646 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8232 | 0.9375 | 0.8766 | 144 | 0.9222 | 0.9708 | 0.9459 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8827 | 0.9834 | 0.9303 | 421 | 0.9744 | 0.7755 | 0.8636 | 49 | 0.8574 | 0.9919 | 0.9197 | 491 | 0.4286 | 0.2727 | 0.3333 | 11 | 0.8441 | 0.9299 | 0.8849 | 0.9842 | | 0.0465 | 1.99 | 26500 | 0.1060 | 0.8136 | 0.9231 | 0.8649 | 52 | 0.5 | 1.0 | 0.6667 | 7 | 0.7778 | 0.8615 | 0.8175 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8552 | 0.9175 | 0.8852 | 206 | 0.82 | 0.9535 | 0.8817 | 43 | 0.8698 | 0.8977 | 0.8835 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8904 | 0.9028 | 0.8966 | 144 | 0.9643 | 0.9474 | 0.9558 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7361 | 0.9739 | 0.8384 | 421 | 0.25 | 0.0612 | 0.0984 | 49 | 0.8832 | 0.9552 | 0.9178 | 491 | 0.1 | 0.1818 | 0.1290 | 11 | 0.8384 | 0.9040 | 0.8700 | 0.9796 | | 0.0448 | 2.02 | 27000 | 0.0686 | 0.7385 | 0.9231 | 0.8205 | 52 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8714 | 0.9385 | 0.9037 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8545 | 0.9126 | 0.8826 | 206 | 0.6727 | 0.8605 | 0.7551 | 43 | 0.8778 | 0.8959 | 0.8868 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9116 | 0.9306 | 0.9210 | 144 | 0.9538 | 0.9649 | 0.9593 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9157 | 0.9549 | 0.9349 | 421 | 0.875 | 0.8571 | 0.8660 | 49 | 0.8855 | 0.9919 | 0.9356 | 491 | 0.4 | 0.3636 | 0.3810 | 11 | 0.8790 | 0.9200 | 0.8990 | 0.9854 | | 0.0379 | 2.06 | 27500 | 0.0633 | 0.8421 | 0.9231 | 0.8807 | 52 | 0.2308 | 0.4286 | 0.3 | 7 | 0.8824 | 0.9231 | 0.9023 | 65 | 0.4545 | 0.7143 | 0.5556 | 7 | 0.8451 | 0.9272 | 0.8843 | 206 | 0.7037 | 0.8837 | 0.7835 | 43 | 0.8901 | 0.8674 | 0.8786 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8303 | 0.9514 | 0.8867 | 144 | 0.9706 | 0.9649 | 0.9677 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9300 | 0.9786 | 0.9537 | 421 | 0.9149 | 0.8776 | 0.8958 | 49 | 0.8385 | 0.9939 | 0.9096 | 491 | 0.2 | 0.3636 | 0.2581 | 11 | 0.8719 | 0.9116 | 0.8913 | 0.9859 | | 0.0352 | 2.1 | 28000 | 0.0653 | 0.8772 | 0.9615 | 0.9174 | 52 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8158 | 0.9538 | 0.8794 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8733 | 0.9369 | 0.9040 | 206 | 0.8913 | 0.9535 | 0.9213 | 43 | 0.8272 | 0.9128 | 0.8679 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8824 | 0.9375 | 0.9091 | 144 | 0.9706 | 0.9649 | 0.9677 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.8790 | 0.9834 | 0.9283 | 421 | 0.9 | 0.9184 | 0.9091 | 49 | 0.8692 | 0.9878 | 0.9247 | 491 | 0.25 | 0.4545 | 0.3226 | 11 | 0.8493 | 0.9377 | 0.8913 | 0.9844 | | 0.0328 | 2.14 | 28500 | 0.0599 | 0.8772 | 0.9615 | 0.9174 | 52 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8806 | 0.9077 | 0.8939 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8804 | 0.8932 | 0.8867 | 206 | 0.8542 | 0.9535 | 0.9011 | 43 | 0.8680 | 0.9074 | 0.8872 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.85 | 0.9444 | 0.8947 | 144 | 0.9701 | 0.9474 | 0.9586 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.9452 | 0.9834 | 0.9639 | 421 | 0.6714 | 0.9592 | 0.7899 | 49 | 0.8937 | 0.9756 | 0.9328 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8786 | 0.9293 | 0.9032 | 0.9867 | | 0.0473 | 2.17 | 29000 | 0.0595 | 0.7692 | 0.9615 | 0.8547 | 52 | 0.2222 | 0.2857 | 0.25 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.6667 | 0.5714 | 0.6154 | 7 | 0.8889 | 0.9320 | 0.9100 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8341 | 0.9189 | 0.8744 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8466 | 0.9583 | 0.8990 | 144 | 0.9711 | 0.9825 | 0.9767 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9234 | 0.9739 | 0.9480 | 421 | 0.75 | 0.9184 | 0.8257 | 49 | 0.8844 | 0.9817 | 0.9305 | 491 | 0.5556 | 0.4545 | 0.5 | 11 | 0.8557 | 0.9386 | 0.8953 | 0.9855 | | 0.0511 | 2.21 | 29500 | 0.0668 | 0.6849 | 0.9615 | 0.8000 | 52 | 0.1522 | 1.0 | 0.2642 | 7 | 0.7561 | 0.9538 | 0.8435 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8761 | 0.9272 | 0.9009 | 206 | 0.8039 | 0.9535 | 0.8723 | 43 | 0.8154 | 0.9195 | 0.8643 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8491 | 0.9375 | 0.8911 | 144 | 0.9709 | 0.9766 | 0.9738 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.8939 | 0.9810 | 0.9354 | 421 | 0.5789 | 0.8980 | 0.704 | 49 | 0.8403 | 0.9857 | 0.9072 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8214 | 0.9407 | 0.8770 | 0.9845 | | 0.0369 | 2.25 | 30000 | 0.0695 | 0.6579 | 0.9615 | 0.7812 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.9154 | 0.8932 | 0.9042 | 206 | 0.9535 | 0.9535 | 0.9535 | 43 | 0.8883 | 0.9001 | 0.8942 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9013 | 0.9514 | 0.9257 | 144 | 0.9527 | 0.9415 | 0.9471 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9126 | 0.9430 | 0.9276 | 421 | 0.5104 | 1.0 | 0.6759 | 49 | 0.9286 | 0.9796 | 0.9534 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8837 | 0.9233 | 0.9030 | 0.9854 | | 0.041 | 2.29 | 30500 | 0.0623 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.4375 | 1.0 | 0.6087 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9061 | 0.9369 | 0.9212 | 206 | 0.8723 | 0.9535 | 0.9111 | 43 | 0.8486 | 0.9225 | 0.8840 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8940 | 0.9375 | 0.9153 | 144 | 0.9708 | 0.9708 | 0.9708 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9556 | 0.9715 | 0.9635 | 421 | 0.7705 | 0.9592 | 0.8545 | 49 | 0.9310 | 0.9898 | 0.9595 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.8803 | 0.9428 | 0.9105 | 0.9853 | | 0.0385 | 2.32 | 31000 | 0.0632 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7848 | 0.9538 | 0.8611 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8915 | 0.9175 | 0.9043 | 206 | 0.9111 | 0.9535 | 0.9318 | 43 | 0.8486 | 0.9092 | 0.8778 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8961 | 0.9583 | 0.9262 | 144 | 0.9709 | 0.9766 | 0.9738 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9180 | 0.9834 | 0.9495 | 421 | 0.8478 | 0.7959 | 0.8211 | 49 | 0.8959 | 0.9817 | 0.9368 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8724 | 0.9338 | 0.9021 | 0.9849 | | 0.0415 | 2.36 | 31500 | 0.0647 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9363 | 0.9272 | 0.9317 | 206 | 0.875 | 0.9767 | 0.9231 | 43 | 0.8679 | 0.9025 | 0.8849 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8726 | 0.9514 | 0.9103 | 144 | 0.9429 | 0.9649 | 0.9538 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9154 | 0.9762 | 0.9448 | 421 | 0.7719 | 0.8980 | 0.8302 | 49 | 0.9067 | 0.9898 | 0.9464 | 491 | 0.1579 | 0.5455 | 0.2449 | 11 | 0.8767 | 0.9329 | 0.9039 | 0.9847 | | 0.0454 | 2.4 | 32000 | 0.0606 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8971 | 0.9385 | 0.9173 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9139 | 0.9272 | 0.9205 | 206 | 0.8542 | 0.9535 | 0.9011 | 43 | 0.8652 | 0.9056 | 0.8849 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9122 | 0.9375 | 0.9247 | 144 | 0.9483 | 0.9649 | 0.9565 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9321 | 0.9786 | 0.9548 | 421 | 0.8136 | 0.9796 | 0.8889 | 49 | 0.9455 | 0.9898 | 0.9672 | 491 | 0.2143 | 0.5455 | 0.3077 | 11 | 0.888 | 0.9350 | 0.9109 | 0.9869 | | 0.0334 | 2.44 | 32500 | 0.0610 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8714 | 0.9385 | 0.9037 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8977 | 0.9369 | 0.9169 | 206 | 0.9130 | 0.9767 | 0.9438 | 43 | 0.8463 | 0.9068 | 0.8755 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9007 | 0.9444 | 0.9220 | 144 | 0.9532 | 0.9532 | 0.9532 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9536 | 0.9762 | 0.9648 | 421 | 0.8545 | 0.9592 | 0.9038 | 49 | 0.9419 | 0.9898 | 0.9652 | 491 | 0.25 | 0.5455 | 0.3429 | 11 | 0.8813 | 0.9356 | 0.9076 | 0.9867 | | 0.0453 | 2.47 | 33000 | 0.0610 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.9057 | 0.9320 | 0.9187 | 206 | 0.9302 | 0.9302 | 0.9302 | 43 | 0.8668 | 0.9098 | 0.8878 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8882 | 0.9375 | 0.9122 | 144 | 0.9588 | 0.9532 | 0.9560 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9303 | 0.9834 | 0.9561 | 421 | 1.0 | 0.8980 | 0.9462 | 49 | 0.9455 | 0.9898 | 0.9672 | 491 | 0.7143 | 0.4545 | 0.5556 | 11 | 0.8952 | 0.9353 | 0.9148 | 0.9875 | | 0.0225 | 2.51 | 33500 | 0.0607 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7949 | 0.9538 | 0.8671 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8733 | 0.9369 | 0.9040 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8641 | 0.9007 | 0.8820 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8616 | 0.9514 | 0.9043 | 144 | 0.9412 | 0.9357 | 0.9384 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9281 | 0.9810 | 0.9538 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.9365 | 0.9919 | 0.9634 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8844 | 0.9323 | 0.9077 | 0.9876 | | 0.0276 | 2.55 | 34000 | 0.0603 | 0.8909 | 0.9423 | 0.9159 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.775 | 0.9538 | 0.8552 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.8894 | 0.9369 | 0.9125 | 206 | 0.9111 | 0.9535 | 0.9318 | 43 | 0.8661 | 0.9201 | 0.8923 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8688 | 0.9653 | 0.9145 | 144 | 0.9649 | 0.9649 | 0.9649 | 171 | 0.1667 | 1.0 | 0.2857 | 3 | 0.9649 | 0.9786 | 0.9717 | 421 | 0.9020 | 0.9388 | 0.92 | 49 | 0.9222 | 0.9898 | 0.9548 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8868 | 0.9428 | 0.9140 | 0.9877 | | 0.0291 | 2.59 | 34500 | 0.0605 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9091 | 0.9223 | 0.9157 | 206 | 0.9524 | 0.9302 | 0.9412 | 43 | 0.8707 | 0.9213 | 0.8953 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8947 | 0.9444 | 0.9189 | 144 | 0.9758 | 0.9415 | 0.9583 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9448 | 0.9762 | 0.9603 | 421 | 0.9787 | 0.9388 | 0.9583 | 49 | 0.8952 | 0.9919 | 0.9411 | 491 | 0.2632 | 0.4545 | 0.3333 | 11 | 0.8885 | 0.9401 | 0.9136 | 0.9881 | | 0.0264 | 2.62 | 35000 | 0.0616 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9019 | 0.9369 | 0.9190 | 206 | 0.8913 | 0.9535 | 0.9213 | 43 | 0.8694 | 0.9310 | 0.8992 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8782 | 0.9514 | 0.9133 | 144 | 0.9422 | 0.9532 | 0.9477 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9258 | 0.9786 | 0.9515 | 421 | 0.8679 | 0.9388 | 0.9020 | 49 | 0.9272 | 0.9857 | 0.9556 | 491 | 0.1852 | 0.4545 | 0.2632 | 11 | 0.8837 | 0.9465 | 0.9140 | 0.9875 | | 0.0343 | 2.66 | 35500 | 0.0595 | 0.7083 | 0.9808 | 0.8226 | 52 | 0.6667 | 0.8571 | 0.75 | 7 | 0.7949 | 0.9538 | 0.8671 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8858 | 0.9417 | 0.9129 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.8556 | 0.9110 | 0.8824 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8616 | 0.9514 | 0.9043 | 144 | 0.9270 | 0.9649 | 0.9456 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9388 | 0.9834 | 0.9606 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.8919 | 0.9919 | 0.9392 | 491 | 0.625 | 0.4545 | 0.5263 | 11 | 0.8728 | 0.9389 | 0.9046 | 0.9871 | | 0.0284 | 2.7 | 36000 | 0.0569 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.9143 | 0.9320 | 0.9231 | 206 | 0.9070 | 0.9070 | 0.9070 | 43 | 0.8724 | 0.9189 | 0.8950 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9145 | 0.9653 | 0.9392 | 144 | 0.9540 | 0.9708 | 0.9623 | 171 | 0.15 | 1.0 | 0.2609 | 3 | 0.9605 | 0.9810 | 0.9706 | 421 | 0.8364 | 0.9388 | 0.8846 | 49 | 0.8907 | 0.9959 | 0.9404 | 491 | 0.625 | 0.4545 | 0.5263 | 11 | 0.8865 | 0.9425 | 0.9137 | 0.9878 | | 0.0377 | 2.74 | 36500 | 0.0554 | 0.7083 | 0.9808 | 0.8226 | 52 | 0.5833 | 1.0 | 0.7368 | 7 | 0.7654 | 0.9538 | 0.8493 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8981 | 0.9417 | 0.9194 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.8700 | 0.9237 | 0.8961 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9205 | 0.9653 | 0.9424 | 144 | 0.9540 | 0.9708 | 0.9623 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9063 | 0.9881 | 0.9455 | 421 | 0.9020 | 0.9388 | 0.92 | 49 | 0.8825 | 0.9939 | 0.9349 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8755 | 0.9477 | 0.9101 | 0.9883 | | 0.0316 | 2.77 | 37000 | 0.0562 | 0.6711 | 0.9808 | 0.7969 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.9143 | 0.9320 | 0.9231 | 206 | 0.9524 | 0.9302 | 0.9412 | 43 | 0.8721 | 0.9243 | 0.8974 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9026 | 0.9653 | 0.9329 | 144 | 0.9653 | 0.9766 | 0.9709 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9202 | 0.9857 | 0.9518 | 421 | 0.8070 | 0.9388 | 0.8679 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8801 | 0.9471 | 0.9123 | 0.9885 | | 0.0454 | 2.81 | 37500 | 0.0555 | 0.8333 | 0.9615 | 0.8929 | 52 | 0.5 | 1.0 | 0.6667 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.9023 | 0.9417 | 0.9216 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8782 | 0.9249 | 0.9009 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8642 | 0.9722 | 0.9150 | 144 | 0.9337 | 0.9883 | 0.9602 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9498 | 0.9881 | 0.9686 | 421 | 0.94 | 0.9592 | 0.9495 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8845 | 0.9492 | 0.9157 | 0.9881 | | 0.0445 | 2.85 | 38000 | 0.0521 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9019 | 0.9369 | 0.9190 | 206 | 0.8511 | 0.9302 | 0.8889 | 43 | 0.8769 | 0.9183 | 0.8971 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8797 | 0.9653 | 0.9205 | 144 | 0.9767 | 0.9825 | 0.9796 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9243 | 0.9857 | 0.9540 | 421 | 0.8103 | 0.9592 | 0.8785 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8845 | 0.9443 | 0.9134 | 0.9884 | | 0.0379 | 2.89 | 38500 | 0.0524 | 0.8727 | 0.9231 | 0.8972 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8611 | 0.9538 | 0.9051 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9147 | 0.9369 | 0.9257 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8903 | 0.9183 | 0.9041 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8742 | 0.9653 | 0.9175 | 144 | 0.9711 | 0.9825 | 0.9767 | 171 | 0.2308 | 1.0 | 0.375 | 3 | 0.9101 | 0.9857 | 0.9464 | 421 | 0.8214 | 0.9388 | 0.8762 | 49 | 0.9067 | 0.9898 | 0.9464 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8932 | 0.9434 | 0.9176 | 0.9885 | | 0.0372 | 2.92 | 39000 | 0.0514 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9279 | 0.9369 | 0.9324 | 206 | 0.8333 | 0.9302 | 0.8791 | 43 | 0.8857 | 0.9195 | 0.9023 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8910 | 0.9653 | 0.9267 | 144 | 0.9767 | 0.9825 | 0.9796 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9141 | 0.9857 | 0.9486 | 421 | 0.8545 | 0.9592 | 0.9038 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.8946 | 0.9446 | 0.9189 | 0.9886 | | 0.0263 | 2.96 | 39500 | 0.0515 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9190 | 0.9369 | 0.9279 | 206 | 0.8511 | 0.9302 | 0.8889 | 43 | 0.8868 | 0.9201 | 0.9031 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8910 | 0.9653 | 0.9267 | 144 | 0.9825 | 0.9825 | 0.9825 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9326 | 0.9857 | 0.9584 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.9137 | 0.9919 | 0.9512 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8982 | 0.9449 | 0.9210 | 0.9885 | | 0.0242 | 3.0 | 40000 | 0.0518 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8267 | 0.9538 | 0.8857 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9190 | 0.9369 | 0.9279 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8827 | 0.9201 | 0.9010 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8688 | 0.9653 | 0.9145 | 144 | 0.9825 | 0.9825 | 0.9825 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.9220 | 0.9834 | 0.9517 | 421 | 0.9038 | 0.9592 | 0.9307 | 49 | 0.9086 | 0.9919 | 0.9484 | 491 | 0.3846 | 0.4545 | 0.4167 | 11 | 0.8933 | 0.9446 | 0.9183 | 0.9885 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

pig4431/Sentiment140_ALBERT_5E

pig4431

2022-11-07T07:45:04Z

105

0

transformers

[ "transformers", "pytorch", "albert", "text-classification", "generated_from_trainer", "dataset:sentiment140", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T07:44:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - sentiment140 metrics: - accuracy model-index: - name: Sentiment140_ALBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: sentiment140 type: sentiment140 config: sentiment140 split: train args: sentiment140 metrics: - name: Accuracy type: accuracy value: 0.8533333333333334 ---  # Sentiment140_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the sentiment140 dataset. It achieves the following results on the evaluation set: - Loss: 0.6103 - Accuracy: 0.8533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6713 | 0.08 | 50 | 0.5704 | 0.7333 | | 0.5742 | 0.16 | 100 | 0.4620 | 0.8 | | 0.5104 | 0.24 | 150 | 0.5536 | 0.74 | | 0.5313 | 0.32 | 200 | 0.5198 | 0.76 | | 0.5023 | 0.4 | 250 | 0.4286 | 0.8 | | 0.4871 | 0.48 | 300 | 0.4294 | 0.8267 | | 0.4513 | 0.56 | 350 | 0.4349 | 0.8133 | | 0.4647 | 0.64 | 400 | 0.4046 | 0.8333 | | 0.4827 | 0.72 | 450 | 0.4218 | 0.8333 | | 0.4517 | 0.8 | 500 | 0.4093 | 0.82 | | 0.4417 | 0.88 | 550 | 0.3999 | 0.84 | | 0.4701 | 0.96 | 600 | 0.3779 | 0.8867 | | 0.397 | 1.04 | 650 | 0.3730 | 0.8667 | | 0.3377 | 1.12 | 700 | 0.3833 | 0.8333 | | 0.411 | 1.2 | 750 | 0.3704 | 0.84 | | 0.3796 | 1.28 | 800 | 0.3472 | 0.86 | | 0.3523 | 1.36 | 850 | 0.3512 | 0.8733 | | 0.3992 | 1.44 | 900 | 0.3712 | 0.84 | | 0.3641 | 1.52 | 950 | 0.3718 | 0.82 | | 0.3973 | 1.6 | 1000 | 0.3508 | 0.84 | | 0.3576 | 1.68 | 1050 | 0.3600 | 0.86 | | 0.3701 | 1.76 | 1100 | 0.3287 | 0.8667 | | 0.3721 | 1.84 | 1150 | 0.3794 | 0.82 | | 0.3673 | 1.92 | 1200 | 0.3378 | 0.8733 | | 0.4223 | 2.0 | 1250 | 0.3508 | 0.86 | | 0.2745 | 2.08 | 1300 | 0.3835 | 0.86 | | 0.283 | 2.16 | 1350 | 0.3500 | 0.8533 | | 0.2769 | 2.24 | 1400 | 0.3334 | 0.8733 | | 0.2491 | 2.32 | 1450 | 0.3519 | 0.8867 | | 0.3237 | 2.4 | 1500 | 0.3438 | 0.86 | | 0.2662 | 2.48 | 1550 | 0.3513 | 0.8667 | | 0.2423 | 2.56 | 1600 | 0.3413 | 0.8867 | | 0.2655 | 2.64 | 1650 | 0.3126 | 0.8933 | | 0.2516 | 2.72 | 1700 | 0.3333 | 0.8733 | | 0.252 | 2.8 | 1750 | 0.3316 | 0.88 | | 0.2872 | 2.88 | 1800 | 0.3227 | 0.9 | | 0.306 | 2.96 | 1850 | 0.3383 | 0.8733 | | 0.248 | 3.04 | 1900 | 0.3474 | 0.8733 | | 0.1507 | 3.12 | 1950 | 0.4140 | 0.8667 | | 0.1994 | 3.2 | 2000 | 0.3729 | 0.8533 | | 0.167 | 3.28 | 2050 | 0.3782 | 0.8867 | | 0.1872 | 3.36 | 2100 | 0.4352 | 0.8867 | | 0.1611 | 3.44 | 2150 | 0.4511 | 0.8667 | | 0.2338 | 3.52 | 2200 | 0.4244 | 0.8533 | | 0.1538 | 3.6 | 2250 | 0.4226 | 0.8733 | | 0.1561 | 3.68 | 2300 | 0.4126 | 0.88 | | 0.2156 | 3.76 | 2350 | 0.4382 | 0.86 | | 0.1684 | 3.84 | 2400 | 0.4969 | 0.86 | | 0.1917 | 3.92 | 2450 | 0.4439 | 0.8667 | | 0.1584 | 4.0 | 2500 | 0.4759 | 0.86 | | 0.1038 | 4.08 | 2550 | 0.5042 | 0.8667 | | 0.0983 | 4.16 | 2600 | 0.5527 | 0.8533 | | 0.1404 | 4.24 | 2650 | 0.5801 | 0.84 | | 0.0844 | 4.32 | 2700 | 0.5884 | 0.86 | | 0.1347 | 4.4 | 2750 | 0.5865 | 0.8467 | | 0.1373 | 4.48 | 2800 | 0.5915 | 0.8533 | | 0.1506 | 4.56 | 2850 | 0.5976 | 0.8467 | | 0.1007 | 4.64 | 2900 | 0.6678 | 0.82 | | 0.1311 | 4.72 | 2950 | 0.6082 | 0.8533 | | 0.1402 | 4.8 | 3000 | 0.6180 | 0.8467 | | 0.1363 | 4.88 | 3050 | 0.6107 | 0.8533 | | 0.0995 | 4.96 | 3100 | 0.6103 | 0.8533 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.1

ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab

ntsema

2022-11-07T07:24:16Z

132

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-07T04:24:31Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Wer type: wer value: 0.2246858832224686 ---  # wav2vec2-xlsr-53-espeak-cv-ft-sah-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Wer: 0.2247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.7431 | 5.71 | 400 | 0.2879 | 0.4054 | | 0.1876 | 11.42 | 800 | 0.2349 | 0.3023 | | 0.0986 | 17.14 | 1200 | 0.2248 | 0.2701 | | 0.0737 | 22.85 | 1600 | 0.2242 | 0.2428 | | 0.0546 | 28.57 | 2000 | 0.2143 | 0.2247 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.14.0.dev20221105+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1

tkubotake/xlm-roberta-base-finetuned-panx-en

tkubotake

2022-11-07T05:12:03Z

6

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T03:30:35Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: train args: PAN-X.en metrics: - name: F1 type: f1 value: 0.7580275229357799 ---  # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.5430 - F1: 0.7580 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1318 | 1.0 | 50 | 0.4145 | 0.7557 | | 0.0589 | 2.0 | 100 | 0.5016 | 0.7524 | | 0.0314 | 3.0 | 150 | 0.5430 | 0.7580 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

tkubotake/xlm-roberta-base-finetuned-panx-it

tkubotake

2022-11-07T04:56:08Z

8

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T03:14:56Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: train args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8602239734549979 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2762 - F1: 0.8602 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1073 | 1.0 | 70 | 0.2783 | 0.8554 | | 0.0728 | 2.0 | 140 | 0.2651 | 0.8605 | | 0.0409 | 3.0 | 210 | 0.2762 | 0.8602 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

huggingtweets/mhhmmad_

huggingtweets

2022-11-07T04:41:10Z

107

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T04:41:03Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1355122703036936198/SDlJIKsr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Mohammad Hassan</div> <div style="text-align: center; font-size: 14px;">@mhhmmad_</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Mohammad Hassan. | Data | Mohammad Hassan | | --- | --- | | Tweets downloaded | 3017 | | Retweets | 679 | | Short tweets | 201 | | Tweets kept | 2137 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wifnwvu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mhhmmad_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mhhmmad_') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

rymaju/t5-small-finetuned-en-to-regex

rymaju

2022-11-07T04:31:37Z

63

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-07T01:32:59Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: t5-small-finetuned-en-to-regex results: [] ---  # t5-small-finetuned-en-to-regex This model is a fine-tuned version of [rymaju/t5-small-finetuned-en-to-regex](https://huggingface.co/rymaju/t5-small-finetuned-en-to-regex) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0032 - Bleu: 12.1984 - Gen Len: 16.7502 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.0092 | 1.0 | 6188 | 0.0043 | 12.1984 | 16.7522 | | 0.0069 | 2.0 | 12376 | 0.0040 | 12.2039 | 16.7502 | | 0.0056 | 3.0 | 18564 | 0.0034 | 12.2091 | 16.7483 | | 0.0048 | 4.0 | 24752 | 0.0035 | 12.2103 | 16.7502 | | 0.0049 | 5.0 | 30940 | 0.0035 | 12.1984 | 16.7502 | | 0.0046 | 6.0 | 37128 | 0.0033 | 12.1984 | 16.7502 | | 0.0046 | 7.0 | 43316 | 0.0035 | 12.1984 | 16.7502 | | 0.0046 | 8.0 | 49504 | 0.0032 | 12.1984 | 16.7502 | | 0.0042 | 9.0 | 55692 | 0.0032 | 12.1984 | 16.7502 | | 0.0043 | 10.0 | 61880 | 0.0032 | 12.1984 | 16.7502 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

jrtec/jrtec-gpt2-text-generation-quotes-jonathan-vargas

jrtec

2022-11-07T04:26:10Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "quotes", "quote", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-06T03:21:37Z

--- license: mit tags: - text-generation - quotes - quote - generated_from_trainer model-index: - name: jrtec-gpt2-text-generation-quotes-jonathan-vargas results: [] widget: - text: "life: " example_title: "Life quote" - text: "death: " example_title: "Death quote" ---  # jrtec-gpt2-text-generation-quotes-jonathan-vargas This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.7033 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7463 | 1.71 | 500 | 0.7033 | | 0.4281 | 3.41 | 1000 | 0.7084 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

Marve271/BartConditionalGeneration-bart-large-finetuned-insult

Marve271

2022-11-07T04:05:25Z

182

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-06T19:15:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: BartConditionalGeneration-bart-large-finetuned-insult results: [] ---  # BartConditionalGeneration-bart-large-finetuned-insult This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6217 | 1.0 | 568 | 4.5864 | | 4.7444 | 2.0 | 1136 | nan | | 4.2308 | 3.0 | 1704 | 3.7590 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

salascorp/categorizacion_comercios_v_0.0.7

salascorp

2022-11-07T03:24:01Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-07T02:51:40Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer metrics: - accuracy model-index: - name: categorizacion_comercios_v_0.0.7 results: [] ---  # categorizacion_comercios_v_0.0.7 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Accuracy: 0.9125 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0+cpu - Datasets 2.6.1 - Tokenizers 0.13.1

jinhybr/OCR-LayoutLMv3-Invoice

jinhybr

2022-11-07T02:11:33Z

113

7

transformers

[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:wild_receipt", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-07T01:13:54Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - wild_receipt metrics: - precision - recall - f1 - accuracy model-index: - name: OCR-LayoutLMv3-Invoice results: - task: name: Token Classification type: token-classification dataset: name: wild_receipt type: wild_receipt config: WildReceipt split: train args: WildReceipt metrics: - name: Precision type: precision value: 0.8765398302764851 - name: Recall type: recall value: 0.8812439796339617 - name: F1 type: f1 value: 0.8788856103753516 - name: Accuracy type: accuracy value: 0.92678512668641 ---  # OCR-LayoutLMv3-Invoice This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the wild_receipt dataset. It achieves the following results on the evaluation set: - Loss: 0.3159 - Precision: 0.8765 - Recall: 0.8812 - F1: 0.8789 - Accuracy: 0.9268 ## Model description More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 6000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.16 | 100 | 1.5032 | 0.4934 | 0.1444 | 0.2234 | 0.6064 | | No log | 0.32 | 200 | 1.0282 | 0.5884 | 0.4420 | 0.5048 | 0.7385 | | No log | 0.47 | 300 | 0.7856 | 0.7448 | 0.6205 | 0.6770 | 0.8133 | | No log | 0.63 | 400 | 0.6464 | 0.7736 | 0.6689 | 0.7174 | 0.8399 | | 1.1733 | 0.79 | 500 | 0.5672 | 0.7609 | 0.7303 | 0.7453 | 0.8557 | | 1.1733 | 0.95 | 600 | 0.5055 | 0.7658 | 0.7652 | 0.7655 | 0.8677 | | 1.1733 | 1.1 | 700 | 0.4735 | 0.7946 | 0.7848 | 0.7897 | 0.8784 | | 1.1733 | 1.26 | 800 | 0.4414 | 0.7962 | 0.7946 | 0.7954 | 0.8818 | | 1.1733 | 1.42 | 900 | 0.4094 | 0.8176 | 0.8064 | 0.8120 | 0.8894 | | 0.5047 | 1.58 | 1000 | 0.3971 | 0.8219 | 0.8248 | 0.8234 | 0.8961 | | 0.5047 | 1.74 | 1100 | 0.4082 | 0.7993 | 0.8362 | 0.8174 | 0.8927 | | 0.5047 | 1.89 | 1200 | 0.3797 | 0.8240 | 0.8317 | 0.8278 | 0.8962 | | 0.5047 | 2.05 | 1300 | 0.3597 | 0.8326 | 0.8331 | 0.8329 | 0.9020 | | 0.5047 | 2.21 | 1400 | 0.3544 | 0.8462 | 0.8283 | 0.8371 | 0.9020 | | 0.368 | 2.37 | 1500 | 0.3374 | 0.8428 | 0.8435 | 0.8432 | 0.9056 | | 0.368 | 2.52 | 1600 | 0.3364 | 0.8406 | 0.8522 | 0.8464 | 0.9089 | | 0.368 | 2.68 | 1700 | 0.3404 | 0.8467 | 0.8536 | 0.8501 | 0.9107 | | 0.368 | 2.84 | 1800 | 0.3319 | 0.8405 | 0.8501 | 0.8453 | 0.9090 | | 0.368 | 3.0 | 1900 | 0.3324 | 0.8584 | 0.8492 | 0.8538 | 0.9117 | | 0.2949 | 3.15 | 2000 | 0.3204 | 0.8691 | 0.8404 | 0.8545 | 0.9119 | | 0.2949 | 3.31 | 2100 | 0.3107 | 0.8599 | 0.8547 | 0.8573 | 0.9162 | | 0.2949 | 3.47 | 2200 | 0.3169 | 0.8680 | 0.8489 | 0.8584 | 0.9146 | | 0.2949 | 3.63 | 2300 | 0.3190 | 0.8683 | 0.8519 | 0.8600 | 0.9152 | | 0.2949 | 3.79 | 2400 | 0.2975 | 0.8631 | 0.8617 | 0.8624 | 0.9182 | | 0.2438 | 3.94 | 2500 | 0.3040 | 0.8566 | 0.8640 | 0.8603 | 0.9171 | | 0.2438 | 4.1 | 2600 | 0.3045 | 0.8585 | 0.8642 | 0.8613 | 0.9181 | | 0.2438 | 4.26 | 2700 | 0.3139 | 0.8498 | 0.8748 | 0.8621 | 0.9160 | | 0.2438 | 4.42 | 2800 | 0.2985 | 0.8642 | 0.8672 | 0.8657 | 0.9214 | | 0.2438 | 4.57 | 2900 | 0.3047 | 0.8688 | 0.8694 | 0.8691 | 0.9214 | | 0.2028 | 4.73 | 3000 | 0.2986 | 0.8686 | 0.8695 | 0.8691 | 0.9207 | | 0.2028 | 4.89 | 3100 | 0.3135 | 0.8628 | 0.8755 | 0.8691 | 0.9197 | | 0.2028 | 5.05 | 3200 | 0.2927 | 0.8656 | 0.8755 | 0.8705 | 0.9217 | | 0.2028 | 5.21 | 3300 | 0.2992 | 0.8724 | 0.8697 | 0.8711 | 0.9228 | | 0.2028 | 5.36 | 3400 | 0.2975 | 0.8831 | 0.8639 | 0.8734 | 0.9244 | | 0.1814 | 5.52 | 3500 | 0.2897 | 0.8736 | 0.8788 | 0.8762 | 0.9250 | | 0.1814 | 5.68 | 3600 | 0.3118 | 0.8674 | 0.8751 | 0.8712 | 0.9216 | | 0.1814 | 5.84 | 3700 | 0.2974 | 0.8735 | 0.8779 | 0.8757 | 0.9237 | | 0.1814 | 5.99 | 3800 | 0.2957 | 0.8696 | 0.8815 | 0.8755 | 0.9240 | | 0.1814 | 6.15 | 3900 | 0.3120 | 0.8698 | 0.8817 | 0.8757 | 0.9250 | | 0.1602 | 6.31 | 4000 | 0.3080 | 0.8715 | 0.8800 | 0.8757 | 0.9238 | | 0.1602 | 6.47 | 4100 | 0.3031 | 0.8767 | 0.8788 | 0.8777 | 0.9261 | | 0.1602 | 6.62 | 4200 | 0.3146 | 0.8699 | 0.8784 | 0.8741 | 0.9227 | | 0.1602 | 6.78 | 4300 | 0.3085 | 0.8717 | 0.8788 | 0.8752 | 0.9248 | | 0.1602 | 6.94 | 4400 | 0.3023 | 0.8749 | 0.8756 | 0.8752 | 0.9250 | | 0.1383 | 7.1 | 4500 | 0.3025 | 0.8860 | 0.8735 | 0.8797 | 0.9252 | | 0.1383 | 7.26 | 4600 | 0.3026 | 0.8775 | 0.8810 | 0.8792 | 0.9272 | | 0.1383 | 7.41 | 4700 | 0.3146 | 0.8715 | 0.8832 | 0.8773 | 0.9251 | | 0.1383 | 7.57 | 4800 | 0.3113 | 0.8769 | 0.8803 | 0.8786 | 0.9275 | | 0.1383 | 7.73 | 4900 | 0.3073 | 0.8797 | 0.8786 | 0.8792 | 0.9261 | | 0.1306 | 7.89 | 5000 | 0.3163 | 0.8714 | 0.8828 | 0.8770 | 0.9248 | | 0.1306 | 8.04 | 5100 | 0.3163 | 0.8753 | 0.8810 | 0.8781 | 0.9250 | | 0.1306 | 8.2 | 5200 | 0.3132 | 0.8743 | 0.8804 | 0.8773 | 0.9257 | | 0.1306 | 8.36 | 5300 | 0.3119 | 0.8735 | 0.8837 | 0.8786 | 0.9264 | | 0.1306 | 8.52 | 5400 | 0.3145 | 0.8826 | 0.8779 | 0.8802 | 0.9272 | | 0.1174 | 8.68 | 5500 | 0.3166 | 0.8776 | 0.8811 | 0.8794 | 0.9261 | | 0.1174 | 8.83 | 5600 | 0.3146 | 0.8776 | 0.8814 | 0.8795 | 0.9260 | | 0.1174 | 8.99 | 5700 | 0.3135 | 0.8763 | 0.8826 | 0.8795 | 0.9271 | | 0.1174 | 9.15 | 5800 | 0.3154 | 0.8794 | 0.8818 | 0.8806 | 0.9275 | | 0.1174 | 9.31 | 5900 | 0.3152 | 0.8788 | 0.8817 | 0.8802 | 0.9274 | | 0.11 | 9.46 | 6000 | 0.3159 | 0.8765 | 0.8812 | 0.8789 | 0.9268 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

huggingtweets/thebuddha_3

huggingtweets

2022-11-07T00:16:25Z

107

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-07T00:16:16Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1421008625095647234/Vfg52xtV_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Buddha</div> <div style="text-align: center; font-size: 14px;">@thebuddha_3</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Buddha. | Data | Buddha | | --- | --- | | Tweets downloaded | 3200 | | Retweets | 138 | | Short tweets | 695 | | Tweets kept | 2367 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/14lqj1g8/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @thebuddha_3's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3rpocant) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3rpocant/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/thebuddha_3') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

huggingtweets/gleampt2-h3xenbrenner2-kidddozer

huggingtweets

2022-11-06T23:40:24Z

97

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-06T23:39:09Z

--- language: en thumbnail: http://www.huggingtweets.com/gleampt2-h3xenbrenner2-kidddozer/1667778020169/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509747695795118080/Vz0be-8x_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1380052646178996227/fmYX0h3D_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">h b & Pepper Boy & gleam</div> <div style="text-align: center; font-size: 14px;">@gleampt2-h3xenbrenner2-kidddozer</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from h b & Pepper Boy & gleam. | Data | h b | Pepper Boy | gleam | | --- | --- | --- | --- | | Tweets downloaded | 1231 | 2848 | 2305 | | Retweets | 75 | 690 | 196 | | Short tweets | 155 | 442 | 170 | | Tweets kept | 1001 | 1716 | 1939 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/336sqi28/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gleampt2-h3xenbrenner2-kidddozer's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/hhg4q0io/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/gleampt2-h3xenbrenner2-kidddozer') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

kaejo98/bart-base_question_generation

kaejo98

2022-11-06T23:27:56Z

8

4

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-01T22:36:20Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base_question_generation results: [] --- # BART-base Question Generation This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>. ## Model description The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions: - <b> Casual-Generation </b>: where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/> Example: <br/> \<generate_questions\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity. - <b> Context-based-Generation </b>: given a section of a passage (context), the model is tasked to generate questions from the passage about the selected section or context. The input should be follow the structure or format: \<generate_context_questions\> \<section\> put your context here \</section\> paragraph: put your passage text here'. <br/> Example: <br/> \<generate_context_questions\> \<section\> Mohorovičić discontinuity \</section\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity. The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method. ## limitations The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data. ## Training and evaluation data The dataset used to train the model comprises the training datasets from: - Reasoning Over Paragraph Effects in Situations (ROPES): https://allenai.org/data/ropes - SQUAD: - DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop - SciQ After preprocessing the data from the above listed datasets, we had 408372 examples for training the model and 25k for development and 18k for testing. ## Training procedure The model is trained (finetuned) for 5 epochs with the hyperparameters listed below: ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.25 - num_epochs: 5 At the end of 5 epochs, the Evaluation loss was: 1.64 and the training loss was: 0.9671. ### Framework versions - Transformers 4.23.1 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

ntsema/wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab

ntsema

2022-11-06T22:55:43Z

126

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-06T05:30:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Wer type: wer value: 1.0547550432276658 ---  # wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.0548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5109 | 8.33 | 400 | inf | 1.0807 | | 0.4252 | 16.66 | 800 | inf | 1.0519 | | 0.1744 | 24.99 | 1200 | inf | 1.0548 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.14.0.dev20221105+cu116 - Datasets 2.6.1 - Tokenizers 0.13.1

halflings/house_price_prediction_ser2

halflings

2022-11-06T21:40:14Z

0

mlconsole

[ "mlconsole", "tabular-regression", "dataset:house_price_prediction", "license:unknown", "model-index", "region:us" ]

tabular-regression

2022-11-06T21:40:10Z

--- license: unknown inference: false tags: - mlconsole - tabular-regression library_name: mlconsole metrics: - mae - loss datasets: - house_price_prediction model-index: - name: house_price_prediction_ser2 results: - task: type: tabular-regression name: tabular-regression dataset: type: house_price_prediction name: house_price_prediction metrics: - type: mae name: Mean absolute error value: 5.011783599853516 - type: loss name: Model loss value: 43.01755905151367 --- # regression model trained on "house_price_prediction" 🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_ser2) in one click. 🧑‍💻 [Train your own model](https://mlconsole.com) on ML Console.

halflings/house_price_prediction_dev

halflings

2022-11-06T21:34:02Z

0

mlconsole

[ "mlconsole", "tabular-regression", "dataset:house_price_prediction", "license:unknown", "model-index", "region:us" ]

tabular-regression

2022-11-06T21:33:58Z

--- license: unknown inference: false tags: - mlconsole - tabular-regression library_name: mlconsole metrics: - mae - loss datasets: - house_price_prediction model-index: - name: house_price_prediction_dev results: - task: type: tabular-regression name: tabular-regression dataset: type: house_price_prediction name: house_price_prediction metrics: - type: mae name: Mean absolute error value: 7.064809322357178 - type: loss name: Model loss value: 98.9962387084961 --- # regression model trained on "house_price_prediction" 🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_dev) in one click. 🧑‍💻 [Train your own model](https://mlconsole.com) on ML Console.

lewtun/distilhubert-finetuned-gtzan

lewtun

2022-11-06T21:05:59Z

31

0

transformers

[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "hf-course", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2022-03-14T17:10:18Z

--- license: apache-2.0 tags: - hf-course - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: [] ---  # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6694 - Accuracy: 0.82 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.99 | 56 | 1.9426 | 0.5 | | No log | 1.99 | 112 | 1.4157 | 0.63 | | No log | 2.99 | 168 | 1.1351 | 0.69 | | No log | 3.99 | 224 | 1.0285 | 0.72 | | No log | 4.99 | 280 | 0.8538 | 0.79 | | No log | 5.99 | 336 | 0.8015 | 0.74 | | No log | 6.99 | 392 | 0.6694 | 0.82 | | No log | 7.99 | 448 | 0.6779 | 0.79 | | 1.0811 | 8.99 | 504 | 0.6414 | 0.81 | | 1.0811 | 9.99 | 560 | 0.6443 | 0.82 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0 - Datasets 2.6.1 - Tokenizers 0.11.6

pig4431/amazonPolarity_DistilBERT_5E

pig4431

2022-11-06T20:58:38Z

107

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_polarity", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-06T20:54:32Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_polarity metrics: - accuracy model-index: - name: amazonPolarity_DistilBERT_5EE results: - task: name: Text Classification type: text-classification dataset: name: amazon_polarity type: amazon_polarity config: amazon_polarity split: train args: amazon_polarity metrics: - name: Accuracy type: accuracy value: 0.94 ---  # amazonPolarity_DistilBERT_5EE This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6581 | 0.03 | 50 | 0.5315 | 0.84 | | 0.4321 | 0.05 | 100 | 0.2897 | 0.8933 | | 0.298 | 0.08 | 150 | 0.3165 | 0.8667 | | 0.2902 | 0.11 | 200 | 0.2552 | 0.9067 | | 0.2824 | 0.13 | 250 | 0.2277 | 0.9133 | | 0.2522 | 0.16 | 300 | 0.1998 | 0.94 | | 0.2781 | 0.19 | 350 | 0.1933 | 0.94 | | 0.2668 | 0.21 | 400 | 0.2316 | 0.92 | | 0.2619 | 0.24 | 450 | 0.1968 | 0.9333 | | 0.2446 | 0.27 | 500 | 0.1846 | 0.9467 | | 0.2677 | 0.29 | 550 | 0.1818 | 0.94 | | 0.2026 | 0.32 | 600 | 0.2348 | 0.9133 | | 0.2351 | 0.35 | 650 | 0.2127 | 0.92 | | 0.2685 | 0.37 | 700 | 0.1792 | 0.94 | | 0.2141 | 0.4 | 750 | 0.2252 | 0.9133 | | 0.2193 | 0.43 | 800 | 0.2131 | 0.9267 | | 0.2456 | 0.45 | 850 | 0.2205 | 0.9133 | | 0.2548 | 0.48 | 900 | 0.1788 | 0.94 | | 0.2353 | 0.51 | 950 | 0.1954 | 0.9267 | | 0.2546 | 0.53 | 1000 | 0.1815 | 0.9333 | | 0.2583 | 0.56 | 1050 | 0.1654 | 0.9333 | | 0.219 | 0.59 | 1100 | 0.1760 | 0.9467 | | 0.2241 | 0.61 | 1150 | 0.2107 | 0.92 | | 0.2201 | 0.64 | 1200 | 0.2381 | 0.8933 | | 0.1745 | 0.67 | 1250 | 0.1944 | 0.92 | | 0.2698 | 0.69 | 1300 | 0.1971 | 0.9267 | | 0.214 | 0.72 | 1350 | 0.1944 | 0.9333 | | 0.2436 | 0.75 | 1400 | 0.2079 | 0.92 | | 0.2318 | 0.77 | 1450 | 0.2088 | 0.9333 | | 0.2206 | 0.8 | 1500 | 0.1875 | 0.94 | | 0.2593 | 0.83 | 1550 | 0.1797 | 0.9267 | | 0.1908 | 0.85 | 1600 | 0.1924 | 0.9333 | | 0.2378 | 0.88 | 1650 | 0.1649 | 0.9267 | | 0.2332 | 0.91 | 1700 | 0.1768 | 0.94 | | 0.2125 | 0.93 | 1750 | 0.2276 | 0.92 | | 0.2174 | 0.96 | 1800 | 0.2035 | 0.9333 | | 0.19 | 0.99 | 1850 | 0.1805 | 0.94 | | 0.1515 | 1.01 | 1900 | 0.1832 | 0.94 | | 0.1671 | 1.04 | 1950 | 0.1902 | 0.94 | | 0.171 | 1.07 | 2000 | 0.2468 | 0.9267 | | 0.1495 | 1.09 | 2050 | 0.2276 | 0.9267 | | 0.1535 | 1.12 | 2100 | 0.1926 | 0.94 | | 0.2085 | 1.15 | 2150 | 0.1878 | 0.94 | | 0.1395 | 1.17 | 2200 | 0.1795 | 0.9467 | | 0.1556 | 1.2 | 2250 | 0.1554 | 0.9467 | | 0.1273 | 1.23 | 2300 | 0.1707 | 0.94 | | 0.1873 | 1.25 | 2350 | 0.1867 | 0.9467 | | 0.1589 | 1.28 | 2400 | 0.2089 | 0.9333 | | 0.1426 | 1.31 | 2450 | 0.1797 | 0.9467 | | 0.149 | 1.33 | 2500 | 0.1991 | 0.9333 | | 0.1535 | 1.36 | 2550 | 0.2116 | 0.94 | | 0.1671 | 1.39 | 2600 | 0.1704 | 0.9467 | | 0.1582 | 1.41 | 2650 | 0.1843 | 0.94 | | 0.1393 | 1.44 | 2700 | 0.1831 | 0.94 | | 0.1474 | 1.47 | 2750 | 0.1895 | 0.94 | | 0.203 | 1.49 | 2800 | 0.1843 | 0.9467 | | 0.1562 | 1.52 | 2850 | 0.2060 | 0.9467 | | 0.1886 | 1.55 | 2900 | 0.1837 | 0.94 | | 0.1332 | 1.57 | 2950 | 0.1920 | 0.9467 | | 0.1519 | 1.6 | 3000 | 0.1789 | 0.9533 | | 0.1354 | 1.63 | 3050 | 0.1974 | 0.9467 | | 0.125 | 1.65 | 3100 | 0.1890 | 0.9533 | | 0.2044 | 1.68 | 3150 | 0.1755 | 0.9533 | | 0.1746 | 1.71 | 3200 | 0.1607 | 0.9467 | | 0.1981 | 1.73 | 3250 | 0.1613 | 0.9533 | | 0.1276 | 1.76 | 3300 | 0.1825 | 0.96 | | 0.1935 | 1.79 | 3350 | 0.1707 | 0.9533 | | 0.1848 | 1.81 | 3400 | 0.1697 | 0.96 | | 0.1596 | 1.84 | 3450 | 0.1581 | 0.9667 | | 0.1797 | 1.87 | 3500 | 0.1634 | 0.96 | | 0.1493 | 1.89 | 3550 | 0.1614 | 0.9533 | | 0.1703 | 1.92 | 3600 | 0.1673 | 0.9467 | | 0.1951 | 1.95 | 3650 | 0.1589 | 0.9533 | | 0.1582 | 1.97 | 3700 | 0.1761 | 0.9467 | | 0.1974 | 2.0 | 3750 | 0.1918 | 0.94 | | 0.1056 | 2.03 | 3800 | 0.2063 | 0.94 | | 0.1109 | 2.05 | 3850 | 0.2031 | 0.9467 | | 0.113 | 2.08 | 3900 | 0.2118 | 0.9467 | | 0.0834 | 2.11 | 3950 | 0.1974 | 0.9533 | | 0.1434 | 2.13 | 4000 | 0.2075 | 0.9533 | | 0.0691 | 2.16 | 4050 | 0.2178 | 0.9533 | | 0.1144 | 2.19 | 4100 | 0.2383 | 0.9467 | | 0.1446 | 2.21 | 4150 | 0.2207 | 0.9533 | | 0.172 | 2.24 | 4200 | 0.2034 | 0.9467 | | 0.1026 | 2.27 | 4250 | 0.2048 | 0.9467 | | 0.1131 | 2.29 | 4300 | 0.2334 | 0.9467 | | 0.121 | 2.32 | 4350 | 0.2367 | 0.9333 | | 0.1144 | 2.35 | 4400 | 0.2313 | 0.9467 | | 0.1089 | 2.37 | 4450 | 0.2352 | 0.9533 | | 0.1193 | 2.4 | 4500 | 0.2440 | 0.94 | | 0.0689 | 2.43 | 4550 | 0.2379 | 0.9333 | | 0.1799 | 2.45 | 4600 | 0.2354 | 0.9467 | | 0.1068 | 2.48 | 4650 | 0.2158 | 0.9533 | | 0.0974 | 2.51 | 4700 | 0.2456 | 0.94 | | 0.0637 | 2.53 | 4750 | 0.2191 | 0.9333 | | 0.1125 | 2.56 | 4800 | 0.2390 | 0.9467 | | 0.1706 | 2.59 | 4850 | 0.2407 | 0.94 | | 0.1533 | 2.61 | 4900 | 0.2242 | 0.9533 | | 0.1357 | 2.64 | 4950 | 0.2119 | 0.9533 | | 0.1342 | 2.67 | 5000 | 0.2268 | 0.9467 | | 0.0796 | 2.69 | 5050 | 0.2450 | 0.9467 | | 0.1351 | 2.72 | 5100 | 0.2499 | 0.94 | | 0.1285 | 2.75 | 5150 | 0.2252 | 0.94 | | 0.1563 | 2.77 | 5200 | 0.2191 | 0.94 | | 0.1022 | 2.8 | 5250 | 0.2256 | 0.9533 | | 0.11 | 2.83 | 5300 | 0.2365 | 0.9467 | | 0.0926 | 2.85 | 5350 | 0.2206 | 0.9467 | | 0.1043 | 2.88 | 5400 | 0.2018 | 0.9533 | | 0.1041 | 2.91 | 5450 | 0.2268 | 0.9467 | | 0.1232 | 2.93 | 5500 | 0.2164 | 0.9467 | | 0.1537 | 2.96 | 5550 | 0.1956 | 0.9533 | | 0.1188 | 2.99 | 5600 | 0.2126 | 0.9467 | | 0.0749 | 3.01 | 5650 | 0.2249 | 0.9467 | | 0.062 | 3.04 | 5700 | 0.2254 | 0.9467 | | 0.0755 | 3.07 | 5750 | 0.2472 | 0.94 | | 0.0866 | 3.09 | 5800 | 0.2569 | 0.94 | | 0.0502 | 3.12 | 5850 | 0.2481 | 0.9467 | | 0.1158 | 3.15 | 5900 | 0.2457 | 0.94 | | 0.0413 | 3.17 | 5950 | 0.2500 | 0.94 | | 0.0966 | 3.2 | 6000 | 0.2851 | 0.9333 | | 0.0613 | 3.23 | 6050 | 0.2717 | 0.9467 | | 0.1029 | 3.25 | 6100 | 0.2714 | 0.94 | | 0.0833 | 3.28 | 6150 | 0.2683 | 0.94 | | 0.0928 | 3.31 | 6200 | 0.2490 | 0.9467 | | 0.0571 | 3.33 | 6250 | 0.2575 | 0.9533 | | 0.1252 | 3.36 | 6300 | 0.2599 | 0.9467 | | 0.0788 | 3.39 | 6350 | 0.2522 | 0.9467 | | 0.0862 | 3.41 | 6400 | 0.2489 | 0.9533 | | 0.112 | 3.44 | 6450 | 0.2452 | 0.9533 | | 0.0868 | 3.47 | 6500 | 0.2438 | 0.9533 | | 0.0979 | 3.49 | 6550 | 0.2474 | 0.94 | | 0.0739 | 3.52 | 6600 | 0.2508 | 0.94 | | 0.0786 | 3.55 | 6650 | 0.2621 | 0.94 | | 0.0872 | 3.57 | 6700 | 0.2543 | 0.9333 | | 0.0962 | 3.6 | 6750 | 0.2347 | 0.9467 | | 0.124 | 3.63 | 6800 | 0.2319 | 0.9533 | | 0.0747 | 3.65 | 6850 | 0.2448 | 0.9533 | | 0.0591 | 3.68 | 6900 | 0.2379 | 0.94 | | 0.1049 | 3.71 | 6950 | 0.2493 | 0.9333 | | 0.0772 | 3.73 | 7000 | 0.2429 | 0.94 | | 0.071 | 3.76 | 7050 | 0.2558 | 0.94 | | 0.1116 | 3.79 | 7100 | 0.2600 | 0.94 | | 0.1199 | 3.81 | 7150 | 0.2480 | 0.94 | | 0.0819 | 3.84 | 7200 | 0.2506 | 0.94 | | 0.1054 | 3.87 | 7250 | 0.2431 | 0.94 | | 0.09 | 3.89 | 7300 | 0.2582 | 0.9333 | | 0.0936 | 3.92 | 7350 | 0.2460 | 0.94 | | 0.0469 | 3.95 | 7400 | 0.2509 | 0.94 | | 0.1101 | 3.97 | 7450 | 0.2545 | 0.9467 | | 0.1077 | 4.0 | 7500 | 0.2640 | 0.9467 | | 0.0777 | 4.03 | 7550 | 0.2709 | 0.94 | | 0.0777 | 4.05 | 7600 | 0.2842 | 0.94 | | 0.0847 | 4.08 | 7650 | 0.2649 | 0.94 | | 0.0462 | 4.11 | 7700 | 0.2702 | 0.9467 | | 0.0572 | 4.13 | 7750 | 0.2628 | 0.94 | | 0.0435 | 4.16 | 7800 | 0.2689 | 0.9467 | | 0.0566 | 4.19 | 7850 | 0.2727 | 0.9467 | | 0.1149 | 4.21 | 7900 | 0.2635 | 0.9467 | | 0.0557 | 4.24 | 7950 | 0.2665 | 0.9467 | | 0.061 | 4.27 | 8000 | 0.2680 | 0.9467 | | 0.0664 | 4.29 | 8050 | 0.2767 | 0.9467 | | 0.0481 | 4.32 | 8100 | 0.2662 | 0.9467 | | 0.0893 | 4.35 | 8150 | 0.2677 | 0.9467 | | 0.0855 | 4.37 | 8200 | 0.2733 | 0.9467 | | 0.0552 | 4.4 | 8250 | 0.2589 | 0.94 | | 0.0469 | 4.43 | 8300 | 0.2733 | 0.94 | | 0.0633 | 4.45 | 8350 | 0.2799 | 0.94 | | 0.0629 | 4.48 | 8400 | 0.2838 | 0.94 | | 0.0854 | 4.51 | 8450 | 0.2837 | 0.94 | | 0.0596 | 4.53 | 8500 | 0.2808 | 0.94 | | 0.0579 | 4.56 | 8550 | 0.2839 | 0.94 | | 0.0508 | 4.59 | 8600 | 0.2844 | 0.94 | | 0.0557 | 4.61 | 8650 | 0.2833 | 0.94 | | 0.0383 | 4.64 | 8700 | 0.2878 | 0.94 | | 0.0554 | 4.67 | 8750 | 0.2924 | 0.94 | | 0.0681 | 4.69 | 8800 | 0.2868 | 0.94 | | 0.065 | 4.72 | 8850 | 0.2888 | 0.94 | | 0.0731 | 4.75 | 8900 | 0.2946 | 0.94 | | 0.0638 | 4.77 | 8950 | 0.2886 | 0.94 | | 0.043 | 4.8 | 9000 | 0.2867 | 0.94 | | 0.0658 | 4.83 | 9050 | 0.2872 | 0.94 | | 0.0249 | 4.85 | 9100 | 0.2882 | 0.94 | | 0.0612 | 4.88 | 9150 | 0.2902 | 0.94 | | 0.0271 | 4.91 | 9200 | 0.2890 | 0.94 | | 0.0308 | 4.93 | 9250 | 0.2897 | 0.94 | | 0.0896 | 4.96 | 9300 | 0.2898 | 0.94 | | 0.1172 | 4.99 | 9350 | 0.2899 | 0.94 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

thaonguyen274/vit-base-patch16-224-finetuned-imageclassification

thaonguyen274

2022-11-06T20:55:55Z

28

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-06T16:57:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-imageclassification results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9501779359430605 ---  # vit-base-patch16-224-finetuned-imageclassification This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1790 - Accuracy: 0.9502 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 9 | 0.5791 | 0.9004 | | 1.4122 | 2.0 | 18 | 0.2002 | 0.9359 | | 0.3147 | 3.0 | 27 | 0.1717 | 0.9502 | | 0.1907 | 4.0 | 36 | 0.1632 | 0.9466 | | 0.158 | 5.0 | 45 | 0.1822 | 0.9466 | | 0.1169 | 6.0 | 54 | 0.1778 | 0.9502 | | 0.0984 | 7.0 | 63 | 0.1552 | 0.9573 | | 0.0971 | 8.0 | 72 | 0.1835 | 0.9502 | | 0.0965 | 9.0 | 81 | 0.1878 | 0.9484 | | 0.0766 | 10.0 | 90 | 0.1790 | 0.9502 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1