librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-27 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-27 18:22:57	card stringlengths 11 1.01M
jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7	jordyvl	2023-07-11T01:00:28Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T23:46:12Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 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. --> # vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2544 - Accuracy: 0.6375 - Brier Loss: 0.4805 - Nll: 3.0517 - F1 Micro: 0.6375 - F1 Macro: 0.6394 - Ece: 0.1654 - Aurc: 0.1376 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 3.2176 \| 0.1275 \| 0.9297 \| 15.5568 \| 0.1275 \| 0.1255 \| 0.1544 \| 0.8595 \| \| No log \| 2.0 \| 50 \| 2.4392 \| 0.405 \| 0.7503 \| 9.6083 \| 0.405 \| 0.3723 \| 0.1816 \| 0.3640 \| \| No log \| 3.0 \| 75 \| 1.9211 \| 0.5025 \| 0.6287 \| 5.6023 \| 0.5025 \| 0.4930 \| 0.1991 \| 0.2451 \| \| No log \| 4.0 \| 100 \| 1.7474 \| 0.5375 \| 0.5956 \| 4.5712 \| 0.5375 \| 0.5387 \| 0.1677 \| 0.2244 \| \| No log \| 5.0 \| 125 \| 1.7107 \| 0.535 \| 0.6051 \| 4.3431 \| 0.535 \| 0.5180 \| 0.1796 \| 0.2269 \| \| No log \| 6.0 \| 150 \| 1.7144 \| 0.545 \| 0.5988 \| 3.6699 \| 0.545 \| 0.5455 \| 0.1918 \| 0.2253 \| \| No log \| 7.0 \| 175 \| 1.9096 \| 0.5625 \| 0.6262 \| 4.6856 \| 0.5625 \| 0.5459 \| 0.1966 \| 0.2362 \| \| No log \| 8.0 \| 200 \| 1.6325 \| 0.575 \| 0.5815 \| 3.9279 \| 0.575 \| 0.5705 \| 0.1893 \| 0.2026 \| \| No log \| 9.0 \| 225 \| 1.8268 \| 0.56 \| 0.6088 \| 4.5140 \| 0.56 \| 0.5482 \| 0.1976 \| 0.2213 \| \| No log \| 10.0 \| 250 \| 1.9253 \| 0.5575 \| 0.6493 \| 4.2860 \| 0.5575 \| 0.5427 \| 0.2286 \| 0.2445 \| \| No log \| 11.0 \| 275 \| 1.6941 \| 0.5725 \| 0.5940 \| 3.9317 \| 0.5725 \| 0.5827 \| 0.2019 \| 0.2232 \| \| No log \| 12.0 \| 300 \| 1.8197 \| 0.5575 \| 0.6138 \| 4.7928 \| 0.5575 \| 0.5476 \| 0.2079 \| 0.2240 \| \| No log \| 13.0 \| 325 \| 1.8958 \| 0.54 \| 0.6508 \| 4.2978 \| 0.54 \| 0.5338 \| 0.2379 \| 0.2357 \| \| No log \| 14.0 \| 350 \| 1.8939 \| 0.535 \| 0.6522 \| 4.5557 \| 0.535 \| 0.5143 \| 0.2324 \| 0.2350 \| \| No log \| 15.0 \| 375 \| 1.8018 \| 0.585 \| 0.6042 \| 4.4728 \| 0.585 \| 0.5829 \| 0.2205 \| 0.2182 \| \| No log \| 16.0 \| 400 \| 1.7645 \| 0.5975 \| 0.5978 \| 3.9939 \| 0.5975 \| 0.5992 \| 0.2130 \| 0.1927 \| \| No log \| 17.0 \| 425 \| 1.6392 \| 0.5925 \| 0.5842 \| 3.6783 \| 0.5925 \| 0.6039 \| 0.1986 \| 0.2017 \| \| No log \| 18.0 \| 450 \| 1.6124 \| 0.5875 \| 0.5761 \| 4.0535 \| 0.5875 \| 0.5721 \| 0.2060 \| 0.1792 \| \| No log \| 19.0 \| 475 \| 1.7517 \| 0.585 \| 0.6102 \| 3.9076 \| 0.585 \| 0.5786 \| 0.2082 \| 0.2071 \| \| 0.6436 \| 20.0 \| 500 \| 1.7467 \| 0.5575 \| 0.6166 \| 3.5052 \| 0.5575 \| 0.5476 \| 0.2252 \| 0.2247 \| \| 0.6436 \| 21.0 \| 525 \| 1.6719 \| 0.5825 \| 0.5745 \| 4.1235 \| 0.5825 \| 0.5877 \| 0.1831 \| 0.1723 \| \| 0.6436 \| 22.0 \| 550 \| 1.4222 \| 0.605 \| 0.5237 \| 3.2051 \| 0.605 \| 0.6083 \| 0.1813 \| 0.1559 \| \| 0.6436 \| 23.0 \| 575 \| 1.6436 \| 0.595 \| 0.5701 \| 4.3949 \| 0.595 \| 0.5834 \| 0.1921 \| 0.1901 \| \| 0.6436 \| 24.0 \| 600 \| 1.4244 \| 0.6075 \| 0.5197 \| 3.3207 \| 0.6075 \| 0.6100 \| 0.1548 \| 0.1616 \| \| 0.6436 \| 25.0 \| 625 \| 1.4567 \| 0.6075 \| 0.5356 \| 3.5288 \| 0.6075 \| 0.6107 \| 0.1768 \| 0.1652 \| \| 0.6436 \| 26.0 \| 650 \| 1.5889 \| 0.595 \| 0.5587 \| 4.1521 \| 0.595 \| 0.5907 \| 0.1943 \| 0.1768 \| \| 0.6436 \| 27.0 \| 675 \| 1.4828 \| 0.5725 \| 0.5532 \| 3.4259 \| 0.5725 \| 0.5720 \| 0.2125 \| 0.1803 \| \| 0.6436 \| 28.0 \| 700 \| 1.4671 \| 0.5975 \| 0.5509 \| 3.2612 \| 0.5975 \| 0.6006 \| 0.1983 \| 0.1797 \| \| 0.6436 \| 29.0 \| 725 \| 1.4049 \| 0.6225 \| 0.5273 \| 3.3136 \| 0.6225 \| 0.6237 \| 0.1995 \| 0.1600 \| \| 0.6436 \| 30.0 \| 750 \| 1.4039 \| 0.6175 \| 0.5208 \| 3.2588 \| 0.6175 \| 0.6063 \| 0.1770 \| 0.1534 \| \| 0.6436 \| 31.0 \| 775 \| 1.4333 \| 0.6 \| 0.5378 \| 3.6417 \| 0.6 \| 0.5995 \| 0.1899 \| 0.1632 \| \| 0.6436 \| 32.0 \| 800 \| 1.3311 \| 0.64 \| 0.5032 \| 3.0056 \| 0.64 \| 0.6394 \| 0.1699 \| 0.1476 \| \| 0.6436 \| 33.0 \| 825 \| 1.3361 \| 0.61 \| 0.5079 \| 3.2304 \| 0.61 \| 0.6123 \| 0.1536 \| 0.1517 \| \| 0.6436 \| 34.0 \| 850 \| 1.2984 \| 0.64 \| 0.4982 \| 3.1446 \| 0.64 \| 0.6444 \| 0.1636 \| 0.1424 \| \| 0.6436 \| 35.0 \| 875 \| 1.3153 \| 0.6275 \| 0.4995 \| 3.0722 \| 0.6275 \| 0.6288 \| 0.1634 \| 0.1486 \| \| 0.6436 \| 36.0 \| 900 \| 1.2773 \| 0.6375 \| 0.4880 \| 2.7136 \| 0.6375 \| 0.6422 \| 0.1606 \| 0.1411 \| \| 0.6436 \| 37.0 \| 925 \| 1.2881 \| 0.64 \| 0.4946 \| 3.0452 \| 0.64 \| 0.6437 \| 0.1732 \| 0.1440 \| \| 0.6436 \| 38.0 \| 950 \| 1.2609 \| 0.64 \| 0.4824 \| 2.7407 \| 0.64 \| 0.6430 \| 0.1485 \| 0.1424 \| \| 0.6436 \| 39.0 \| 975 \| 1.2685 \| 0.645 \| 0.4869 \| 2.7203 \| 0.645 \| 0.6484 \| 0.1680 \| 0.1398 \| \| 0.0861 \| 40.0 \| 1000 \| 1.2546 \| 0.635 \| 0.4808 \| 2.7042 \| 0.635 \| 0.6356 \| 0.1669 \| 0.1416 \| \| 0.0861 \| 41.0 \| 1025 \| 1.2599 \| 0.6425 \| 0.4858 \| 2.6880 \| 0.6425 \| 0.6457 \| 0.1539 \| 0.1387 \| \| 0.0861 \| 42.0 \| 1050 \| 1.2413 \| 0.635 \| 0.4783 \| 2.8343 \| 0.635 \| 0.6361 \| 0.1679 \| 0.1369 \| \| 0.0861 \| 43.0 \| 1075 \| 1.2670 \| 0.6325 \| 0.4901 \| 2.8366 \| 0.6325 \| 0.6337 \| 0.1501 \| 0.1399 \| \| 0.0861 \| 44.0 \| 1100 \| 1.2793 \| 0.63 \| 0.4919 \| 3.1711 \| 0.63 \| 0.6309 \| 0.1672 \| 0.1465 \| \| 0.0861 \| 45.0 \| 1125 \| 1.2555 \| 0.635 \| 0.4844 \| 2.9284 \| 0.635 \| 0.6379 \| 0.1791 \| 0.1401 \| \| 0.0861 \| 46.0 \| 1150 \| 1.2491 \| 0.635 \| 0.4806 \| 2.8475 \| 0.635 \| 0.6358 \| 0.1611 \| 0.1392 \| \| 0.0861 \| 47.0 \| 1175 \| 1.2533 \| 0.6325 \| 0.4837 \| 2.8229 \| 0.6325 \| 0.6352 \| 0.1623 \| 0.1378 \| \| 0.0861 \| 48.0 \| 1200 \| 1.2602 \| 0.635 \| 0.4857 \| 2.9963 \| 0.635 \| 0.6368 \| 0.1535 \| 0.1426 \| \| 0.0861 \| 49.0 \| 1225 \| 1.2598 \| 0.635 \| 0.4848 \| 2.8569 \| 0.635 \| 0.6370 \| 0.1718 \| 0.1389 \| \| 0.0861 \| 50.0 \| 1250 \| 1.2577 \| 0.6225 \| 0.4839 \| 2.8645 \| 0.6225 \| 0.6237 \| 0.1678 \| 0.1420 \| \| 0.0861 \| 51.0 \| 1275 \| 1.2547 \| 0.63 \| 0.4817 \| 2.8344 \| 0.63 \| 0.6314 \| 0.1721 \| 0.1399 \| \| 0.0861 \| 52.0 \| 1300 \| 1.2525 \| 0.64 \| 0.4819 \| 2.7720 \| 0.64 \| 0.6411 \| 0.1567 \| 0.1378 \| \| 0.0861 \| 53.0 \| 1325 \| 1.2627 \| 0.6325 \| 0.4854 \| 2.9202 \| 0.6325 \| 0.6337 \| 0.1688 \| 0.1406 \| \| 0.0861 \| 54.0 \| 1350 \| 1.2565 \| 0.63 \| 0.4836 \| 2.8392 \| 0.63 \| 0.6320 \| 0.1612 \| 0.1404 \| \| 0.0861 \| 55.0 \| 1375 \| 1.2514 \| 0.6325 \| 0.4813 \| 2.9887 \| 0.6325 \| 0.6343 \| 0.1652 \| 0.1386 \| \| 0.0861 \| 56.0 \| 1400 \| 1.2541 \| 0.6275 \| 0.4822 \| 2.9067 \| 0.6275 \| 0.6296 \| 0.1649 \| 0.1401 \| \| 0.0861 \| 57.0 \| 1425 \| 1.2529 \| 0.64 \| 0.4810 \| 2.9166 \| 0.64 \| 0.6432 \| 0.1765 \| 0.1372 \| \| 0.0861 \| 58.0 \| 1450 \| 1.2464 \| 0.6275 \| 0.4799 \| 2.9713 \| 0.6275 \| 0.6291 \| 0.1653 \| 0.1401 \| \| 0.0861 \| 59.0 \| 1475 \| 1.2576 \| 0.63 \| 0.4826 \| 2.9124 \| 0.63 \| 0.6323 \| 0.1557 \| 0.1397 \| \| 0.0496 \| 60.0 \| 1500 \| 1.2494 \| 0.63 \| 0.4804 \| 2.8355 \| 0.63 \| 0.6317 \| 0.1672 \| 0.1390 \| \| 0.0496 \| 61.0 \| 1525 \| 1.2496 \| 0.6325 \| 0.4803 \| 2.9091 \| 0.6325 \| 0.6352 \| 0.1510 \| 0.1383 \| \| 0.0496 \| 62.0 \| 1550 \| 1.2592 \| 0.6375 \| 0.4838 \| 2.8980 \| 0.6375 \| 0.6384 \| 0.1758 \| 0.1398 \| \| 0.0496 \| 63.0 \| 1575 \| 1.2504 \| 0.63 \| 0.4806 \| 2.9843 \| 0.63 \| 0.6316 \| 0.1691 \| 0.1391 \| \| 0.0496 \| 64.0 \| 1600 \| 1.2528 \| 0.6325 \| 0.4810 \| 2.9045 \| 0.6325 \| 0.6349 \| 0.1737 \| 0.1388 \| \| 0.0496 \| 65.0 \| 1625 \| 1.2589 \| 0.6425 \| 0.4833 \| 2.9817 \| 0.6425 \| 0.6447 \| 0.1719 \| 0.1380 \| \| 0.0496 \| 66.0 \| 1650 \| 1.2531 \| 0.63 \| 0.4811 \| 2.9027 \| 0.63 \| 0.6321 \| 0.1751 \| 0.1391 \| \| 0.0496 \| 67.0 \| 1675 \| 1.2520 \| 0.635 \| 0.4808 \| 2.9794 \| 0.635 \| 0.6379 \| 0.1715 \| 0.1378 \| \| 0.0496 \| 68.0 \| 1700 \| 1.2543 \| 0.64 \| 0.4815 \| 2.9771 \| 0.64 \| 0.6420 \| 0.1562 \| 0.1380 \| \| 0.0496 \| 69.0 \| 1725 \| 1.2538 \| 0.6325 \| 0.4808 \| 2.9080 \| 0.6325 \| 0.6345 \| 0.1681 \| 0.1385 \| \| 0.0496 \| 70.0 \| 1750 \| 1.2543 \| 0.6325 \| 0.4813 \| 2.9102 \| 0.6325 \| 0.6347 \| 0.1725 \| 0.1390 \| \| 0.0496 \| 71.0 \| 1775 \| 1.2534 \| 0.6325 \| 0.4809 \| 2.9778 \| 0.6325 \| 0.6353 \| 0.1495 \| 0.1385 \| \| 0.0496 \| 72.0 \| 1800 \| 1.2539 \| 0.6375 \| 0.4809 \| 2.9024 \| 0.6375 \| 0.6394 \| 0.1588 \| 0.1381 \| \| 0.0496 \| 73.0 \| 1825 \| 1.2531 \| 0.635 \| 0.4806 \| 2.9812 \| 0.635 \| 0.6378 \| 0.1552 \| 0.1380 \| \| 0.0496 \| 74.0 \| 1850 \| 1.2531 \| 0.635 \| 0.4805 \| 2.9783 \| 0.635 \| 0.6377 \| 0.1700 \| 0.1380 \| \| 0.0496 \| 75.0 \| 1875 \| 1.2533 \| 0.6375 \| 0.4809 \| 2.9772 \| 0.6375 \| 0.6400 \| 0.1645 \| 0.1372 \| \| 0.0496 \| 76.0 \| 1900 \| 1.2539 \| 0.6375 \| 0.4808 \| 2.9777 \| 0.6375 \| 0.6393 \| 0.1675 \| 0.1376 \| \| 0.0496 \| 77.0 \| 1925 \| 1.2537 \| 0.635 \| 0.4808 \| 2.9832 \| 0.635 \| 0.6375 \| 0.1648 \| 0.1381 \| \| 0.0496 \| 78.0 \| 1950 \| 1.2539 \| 0.6375 \| 0.4807 \| 2.9769 \| 0.6375 \| 0.6394 \| 0.1636 \| 0.1374 \| \| 0.0496 \| 79.0 \| 1975 \| 1.2534 \| 0.6375 \| 0.4805 \| 2.9796 \| 0.6375 \| 0.6399 \| 0.1599 \| 0.1375 \| \| 0.048 \| 80.0 \| 2000 \| 1.2537 \| 0.6375 \| 0.4806 \| 3.0539 \| 0.6375 \| 0.6399 \| 0.1657 \| 0.1375 \| \| 0.048 \| 81.0 \| 2025 \| 1.2535 \| 0.6375 \| 0.4805 \| 3.0534 \| 0.6375 \| 0.6399 \| 0.1728 \| 0.1375 \| \| 0.048 \| 82.0 \| 2050 \| 1.2539 \| 0.6375 \| 0.4806 \| 2.9831 \| 0.6375 \| 0.6393 \| 0.1674 \| 0.1375 \| \| 0.048 \| 83.0 \| 2075 \| 1.2542 \| 0.6375 \| 0.4807 \| 3.0538 \| 0.6375 \| 0.6399 \| 0.1674 \| 0.1375 \| \| 0.048 \| 84.0 \| 2100 \| 1.2539 \| 0.6375 \| 0.4805 \| 3.0531 \| 0.6375 \| 0.6394 \| 0.1564 \| 0.1375 \| \| 0.048 \| 85.0 \| 2125 \| 1.2542 \| 0.6375 \| 0.4806 \| 3.0531 \| 0.6375 \| 0.6393 \| 0.1676 \| 0.1376 \| \| 0.048 \| 86.0 \| 2150 \| 1.2541 \| 0.6375 \| 0.4806 \| 3.0527 \| 0.6375 \| 0.6399 \| 0.1691 \| 0.1375 \| \| 0.048 \| 87.0 \| 2175 \| 1.2542 \| 0.6375 \| 0.4805 \| 3.0525 \| 0.6375 \| 0.6394 \| 0.1677 \| 0.1376 \| \| 0.048 \| 88.0 \| 2200 \| 1.2542 \| 0.6375 \| 0.4806 \| 3.0525 \| 0.6375 \| 0.6393 \| 0.1651 \| 0.1375 \| \| 0.048 \| 89.0 \| 2225 \| 1.2543 \| 0.6375 \| 0.4805 \| 3.0525 \| 0.6375 \| 0.6394 \| 0.1601 \| 0.1375 \| \| 0.048 \| 90.0 \| 2250 \| 1.2543 \| 0.6375 \| 0.4805 \| 3.0521 \| 0.6375 \| 0.6394 \| 0.1661 \| 0.1375 \| \| 0.048 \| 91.0 \| 2275 \| 1.2541 \| 0.6375 \| 0.4805 \| 3.0521 \| 0.6375 \| 0.6394 \| 0.1665 \| 0.1376 \| \| 0.048 \| 92.0 \| 2300 \| 1.2542 \| 0.6375 \| 0.4805 \| 3.0521 \| 0.6375 \| 0.6394 \| 0.1638 \| 0.1375 \| \| 0.048 \| 93.0 \| 2325 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0518 \| 0.6375 \| 0.6394 \| 0.1671 \| 0.1376 \| \| 0.048 \| 94.0 \| 2350 \| 1.2543 \| 0.6375 \| 0.4805 \| 3.0519 \| 0.6375 \| 0.6394 \| 0.1601 \| 0.1376 \| \| 0.048 \| 95.0 \| 2375 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0518 \| 0.6375 \| 0.6394 \| 0.1638 \| 0.1376 \| \| 0.048 \| 96.0 \| 2400 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0518 \| 0.6375 \| 0.6394 \| 0.1638 \| 0.1376 \| \| 0.048 \| 97.0 \| 2425 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0517 \| 0.6375 \| 0.6394 \| 0.1655 \| 0.1376 \| \| 0.048 \| 98.0 \| 2450 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0517 \| 0.6375 \| 0.6394 \| 0.1638 \| 0.1376 \| \| 0.048 \| 99.0 \| 2475 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0517 \| 0.6375 \| 0.6394 \| 0.1654 \| 0.1376 \| \| 0.0478 \| 100.0 \| 2500 \| 1.2544 \| 0.6375 \| 0.4805 \| 3.0517 \| 0.6375 \| 0.6394 \| 0.1654 \| 0.1376 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
MaitreHibou/ppo-SnowballTarget	MaitreHibou	2023-07-11T01:00:11Z	16	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-11T01:00:06Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: MaitreHibou/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
hopkins/strict-small-4	hopkins	2023-07-11T00:43:51Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-06-13T21:25:31Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: strict-small-4 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. --> # strict-small-4 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.8588 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 9 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.9925 \| 1.83 \| 1000 \| 4.2033 \| \| 3.7647 \| 3.67 \| 2000 \| 3.9152 \| \| 3.3569 \| 5.5 \| 3000 \| 3.8495 \| \| 3.0079 \| 7.34 \| 4000 \| 3.8588 \| ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
casque/CrystalMaidenv0.2	casque	2023-07-11T00:42:48Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-11T00:39:34Z	--- license: creativeml-openrail-m ---
ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH	ALM-AHME	2023-07-11T00:40:15Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T02:43:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- 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. --> # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0929 \| 1.0 \| 281 \| 0.0919 \| 0.9657 \| \| 0.0908 \| 2.0 \| 562 \| 0.0127 \| 0.9967 \| \| 0.0525 \| 3.0 \| 843 \| 0.0133 \| 0.9947 \| \| 0.1301 \| 4.0 \| 1125 \| 0.0270 \| 0.9927 \| \| 0.0624 \| 5.0 \| 1406 \| 0.0064 \| 0.9973 \| \| 0.0506 \| 6.0 \| 1687 \| 0.0025 \| 0.999 \| \| 0.0001 \| 6.99 \| 1967 \| 0.0002 \| 1.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
foster123/test	foster123	2023-07-11T00:39:29Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-10T06:23:46Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
layoric/openllama-7b-qlora-orca	layoric	2023-07-11T00:31:19Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-09T23:58:03Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
bobobert4/poca-SoccerTwos	bobobert4	2023-07-11T00:18:04Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-07-11T00:16:06Z	--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bobobert4/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jz0214/sd-class-butterflies-64	jz0214	2023-07-10T23:52:24Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T23:50:42Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jz0214/sd-class-butterflies-64') image = pipeline().images[0] image ```
shenyichong/ppo-LunarLander-v2	shenyichong	2023-07-10T23:37:07Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T23:36:50Z	--- 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: 263.84 +/- 7.90 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 ... ```
JBJoyce/whisper-large-v2-finetuned-gtzan	JBJoyce	2023-07-10T23:32:02Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-10T19:35:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-large-v2-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. --> # whisper-large-v2-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7142 - Accuracy: 0.9 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0464 \| 1.0 \| 449 \| 1.6761 \| 0.42 \| \| 0.9369 \| 2.0 \| 899 \| 1.0398 \| 0.74 \| \| 1.0591 \| 3.0 \| 1348 \| 1.0710 \| 0.78 \| \| 0.0632 \| 4.0 \| 1798 \| 0.6605 \| 0.86 \| \| 0.0022 \| 5.0 \| 2247 \| 1.0940 \| 0.82 \| \| 0.0004 \| 6.0 \| 2697 \| 0.7089 \| 0.92 \| \| 0.0004 \| 7.0 \| 3146 \| 0.6176 \| 0.92 \| \| 0.0005 \| 8.0 \| 3596 \| 0.6688 \| 0.9 \| \| 0.0002 \| 9.0 \| 4045 \| 0.7052 \| 0.9 \| \| 0.0002 \| 9.99 \| 4490 \| 0.7142 \| 0.9 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jz0214/sd-class-butterflies-32	jz0214	2023-07-10T23:09:47Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-10T23:08:46Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jz0214/sd-class-butterflies-32') image = pipeline().images[0] image ```
wesley7137/fal-7B-shard-quantum	wesley7137	2023-07-10T22:53:05Z	0	0	peft	[ "peft", "pytorch", "RefinedWebModel", "custom_code", "region:us" ]	null	2023-07-10T22:04:14Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0
jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.9	jordyvl	2023-07-10T22:40:13Z	161	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T22:00:19Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 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. --> # vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.85 - Brier Loss: 0.2432 - Nll: 1.1157 - F1 Micro: 0.85 - F1 Macro: 0.8450 - Ece: 0.1621 - Aurc: 0.0427 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.1036 \| 0.215 \| 0.8753 \| 5.3195 \| 0.2150 \| 0.1264 \| 0.2571 \| 0.6923 \| \| No log \| 2.0 \| 14 \| 1.6952 \| 0.405 \| 0.7407 \| 3.4929 \| 0.405 \| 0.2416 \| 0.2907 \| 0.4040 \| \| No log \| 3.0 \| 21 \| 1.1843 \| 0.62 \| 0.5633 \| 2.0113 \| 0.62 \| 0.5725 \| 0.2740 \| 0.2014 \| \| No log \| 4.0 \| 28 \| 0.8797 \| 0.71 \| 0.4080 \| 1.7043 \| 0.7100 \| 0.6683 \| 0.2024 \| 0.1125 \| \| No log \| 5.0 \| 35 \| 0.8570 \| 0.715 \| 0.3837 \| 1.6476 \| 0.715 \| 0.7280 \| 0.2189 \| 0.1079 \| \| No log \| 6.0 \| 42 \| 0.7484 \| 0.775 \| 0.3285 \| 1.5962 \| 0.775 \| 0.7668 \| 0.1873 \| 0.0816 \| \| No log \| 7.0 \| 49 \| 0.7337 \| 0.79 \| 0.3131 \| 1.5377 \| 0.79 \| 0.7779 \| 0.1904 \| 0.0771 \| \| No log \| 8.0 \| 56 \| 0.6709 \| 0.795 \| 0.3012 \| 1.2156 \| 0.795 \| 0.7776 \| 0.1939 \| 0.0761 \| \| No log \| 9.0 \| 63 \| 0.6901 \| 0.795 \| 0.3069 \| 1.4725 \| 0.795 \| 0.7916 \| 0.1882 \| 0.0769 \| \| No log \| 10.0 \| 70 \| 0.7960 \| 0.75 \| 0.3586 \| 1.4426 \| 0.75 \| 0.7406 \| 0.1868 \| 0.0976 \| \| No log \| 11.0 \| 77 \| 0.7489 \| 0.77 \| 0.3296 \| 1.6202 \| 0.7700 \| 0.7794 \| 0.2020 \| 0.0878 \| \| No log \| 12.0 \| 84 \| 0.7068 \| 0.785 \| 0.3270 \| 1.4127 \| 0.785 \| 0.7812 \| 0.1922 \| 0.0759 \| \| No log \| 13.0 \| 91 \| 0.6687 \| 0.79 \| 0.3050 \| 1.3820 \| 0.79 \| 0.7945 \| 0.1818 \| 0.0625 \| \| No log \| 14.0 \| 98 \| 0.6052 \| 0.79 \| 0.2854 \| 1.0602 \| 0.79 \| 0.7716 \| 0.1702 \| 0.0590 \| \| No log \| 15.0 \| 105 \| 0.6369 \| 0.795 \| 0.2959 \| 1.0580 \| 0.795 \| 0.7953 \| 0.1709 \| 0.0603 \| \| No log \| 16.0 \| 112 \| 0.6204 \| 0.81 \| 0.2816 \| 1.1886 \| 0.81 \| 0.8050 \| 0.1657 \| 0.0702 \| \| No log \| 17.0 \| 119 \| 0.5648 \| 0.83 \| 0.2475 \| 1.2506 \| 0.83 \| 0.8241 \| 0.1347 \| 0.0612 \| \| No log \| 18.0 \| 126 \| 0.5849 \| 0.83 \| 0.2672 \| 1.2245 \| 0.83 \| 0.8155 \| 0.1646 \| 0.0601 \| \| No log \| 19.0 \| 133 \| 0.5536 \| 0.835 \| 0.2475 \| 1.0514 \| 0.835 \| 0.8254 \| 0.1683 \| 0.0531 \| \| No log \| 20.0 \| 140 \| 0.5689 \| 0.835 \| 0.2513 \| 1.2369 \| 0.835 \| 0.8437 \| 0.1722 \| 0.0489 \| \| No log \| 21.0 \| 147 \| 0.5540 \| 0.83 \| 0.2485 \| 1.2139 \| 0.83 \| 0.8165 \| 0.1641 \| 0.0608 \| \| No log \| 22.0 \| 154 \| 0.5352 \| 0.835 \| 0.2402 \| 1.0108 \| 0.835 \| 0.8295 \| 0.1408 \| 0.0430 \| \| No log \| 23.0 \| 161 \| 0.5380 \| 0.84 \| 0.2403 \| 1.2280 \| 0.8400 \| 0.8347 \| 0.1405 \| 0.0436 \| \| No log \| 24.0 \| 168 \| 0.5422 \| 0.835 \| 0.2471 \| 1.0204 \| 0.835 \| 0.8324 \| 0.1606 \| 0.0445 \| \| No log \| 25.0 \| 175 \| 0.5342 \| 0.85 \| 0.2404 \| 1.0767 \| 0.85 \| 0.8487 \| 0.1469 \| 0.0432 \| \| No log \| 26.0 \| 182 \| 0.5374 \| 0.84 \| 0.2429 \| 1.0774 \| 0.8400 \| 0.8334 \| 0.1420 \| 0.0462 \| \| No log \| 27.0 \| 189 \| 0.5311 \| 0.85 \| 0.2395 \| 1.0748 \| 0.85 \| 0.8487 \| 0.1439 \| 0.0446 \| \| No log \| 28.0 \| 196 \| 0.5298 \| 0.85 \| 0.2384 \| 1.1337 \| 0.85 \| 0.8487 \| 0.1570 \| 0.0437 \| \| No log \| 29.0 \| 203 \| 0.5387 \| 0.845 \| 0.2435 \| 1.1319 \| 0.845 \| 0.8424 \| 0.1539 \| 0.0458 \| \| No log \| 30.0 \| 210 \| 0.5361 \| 0.85 \| 0.2430 \| 1.0648 \| 0.85 \| 0.8450 \| 0.1679 \| 0.0431 \| \| No log \| 31.0 \| 217 \| 0.5339 \| 0.85 \| 0.2413 \| 1.0676 \| 0.85 \| 0.8487 \| 0.1646 \| 0.0428 \| \| No log \| 32.0 \| 224 \| 0.5345 \| 0.85 \| 0.2421 \| 1.0709 \| 0.85 \| 0.8487 \| 0.1476 \| 0.0440 \| \| No log \| 33.0 \| 231 \| 0.5343 \| 0.85 \| 0.2421 \| 1.1236 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0431 \| \| No log \| 34.0 \| 238 \| 0.5353 \| 0.845 \| 0.2426 \| 1.1244 \| 0.845 \| 0.8424 \| 0.1710 \| 0.0428 \| \| No log \| 35.0 \| 245 \| 0.5346 \| 0.85 \| 0.2423 \| 1.0649 \| 0.85 \| 0.8487 \| 0.1520 \| 0.0440 \| \| No log \| 36.0 \| 252 \| 0.5356 \| 0.855 \| 0.2422 \| 1.1241 \| 0.855 \| 0.8517 \| 0.1814 \| 0.0429 \| \| No log \| 37.0 \| 259 \| 0.5357 \| 0.85 \| 0.2426 \| 1.1237 \| 0.85 \| 0.8450 \| 0.1670 \| 0.0425 \| \| No log \| 38.0 \| 266 \| 0.5356 \| 0.845 \| 0.2426 \| 1.1226 \| 0.845 \| 0.8419 \| 0.1607 \| 0.0435 \| \| No log \| 39.0 \| 273 \| 0.5347 \| 0.855 \| 0.2420 \| 1.0739 \| 0.855 \| 0.8517 \| 0.1597 \| 0.0427 \| \| No log \| 40.0 \| 280 \| 0.5356 \| 0.855 \| 0.2423 \| 1.1203 \| 0.855 \| 0.8517 \| 0.1676 \| 0.0435 \| \| No log \| 41.0 \| 287 \| 0.5365 \| 0.85 \| 0.2431 \| 1.1199 \| 0.85 \| 0.8450 \| 0.1780 \| 0.0429 \| \| No log \| 42.0 \| 294 \| 0.5356 \| 0.85 \| 0.2426 \| 1.1173 \| 0.85 \| 0.8450 \| 0.1653 \| 0.0430 \| \| No log \| 43.0 \| 301 \| 0.5363 \| 0.85 \| 0.2428 \| 1.1189 \| 0.85 \| 0.8450 \| 0.1550 \| 0.0435 \| \| No log \| 44.0 \| 308 \| 0.5345 \| 0.85 \| 0.2418 \| 1.1193 \| 0.85 \| 0.8450 \| 0.1590 \| 0.0428 \| \| No log \| 45.0 \| 315 \| 0.5374 \| 0.85 \| 0.2435 \| 1.1202 \| 0.85 \| 0.8450 \| 0.1633 \| 0.0435 \| \| No log \| 46.0 \| 322 \| 0.5355 \| 0.85 \| 0.2423 \| 1.1183 \| 0.85 \| 0.8450 \| 0.1564 \| 0.0428 \| \| No log \| 47.0 \| 329 \| 0.5354 \| 0.85 \| 0.2425 \| 1.1176 \| 0.85 \| 0.8450 \| 0.1509 \| 0.0429 \| \| No log \| 48.0 \| 336 \| 0.5369 \| 0.85 \| 0.2433 \| 1.1177 \| 0.85 \| 0.8450 \| 0.1517 \| 0.0432 \| \| No log \| 49.0 \| 343 \| 0.5361 \| 0.85 \| 0.2428 \| 1.1182 \| 0.85 \| 0.8450 \| 0.1490 \| 0.0428 \| \| No log \| 50.0 \| 350 \| 0.5364 \| 0.85 \| 0.2431 \| 1.1179 \| 0.85 \| 0.8450 \| 0.1654 \| 0.0430 \| \| No log \| 51.0 \| 357 \| 0.5365 \| 0.85 \| 0.2428 \| 1.1185 \| 0.85 \| 0.8450 \| 0.1729 \| 0.0432 \| \| No log \| 52.0 \| 364 \| 0.5364 \| 0.85 \| 0.2430 \| 1.1165 \| 0.85 \| 0.8450 \| 0.1614 \| 0.0429 \| \| No log \| 53.0 \| 371 \| 0.5362 \| 0.85 \| 0.2429 \| 1.1167 \| 0.85 \| 0.8450 \| 0.1694 \| 0.0430 \| \| No log \| 54.0 \| 378 \| 0.5369 \| 0.85 \| 0.2432 \| 1.1170 \| 0.85 \| 0.8450 \| 0.1597 \| 0.0432 \| \| No log \| 55.0 \| 385 \| 0.5368 \| 0.85 \| 0.2430 \| 1.1168 \| 0.85 \| 0.8450 \| 0.1670 \| 0.0429 \| \| No log \| 56.0 \| 392 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1180 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0430 \| \| No log \| 57.0 \| 399 \| 0.5364 \| 0.85 \| 0.2429 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1649 \| 0.0429 \| \| No log \| 58.0 \| 406 \| 0.5364 \| 0.85 \| 0.2430 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1611 \| 0.0429 \| \| No log \| 59.0 \| 413 \| 0.5365 \| 0.85 \| 0.2428 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1591 \| 0.0429 \| \| No log \| 60.0 \| 420 \| 0.5364 \| 0.85 \| 0.2429 \| 1.1155 \| 0.85 \| 0.8450 \| 0.1588 \| 0.0429 \| \| No log \| 61.0 \| 427 \| 0.5370 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1772 \| 0.0432 \| \| No log \| 62.0 \| 434 \| 0.5367 \| 0.85 \| 0.2429 \| 1.1167 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0429 \| \| No log \| 63.0 \| 441 \| 0.5362 \| 0.85 \| 0.2428 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1503 \| 0.0428 \| \| No log \| 64.0 \| 448 \| 0.5372 \| 0.85 \| 0.2433 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1616 \| 0.0432 \| \| No log \| 65.0 \| 455 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1499 \| 0.0429 \| \| No log \| 66.0 \| 462 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1591 \| 0.0427 \| \| No log \| 67.0 \| 469 \| 0.5367 \| 0.85 \| 0.2430 \| 1.1164 \| 0.85 \| 0.8450 \| 0.1562 \| 0.0428 \| \| No log \| 68.0 \| 476 \| 0.5368 \| 0.85 \| 0.2430 \| 1.1168 \| 0.85 \| 0.8450 \| 0.1556 \| 0.0427 \| \| No log \| 69.0 \| 483 \| 0.5368 \| 0.85 \| 0.2431 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1593 \| 0.0428 \| \| No log \| 70.0 \| 490 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1628 \| 0.0428 \| \| No log \| 71.0 \| 497 \| 0.5371 \| 0.85 \| 0.2432 \| 1.1163 \| 0.85 \| 0.8450 \| 0.1599 \| 0.0429 \| \| 0.1708 \| 72.0 \| 504 \| 0.5370 \| 0.85 \| 0.2430 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1559 \| 0.0430 \| \| 0.1708 \| 73.0 \| 511 \| 0.5372 \| 0.85 \| 0.2433 \| 1.1154 \| 0.85 \| 0.8450 \| 0.1556 \| 0.0428 \| \| 0.1708 \| 74.0 \| 518 \| 0.5370 \| 0.85 \| 0.2429 \| 1.1165 \| 0.85 \| 0.8450 \| 0.1540 \| 0.0428 \| \| 0.1708 \| 75.0 \| 525 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1616 \| 0.0427 \| \| 0.1708 \| 76.0 \| 532 \| 0.5369 \| 0.85 \| 0.2431 \| 1.1161 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0427 \| \| 0.1708 \| 77.0 \| 539 \| 0.5369 \| 0.85 \| 0.2430 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1623 \| 0.0429 \| \| 0.1708 \| 78.0 \| 546 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0427 \| \| 0.1708 \| 79.0 \| 553 \| 0.5375 \| 0.85 \| 0.2433 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1688 \| 0.0429 \| \| 0.1708 \| 80.0 \| 560 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1623 \| 0.0429 \| \| 0.1708 \| 81.0 \| 567 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1162 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0428 \| \| 0.1708 \| 82.0 \| 574 \| 0.5374 \| 0.85 \| 0.2433 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0428 \| \| 0.1708 \| 83.0 \| 581 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1159 \| 0.85 \| 0.8450 \| 0.1622 \| 0.0428 \| \| 0.1708 \| 84.0 \| 588 \| 0.5371 \| 0.85 \| 0.2431 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 85.0 \| 595 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1687 \| 0.0426 \| \| 0.1708 \| 86.0 \| 602 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1619 \| 0.0426 \| \| 0.1708 \| 87.0 \| 609 \| 0.5374 \| 0.85 \| 0.2432 \| 1.1159 \| 0.85 \| 0.8450 \| 0.1687 \| 0.0428 \| \| 0.1708 \| 88.0 \| 616 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1160 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 89.0 \| 623 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 90.0 \| 630 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 91.0 \| 637 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 92.0 \| 644 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 93.0 \| 651 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1156 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 94.0 \| 658 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1158 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 95.0 \| 665 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 96.0 \| 672 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 97.0 \| 679 \| 0.5372 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1620 \| 0.0427 \| \| 0.1708 \| 98.0 \| 686 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 99.0 \| 693 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| \| 0.1708 \| 100.0 \| 700 \| 0.5373 \| 0.85 \| 0.2432 \| 1.1157 \| 0.85 \| 0.8450 \| 0.1621 \| 0.0427 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
maidacundo/falcon_qlora_sql_r2	maidacundo	2023-07-10T22:30:14Z	0	0	null	[ "generated_from_trainer", "dataset:spider", "base_model:tiiuae/falcon-7b", "base_model:finetune:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	null	2023-07-10T09:40:03Z	--- license: apache-2.0 base_model: tiiuae/falcon-7b tags: - generated_from_trainer datasets: - spider model-index: - name: falcon_qlora_sql_r2 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. --> # falcon_qlora_sql_r2 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the spider dataset. It achieves the following results on the evaluation set: - Loss: 0.1735 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 43.7 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2993 \| 0.23 \| 100 \| 0.2863 \| \| 0.8003 \| 0.46 \| 200 \| 0.3358 \| \| 0.1872 \| 0.68 \| 300 \| 0.2424 \| \| 0.1267 \| 0.91 \| 400 \| 0.2362 \| \| 0.2214 \| 1.14 \| 500 \| 0.2564 \| \| 0.2885 \| 1.37 \| 600 \| 0.2187 \| \| 0.1654 \| 1.6 \| 700 \| 0.1988 \| \| 0.1633 \| 1.83 \| 800 \| 0.2062 \| \| 0.0381 \| 2.05 \| 900 \| 0.1868 \| \| 0.0633 \| 2.28 \| 1000 \| 0.1767 \| \| 0.163 \| 2.51 \| 1100 \| 0.1861 \| \| 0.1718 \| 2.74 \| 1200 \| 0.1875 \| \| 0.1743 \| 2.97 \| 1300 \| 0.1854 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_MSE	jordyvl	2023-07-10T22:30:03Z	163	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T21:13:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_MSE 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. --> # vit-small_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4673 - Accuracy: 0.6425 - Brier Loss: 0.4763 - Nll: 3.0680 - F1 Micro: 0.6425 - F1 Macro: 0.6485 - Ece: 0.1946 - Aurc: 0.1381 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 4.4851 \| 0.06 \| 0.9565 \| 13.8276 \| 0.06 \| 0.0556 \| 0.1688 \| 0.9385 \| \| No log \| 2.0 \| 50 \| 3.5619 \| 0.3775 \| 0.7827 \| 6.2649 \| 0.3775 \| 0.3611 \| 0.2331 \| 0.3882 \| \| No log \| 3.0 \| 75 \| 2.8990 \| 0.5025 \| 0.6453 \| 4.7376 \| 0.5025 \| 0.4858 \| 0.1689 \| 0.2658 \| \| No log \| 4.0 \| 100 \| 2.5972 \| 0.515 \| 0.5980 \| 4.4210 \| 0.515 \| 0.4895 \| 0.1605 \| 0.2249 \| \| No log \| 5.0 \| 125 \| 2.4353 \| 0.56 \| 0.5762 \| 3.4885 \| 0.56 \| 0.5566 \| 0.1548 \| 0.2100 \| \| No log \| 6.0 \| 150 \| 2.4157 \| 0.5475 \| 0.5864 \| 3.8261 \| 0.5475 \| 0.5323 \| 0.1837 \| 0.2167 \| \| No log \| 7.0 \| 175 \| 2.1786 \| 0.6075 \| 0.5203 \| 3.4565 \| 0.6075 \| 0.6103 \| 0.1403 \| 0.1670 \| \| No log \| 8.0 \| 200 \| 2.1082 \| 0.63 \| 0.5040 \| 3.3570 \| 0.63 \| 0.6246 \| 0.1580 \| 0.1530 \| \| No log \| 9.0 \| 225 \| 2.0472 \| 0.625 \| 0.5042 \| 3.8572 \| 0.625 \| 0.6184 \| 0.1552 \| 0.1530 \| \| No log \| 10.0 \| 250 \| 2.0589 \| 0.6025 \| 0.5468 \| 3.5723 \| 0.6025 \| 0.5982 \| 0.1781 \| 0.1785 \| \| No log \| 11.0 \| 275 \| 1.8965 \| 0.65 \| 0.4755 \| 3.4466 \| 0.65 \| 0.6497 \| 0.1605 \| 0.1475 \| \| No log \| 12.0 \| 300 \| 1.9014 \| 0.6325 \| 0.5066 \| 3.0881 \| 0.6325 \| 0.6359 \| 0.1658 \| 0.1591 \| \| No log \| 13.0 \| 325 \| 1.7904 \| 0.6175 \| 0.5162 \| 3.4673 \| 0.6175 \| 0.6141 \| 0.1525 \| 0.1598 \| \| No log \| 14.0 \| 350 \| 1.8624 \| 0.625 \| 0.5173 \| 3.6824 \| 0.625 \| 0.6179 \| 0.1567 \| 0.1624 \| \| No log \| 15.0 \| 375 \| 1.7083 \| 0.6625 \| 0.4817 \| 3.1296 \| 0.6625 \| 0.6686 \| 0.1651 \| 0.1405 \| \| No log \| 16.0 \| 400 \| 1.8848 \| 0.59 \| 0.5478 \| 4.3761 \| 0.59 \| 0.5913 \| 0.2083 \| 0.1696 \| \| No log \| 17.0 \| 425 \| 1.7238 \| 0.6125 \| 0.5229 \| 3.1232 \| 0.6125 \| 0.6052 \| 0.1833 \| 0.1553 \| \| No log \| 18.0 \| 450 \| 1.7126 \| 0.625 \| 0.5152 \| 2.9267 \| 0.625 \| 0.6284 \| 0.1747 \| 0.1565 \| \| No log \| 19.0 \| 475 \| 1.6459 \| 0.6275 \| 0.5024 \| 2.9078 \| 0.6275 \| 0.6219 \| 0.1766 \| 0.1527 \| \| 1.0542 \| 20.0 \| 500 \| 1.6029 \| 0.6275 \| 0.4855 \| 3.0931 \| 0.6275 \| 0.6316 \| 0.1720 \| 0.1414 \| \| 1.0542 \| 21.0 \| 525 \| 1.6566 \| 0.6525 \| 0.4847 \| 3.0998 \| 0.6525 \| 0.6479 \| 0.1558 \| 0.1438 \| \| 1.0542 \| 22.0 \| 550 \| 1.6169 \| 0.645 \| 0.4894 \| 3.0081 \| 0.645 \| 0.6471 \| 0.1687 \| 0.1400 \| \| 1.0542 \| 23.0 \| 575 \| 1.5322 \| 0.6525 \| 0.4557 \| 3.3587 \| 0.6525 \| 0.6520 \| 0.1428 \| 0.1247 \| \| 1.0542 \| 24.0 \| 600 \| 1.5991 \| 0.6475 \| 0.4787 \| 2.9349 \| 0.6475 \| 0.6444 \| 0.1580 \| 0.1450 \| \| 1.0542 \| 25.0 \| 625 \| 1.5625 \| 0.6375 \| 0.4926 \| 3.0245 \| 0.6375 \| 0.6378 \| 0.1641 \| 0.1433 \| \| 1.0542 \| 26.0 \| 650 \| 1.5366 \| 0.64 \| 0.4884 \| 3.3388 \| 0.64 \| 0.6461 \| 0.1595 \| 0.1453 \| \| 1.0542 \| 27.0 \| 675 \| 1.5686 \| 0.65 \| 0.4765 \| 3.5120 \| 0.65 \| 0.6504 \| 0.1625 \| 0.1359 \| \| 1.0542 \| 28.0 \| 700 \| 1.5562 \| 0.6475 \| 0.4817 \| 3.0348 \| 0.6475 \| 0.6488 \| 0.1459 \| 0.1388 \| \| 1.0542 \| 29.0 \| 725 \| 1.5213 \| 0.6475 \| 0.4719 \| 3.2628 \| 0.6475 \| 0.6475 \| 0.1634 \| 0.1326 \| \| 1.0542 \| 30.0 \| 750 \| 1.5492 \| 0.6675 \| 0.4730 \| 3.1693 \| 0.6675 \| 0.6679 \| 0.1469 \| 0.1415 \| \| 1.0542 \| 31.0 \| 775 \| 1.5311 \| 0.65 \| 0.4896 \| 3.0881 \| 0.65 \| 0.6504 \| 0.1815 \| 0.1380 \| \| 1.0542 \| 32.0 \| 800 \| 1.5556 \| 0.6475 \| 0.4821 \| 3.1829 \| 0.6475 \| 0.6491 \| 0.1640 \| 0.1405 \| \| 1.0542 \| 33.0 \| 825 \| 1.5471 \| 0.6375 \| 0.4846 \| 3.4190 \| 0.6375 \| 0.6407 \| 0.1628 \| 0.1415 \| \| 1.0542 \| 34.0 \| 850 \| 1.4809 \| 0.6575 \| 0.4714 \| 2.9136 \| 0.6575 \| 0.6612 \| 0.1729 \| 0.1338 \| \| 1.0542 \| 35.0 \| 875 \| 1.5256 \| 0.66 \| 0.4773 \| 3.2303 \| 0.66 \| 0.6650 \| 0.1746 \| 0.1368 \| \| 1.0542 \| 36.0 \| 900 \| 1.4929 \| 0.6675 \| 0.4671 \| 3.2360 \| 0.6675 \| 0.6698 \| 0.1698 \| 0.1309 \| \| 1.0542 \| 37.0 \| 925 \| 1.4923 \| 0.645 \| 0.4880 \| 3.0567 \| 0.645 \| 0.6564 \| 0.1764 \| 0.1395 \| \| 1.0542 \| 38.0 \| 950 \| 1.5038 \| 0.665 \| 0.4672 \| 3.2116 \| 0.665 \| 0.6661 \| 0.1588 \| 0.1343 \| \| 1.0542 \| 39.0 \| 975 \| 1.4708 \| 0.6625 \| 0.4669 \| 3.1420 \| 0.6625 \| 0.6675 \| 0.1683 \| 0.1301 \| \| 0.0522 \| 40.0 \| 1000 \| 1.5153 \| 0.6475 \| 0.4865 \| 3.1796 \| 0.6475 \| 0.6447 \| 0.1639 \| 0.1400 \| \| 0.0522 \| 41.0 \| 1025 \| 1.4705 \| 0.6575 \| 0.4642 \| 3.2196 \| 0.6575 \| 0.6626 \| 0.1440 \| 0.1308 \| \| 0.0522 \| 42.0 \| 1050 \| 1.4844 \| 0.6575 \| 0.4722 \| 3.2445 \| 0.6575 \| 0.6595 \| 0.1746 \| 0.1328 \| \| 0.0522 \| 43.0 \| 1075 \| 1.4957 \| 0.6425 \| 0.4828 \| 3.1456 \| 0.6425 \| 0.6468 \| 0.1499 \| 0.1417 \| \| 0.0522 \| 44.0 \| 1100 \| 1.5179 \| 0.645 \| 0.4910 \| 3.3921 \| 0.645 \| 0.6470 \| 0.1861 \| 0.1433 \| \| 0.0522 \| 45.0 \| 1125 \| 1.4878 \| 0.6425 \| 0.4839 \| 3.2139 \| 0.6425 \| 0.6478 \| 0.1720 \| 0.1403 \| \| 0.0522 \| 46.0 \| 1150 \| 1.4666 \| 0.655 \| 0.4741 \| 2.9333 \| 0.655 \| 0.6601 \| 0.1813 \| 0.1347 \| \| 0.0522 \| 47.0 \| 1175 \| 1.4954 \| 0.6575 \| 0.4776 \| 3.2102 \| 0.6575 \| 0.6604 \| 0.1842 \| 0.1390 \| \| 0.0522 \| 48.0 \| 1200 \| 1.4976 \| 0.645 \| 0.4856 \| 3.1539 \| 0.645 \| 0.6493 \| 0.1549 \| 0.1407 \| \| 0.0522 \| 49.0 \| 1225 \| 1.4772 \| 0.64 \| 0.4780 \| 2.9845 \| 0.64 \| 0.6445 \| 0.1826 \| 0.1388 \| \| 0.0522 \| 50.0 \| 1250 \| 1.4584 \| 0.65 \| 0.4703 \| 3.0776 \| 0.65 \| 0.6533 \| 0.1685 \| 0.1352 \| \| 0.0522 \| 51.0 \| 1275 \| 1.4828 \| 0.6325 \| 0.4844 \| 3.1425 \| 0.6325 \| 0.6377 \| 0.1641 \| 0.1409 \| \| 0.0522 \| 52.0 \| 1300 \| 1.4676 \| 0.6525 \| 0.4737 \| 3.1483 \| 0.6525 \| 0.6565 \| 0.1773 \| 0.1358 \| \| 0.0522 \| 53.0 \| 1325 \| 1.4675 \| 0.6475 \| 0.4791 \| 3.1411 \| 0.6475 \| 0.6515 \| 0.1820 \| 0.1388 \| \| 0.0522 \| 54.0 \| 1350 \| 1.4724 \| 0.645 \| 0.4764 \| 3.0744 \| 0.645 \| 0.6499 \| 0.1847 \| 0.1382 \| \| 0.0522 \| 55.0 \| 1375 \| 1.4689 \| 0.6425 \| 0.4769 \| 3.2256 \| 0.6425 \| 0.6476 \| 0.1839 \| 0.1376 \| \| 0.0522 \| 56.0 \| 1400 \| 1.4660 \| 0.6425 \| 0.4760 \| 2.9907 \| 0.6425 \| 0.6479 \| 0.1906 \| 0.1378 \| \| 0.0522 \| 57.0 \| 1425 \| 1.4663 \| 0.645 \| 0.4757 \| 3.0722 \| 0.645 \| 0.6514 \| 0.1705 \| 0.1367 \| \| 0.0522 \| 58.0 \| 1450 \| 1.4678 \| 0.65 \| 0.4770 \| 3.0710 \| 0.65 \| 0.6546 \| 0.1794 \| 0.1371 \| \| 0.0522 \| 59.0 \| 1475 \| 1.4717 \| 0.64 \| 0.4786 \| 3.0737 \| 0.64 \| 0.6455 \| 0.1889 \| 0.1392 \| \| 0.0064 \| 60.0 \| 1500 \| 1.4691 \| 0.645 \| 0.4768 \| 3.0688 \| 0.645 \| 0.6499 \| 0.1815 \| 0.1378 \| \| 0.0064 \| 61.0 \| 1525 \| 1.4689 \| 0.64 \| 0.4767 \| 3.0688 \| 0.64 \| 0.6452 \| 0.1846 \| 0.1382 \| \| 0.0064 \| 62.0 \| 1550 \| 1.4689 \| 0.64 \| 0.4770 \| 3.0674 \| 0.64 \| 0.6455 \| 0.1937 \| 0.1383 \| \| 0.0064 \| 63.0 \| 1575 \| 1.4687 \| 0.6425 \| 0.4767 \| 3.0700 \| 0.6425 \| 0.6485 \| 0.1897 \| 0.1381 \| \| 0.0064 \| 64.0 \| 1600 \| 1.4674 \| 0.6425 \| 0.4764 \| 3.0675 \| 0.6425 \| 0.6472 \| 0.1855 \| 0.1375 \| \| 0.0064 \| 65.0 \| 1625 \| 1.4681 \| 0.6425 \| 0.4766 \| 3.0694 \| 0.6425 \| 0.6485 \| 0.1917 \| 0.1381 \| \| 0.0064 \| 66.0 \| 1650 \| 1.4681 \| 0.6425 \| 0.4766 \| 3.0687 \| 0.6425 \| 0.6472 \| 0.1905 \| 0.1378 \| \| 0.0064 \| 67.0 \| 1675 \| 1.4667 \| 0.645 \| 0.4757 \| 3.0681 \| 0.645 \| 0.6505 \| 0.1899 \| 0.1375 \| \| 0.0064 \| 68.0 \| 1700 \| 1.4683 \| 0.6425 \| 0.4771 \| 3.0686 \| 0.6425 \| 0.6474 \| 0.1871 \| 0.1379 \| \| 0.0064 \| 69.0 \| 1725 \| 1.4672 \| 0.64 \| 0.4760 \| 3.0679 \| 0.64 \| 0.6455 \| 0.1932 \| 0.1380 \| \| 0.0064 \| 70.0 \| 1750 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0683 \| 0.6425 \| 0.6474 \| 0.1955 \| 0.1376 \| \| 0.0064 \| 71.0 \| 1775 \| 1.4676 \| 0.645 \| 0.4763 \| 3.0680 \| 0.645 \| 0.6505 \| 0.1921 \| 0.1376 \| \| 0.0064 \| 72.0 \| 1800 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0683 \| 0.6425 \| 0.6474 \| 0.1946 \| 0.1376 \| \| 0.0064 \| 73.0 \| 1825 \| 1.4675 \| 0.6425 \| 0.4763 \| 3.0682 \| 0.6425 \| 0.6474 \| 0.1946 \| 0.1377 \| \| 0.0064 \| 74.0 \| 1850 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0682 \| 0.6425 \| 0.6485 \| 0.1945 \| 0.1380 \| \| 0.0064 \| 75.0 \| 1875 \| 1.4674 \| 0.64 \| 0.4763 \| 3.0680 \| 0.64 \| 0.6455 \| 0.1960 \| 0.1380 \| \| 0.0064 \| 76.0 \| 1900 \| 1.4675 \| 0.64 \| 0.4764 \| 3.0682 \| 0.64 \| 0.6455 \| 0.1972 \| 0.1381 \| \| 0.0064 \| 77.0 \| 1925 \| 1.4675 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1947 \| 0.1380 \| \| 0.0064 \| 78.0 \| 1950 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0064 \| 79.0 \| 1975 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6474 \| 0.1935 \| 0.1376 \| \| 0.0 \| 80.0 \| 2000 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1380 \| \| 0.0 \| 81.0 \| 2025 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1380 \| \| 0.0 \| 82.0 \| 2050 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1935 \| 0.1380 \| \| 0.0 \| 83.0 \| 2075 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 84.0 \| 2100 \| 1.4674 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0 \| 85.0 \| 2125 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 86.0 \| 2150 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 87.0 \| 2175 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0681 \| 0.6425 \| 0.6485 \| 0.1958 \| 0.1381 \| \| 0.0 \| 88.0 \| 2200 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 89.0 \| 2225 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 90.0 \| 2250 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 91.0 \| 2275 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 92.0 \| 2300 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 93.0 \| 2325 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 94.0 \| 2350 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1909 \| 0.1381 \| \| 0.0 \| 95.0 \| 2375 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 96.0 \| 2400 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 97.0 \| 2425 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 98.0 \| 2450 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 99.0 \| 2475 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| \| 0.0 \| 100.0 \| 2500 \| 1.4673 \| 0.6425 \| 0.4763 \| 3.0680 \| 0.6425 \| 0.6485 \| 0.1946 \| 0.1381 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
MnLgt/swivel_inversion	MnLgt	2023-07-10T22:11:42Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-07-10T22:11:41Z	--- license: mit --- ### swivel_inversion on Stable Diffusion This is the `<swivel-chair>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<swivel-chair> 0](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/942493f105b42e65e9bbb2afb8fd24ee.jpg) ![<swivel-chair> 1](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/25a60f0b7e1df4480da0096f4855d3cd.jpg) ![<swivel-chair> 2](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/099ba1cdff4a7d6d76437ec3b9d48743.jpg) ![<swivel-chair> 3](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a95a8487c048359027c5dc1f2f4231cd.jpg) ![<swivel-chair> 4](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e2258228f0c125fc4f0d2b3c27c4b5b5.jpg) ![<swivel-chair> 5](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/7692300d5457b1ad0b9b77bb4370a7b5.jpg) ![<swivel-chair> 6](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3b7fb905cd512af41d664db5b5c9c489.jpg) ![<swivel-chair> 7](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/89803df4905f81d2c1f70a1a7faf68fd.jpg) ![<swivel-chair> 8](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/570e6f83c4b0cd052893aee8e7030521.jpg) ![<swivel-chair> 9](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/465e22dc7bbfd6f42a803e8ab35c0609.jpg) ![<swivel-chair> 10](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a6490c193d2815bd520a2478fcdb543f.jpg) ![<swivel-chair> 11](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/c789ecc814b35df75187611633dbd84a.jpg) ![<swivel-chair> 12](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/11ca822a037aa86f3316564ac212ac1c.jpg) ![<swivel-chair> 13](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/5dea3e2b3148e21a4cb4dfe8dea7af08.jpg) ![<swivel-chair> 14](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/46b89058d8c2342f3c152b50039cb0c9.jpg) ![<swivel-chair> 15](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/749bf590f4dd9f4c3ef1ffd58e7db3e8.jpg) ![<swivel-chair> 16](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6e32521663ac1cd6d0999e4a09dbf5a1.jpg) ![<swivel-chair> 17](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/bd3580a999d8ec073f2e9e7584fb1479.jpg) ![<swivel-chair> 18](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/fa116ff22175ba831f641af9bc1b44c8.jpg) ![<swivel-chair> 19](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/f32510be7c6e3d2d540d53ef0c0b5536.jpg) ![<swivel-chair> 20](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3aab159b96dbcc1d403eeeea81191fb2.jpg) ![<swivel-chair> 21](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6405e9bae158bf99eab384a36468c0cc.jpg) ![<swivel-chair> 22](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e727d064c7c19b510acaacb2637c195e.jpg) ![<swivel-chair> 23](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ebf0269fca456ea8e2e307f6de6222ef.jpg) ![<swivel-chair> 24](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ac4a58c646a756d07608c485bbe7fa45.jpg)
carova/ppo-LunarLander-v2	carova	2023-07-10T22:11:10Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T17:56:56Z	--- 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: 233.36 +/- 68.33 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 ... ```
TheBloke/airochronos-33B-GGML	TheBloke	2023-07-10T22:07:18Z	0	18	null	[ "license:other", "region:us" ]	null	2023-07-10T21:14:18Z	--- inference: false license: other --- <!-- header start --> <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <!-- header end --> # Henk717's Airochronos 33B GGML These files are GGML format model files for [Henk717's Airochronos 33B](https://huggingface.co/Henk717/airochronos-33B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/airochronos-33B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/airochronos-33B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Henk717/airochronos-33B) ## Prompt template: Alpaca ```Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- compatibility_ggml start --> ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| airochronos-33b.ggmlv3.q2_K.bin \| q2_K \| 2 \| 13.71 GB\| 16.21 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| airochronos-33b.ggmlv3.q3_K_S.bin \| q3_K_S \| 3 \| 14.06 GB\| 16.56 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| airochronos-33b.ggmlv3.q3_K_M.bin \| q3_K_M \| 3 \| 15.72 GB\| 18.22 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| airochronos-33b.ggmlv3.q3_K_L.bin \| q3_K_L \| 3 \| 17.28 GB\| 19.78 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| airochronos-33b.ggmlv3.q4_K_S.bin \| q4_K_S \| 4 \| 18.36 GB\| 20.86 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| airochronos-33b.ggmlv3.q4_K_M.bin \| q4_K_M \| 4 \| 19.62 GB\| 22.12 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| airochronos-33b.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 18.30 GB\| 20.80 GB \| Original quant method, 4-bit. \| \| airochronos-33b.ggmlv3.q4_1.bin \| q4_1 \| 4 \| 20.33 GB\| 22.83 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| airochronos-33b.ggmlv3.q5_K_S.bin \| q5_K_S \| 5 \| 22.40 GB\| 24.90 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| airochronos-33b.ggmlv3.q5_K_M.bin \| q5_K_M \| 5 \| 23.05 GB\| 25.55 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| airochronos-33b.ggmlv3.q5_0.bin \| q5_0 \| 5 \| 22.37 GB\| 24.87 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| airochronos-33b.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 24.40 GB\| 26.90 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| airochronos-33b.ggmlv3.q6_K.bin \| q6_K \| 6 \| 26.69 GB\| 29.19 GB \| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors \| \| airochronos-33b.ggmlv3.q8_0.bin \| q8_0 \| 8 \| 34.56 GB\| 37.06 GB \| Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m airochronos-33b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). <!-- footer start --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Luke from CarbonQuill, Aemon Algiz. Patreon special mentions: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang. Thank you to all my generous patrons and donaters! <!-- footer end --> # Original model card: Henk717's Airochronos 33B After the initial experiment with chronoboros-33B it was evident that the merge was to unpredictable to be useful, testing the individual models it became clear that the bias should be weighted towards Chronos. This is the new release of the merge with 75% chronos 33B, and 25% airoboros-1.4 33B. Model has been tested with the Alpaca prompting format combined with KoboldAI Lite's instruct and chat modes, as well as regular story writing. It has also been tested on basic reasoning tasks, but has not seen much testing for factual information.
jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.7	jordyvl	2023-07-10T21:59:33Z	161	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T21:19:56Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 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. --> # vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4918 - Accuracy: 0.85 - Brier Loss: 0.2583 - Nll: 1.0894 - F1 Micro: 0.85 - F1 Macro: 0.8374 - Ece: 0.1917 - Aurc: 0.0470 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.8329 \| 0.225 \| 0.8761 \| 5.2731 \| 0.225 \| 0.1384 \| 0.2607 \| 0.6977 \| \| No log \| 2.0 \| 14 \| 1.4785 \| 0.405 \| 0.7460 \| 3.4067 \| 0.405 \| 0.2289 \| 0.3097 \| 0.4085 \| \| No log \| 3.0 \| 21 \| 1.0406 \| 0.6 \| 0.5725 \| 1.8722 \| 0.6 \| 0.5345 \| 0.3050 \| 0.2010 \| \| No log \| 4.0 \| 28 \| 0.8087 \| 0.725 \| 0.4192 \| 1.6096 \| 0.7250 \| 0.6767 \| 0.2345 \| 0.1149 \| \| No log \| 5.0 \| 35 \| 0.7666 \| 0.735 \| 0.3731 \| 1.6189 \| 0.735 \| 0.7350 \| 0.2377 \| 0.1011 \| \| No log \| 6.0 \| 42 \| 0.6960 \| 0.78 \| 0.3413 \| 1.5230 \| 0.78 \| 0.7592 \| 0.2295 \| 0.0868 \| \| No log \| 7.0 \| 49 \| 0.6490 \| 0.805 \| 0.3110 \| 1.4861 \| 0.805 \| 0.7864 \| 0.2138 \| 0.0785 \| \| No log \| 8.0 \| 56 \| 0.6238 \| 0.795 \| 0.3069 \| 1.2098 \| 0.795 \| 0.7816 \| 0.2065 \| 0.0698 \| \| No log \| 9.0 \| 63 \| 0.5755 \| 0.83 \| 0.2866 \| 1.1943 \| 0.83 \| 0.8117 \| 0.1937 \| 0.0694 \| \| No log \| 10.0 \| 70 \| 0.6360 \| 0.77 \| 0.3164 \| 1.2608 \| 0.7700 \| 0.7550 \| 0.1785 \| 0.0677 \| \| No log \| 11.0 \| 77 \| 0.6548 \| 0.785 \| 0.3335 \| 1.4895 \| 0.785 \| 0.7707 \| 0.2281 \| 0.0885 \| \| No log \| 12.0 \| 84 \| 0.5847 \| 0.805 \| 0.3002 \| 1.4317 \| 0.805 \| 0.7807 \| 0.2264 \| 0.0756 \| \| No log \| 13.0 \| 91 \| 0.5956 \| 0.81 \| 0.3040 \| 1.2590 \| 0.81 \| 0.7928 \| 0.2241 \| 0.0556 \| \| No log \| 14.0 \| 98 \| 0.5692 \| 0.81 \| 0.3025 \| 1.2119 \| 0.81 \| 0.8043 \| 0.2235 \| 0.0665 \| \| No log \| 15.0 \| 105 \| 0.5223 \| 0.83 \| 0.2762 \| 1.1162 \| 0.83 \| 0.8221 \| 0.1798 \| 0.0552 \| \| No log \| 16.0 \| 112 \| 0.4981 \| 0.84 \| 0.2523 \| 1.0864 \| 0.8400 \| 0.8372 \| 0.1868 \| 0.0396 \| \| No log \| 17.0 \| 119 \| 0.5207 \| 0.805 \| 0.2741 \| 1.0416 \| 0.805 \| 0.7897 \| 0.1960 \| 0.0551 \| \| No log \| 18.0 \| 126 \| 0.5165 \| 0.84 \| 0.2723 \| 1.1596 \| 0.8400 \| 0.8325 \| 0.1942 \| 0.0506 \| \| No log \| 19.0 \| 133 \| 0.4979 \| 0.845 \| 0.2573 \| 1.2329 \| 0.845 \| 0.8297 \| 0.1825 \| 0.0444 \| \| No log \| 20.0 \| 140 \| 0.4953 \| 0.855 \| 0.2565 \| 1.1213 \| 0.855 \| 0.8442 \| 0.1844 \| 0.0474 \| \| No log \| 21.0 \| 147 \| 0.5296 \| 0.82 \| 0.2792 \| 1.0000 \| 0.82 \| 0.8218 \| 0.1768 \| 0.0523 \| \| No log \| 22.0 \| 154 \| 0.5027 \| 0.835 \| 0.2625 \| 0.9926 \| 0.835 \| 0.8238 \| 0.2035 \| 0.0481 \| \| No log \| 23.0 \| 161 \| 0.5027 \| 0.84 \| 0.2642 \| 1.0500 \| 0.8400 \| 0.8299 \| 0.1616 \| 0.0482 \| \| No log \| 24.0 \| 168 \| 0.5017 \| 0.84 \| 0.2616 \| 1.0560 \| 0.8400 \| 0.8314 \| 0.1819 \| 0.0497 \| \| No log \| 25.0 \| 175 \| 0.4942 \| 0.85 \| 0.2594 \| 1.1003 \| 0.85 \| 0.8407 \| 0.1793 \| 0.0483 \| \| No log \| 26.0 \| 182 \| 0.4943 \| 0.83 \| 0.2586 \| 1.0436 \| 0.83 \| 0.8140 \| 0.1869 \| 0.0518 \| \| No log \| 27.0 \| 189 \| 0.4950 \| 0.835 \| 0.2613 \| 1.0817 \| 0.835 \| 0.8224 \| 0.2039 \| 0.0504 \| \| No log \| 28.0 \| 196 \| 0.4957 \| 0.85 \| 0.2599 \| 1.1109 \| 0.85 \| 0.8309 \| 0.2058 \| 0.0485 \| \| No log \| 29.0 \| 203 \| 0.4956 \| 0.845 \| 0.2599 \| 1.0914 \| 0.845 \| 0.8304 \| 0.1916 \| 0.0492 \| \| No log \| 30.0 \| 210 \| 0.4893 \| 0.84 \| 0.2561 \| 1.0890 \| 0.8400 \| 0.8214 \| 0.2071 \| 0.0482 \| \| No log \| 31.0 \| 217 \| 0.4920 \| 0.835 \| 0.2587 \| 1.0907 \| 0.835 \| 0.8270 \| 0.2031 \| 0.0482 \| \| No log \| 32.0 \| 224 \| 0.4927 \| 0.83 \| 0.2601 \| 1.0879 \| 0.83 \| 0.8157 \| 0.2093 \| 0.0500 \| \| No log \| 33.0 \| 231 \| 0.4925 \| 0.835 \| 0.2593 \| 1.0886 \| 0.835 \| 0.8270 \| 0.1810 \| 0.0484 \| \| No log \| 34.0 \| 238 \| 0.4909 \| 0.845 \| 0.2578 \| 1.0871 \| 0.845 \| 0.8304 \| 0.1916 \| 0.0478 \| \| No log \| 35.0 \| 245 \| 0.4927 \| 0.845 \| 0.2591 \| 1.0866 \| 0.845 \| 0.8378 \| 0.1943 \| 0.0473 \| \| No log \| 36.0 \| 252 \| 0.4919 \| 0.85 \| 0.2581 \| 1.0891 \| 0.85 \| 0.8342 \| 0.2193 \| 0.0475 \| \| No log \| 37.0 \| 259 \| 0.4908 \| 0.845 \| 0.2579 \| 1.0867 \| 0.845 \| 0.8346 \| 0.2215 \| 0.0474 \| \| No log \| 38.0 \| 266 \| 0.4929 \| 0.85 \| 0.2590 \| 1.0873 \| 0.85 \| 0.8407 \| 0.1884 \| 0.0471 \| \| No log \| 39.0 \| 273 \| 0.4913 \| 0.85 \| 0.2584 \| 1.0861 \| 0.85 \| 0.8374 \| 0.1944 \| 0.0474 \| \| No log \| 40.0 \| 280 \| 0.4933 \| 0.835 \| 0.2595 \| 1.0871 \| 0.835 \| 0.8248 \| 0.1893 \| 0.0491 \| \| No log \| 41.0 \| 287 \| 0.4936 \| 0.84 \| 0.2599 \| 1.0863 \| 0.8400 \| 0.8276 \| 0.1860 \| 0.0486 \| \| No log \| 42.0 \| 294 \| 0.4911 \| 0.85 \| 0.2580 \| 1.0861 \| 0.85 \| 0.8374 \| 0.2186 \| 0.0474 \| \| No log \| 43.0 \| 301 \| 0.4915 \| 0.85 \| 0.2581 \| 1.0860 \| 0.85 \| 0.8374 \| 0.2023 \| 0.0475 \| \| No log \| 44.0 \| 308 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0874 \| 0.85 \| 0.8374 \| 0.2013 \| 0.0477 \| \| No log \| 45.0 \| 315 \| 0.4915 \| 0.85 \| 0.2583 \| 1.0862 \| 0.85 \| 0.8374 \| 0.1941 \| 0.0475 \| \| No log \| 46.0 \| 322 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0878 \| 0.85 \| 0.8374 \| 0.1852 \| 0.0473 \| \| No log \| 47.0 \| 329 \| 0.4916 \| 0.85 \| 0.2583 \| 1.0873 \| 0.85 \| 0.8374 \| 0.2089 \| 0.0473 \| \| No log \| 48.0 \| 336 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0879 \| 0.85 \| 0.8374 \| 0.2026 \| 0.0477 \| \| No log \| 49.0 \| 343 \| 0.4918 \| 0.845 \| 0.2584 \| 1.0884 \| 0.845 \| 0.8282 \| 0.1963 \| 0.0478 \| \| No log \| 50.0 \| 350 \| 0.4922 \| 0.85 \| 0.2587 \| 1.0871 \| 0.85 \| 0.8374 \| 0.2102 \| 0.0474 \| \| No log \| 51.0 \| 357 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0879 \| 0.85 \| 0.8374 \| 0.2095 \| 0.0474 \| \| No log \| 52.0 \| 364 \| 0.4926 \| 0.85 \| 0.2589 \| 1.0878 \| 0.85 \| 0.8374 \| 0.2022 \| 0.0477 \| \| No log \| 53.0 \| 371 \| 0.4920 \| 0.85 \| 0.2586 \| 1.0888 \| 0.85 \| 0.8374 \| 0.2027 \| 0.0475 \| \| No log \| 54.0 \| 378 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0886 \| 0.85 \| 0.8374 \| 0.2020 \| 0.0474 \| \| No log \| 55.0 \| 385 \| 0.4921 \| 0.85 \| 0.2587 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1929 \| 0.0471 \| \| No log \| 56.0 \| 392 \| 0.4925 \| 0.85 \| 0.2589 \| 1.0881 \| 0.85 \| 0.8374 \| 0.1946 \| 0.0473 \| \| No log \| 57.0 \| 399 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1932 \| 0.0472 \| \| No log \| 58.0 \| 406 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0877 \| 0.85 \| 0.8374 \| 0.1948 \| 0.0476 \| \| No log \| 59.0 \| 413 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1931 \| 0.0472 \| \| No log \| 60.0 \| 420 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0882 \| 0.85 \| 0.8374 \| 0.1945 \| 0.0475 \| \| No log \| 61.0 \| 427 \| 0.4916 \| 0.85 \| 0.2582 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1936 \| 0.0472 \| \| No log \| 62.0 \| 434 \| 0.4920 \| 0.85 \| 0.2586 \| 1.0882 \| 0.85 \| 0.8374 \| 0.1942 \| 0.0473 \| \| No log \| 63.0 \| 441 \| 0.4922 \| 0.85 \| 0.2587 \| 1.0889 \| 0.85 \| 0.8374 \| 0.1935 \| 0.0473 \| \| No log \| 64.0 \| 448 \| 0.4921 \| 0.85 \| 0.2586 \| 1.0885 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0473 \| \| No log \| 65.0 \| 455 \| 0.4916 \| 0.85 \| 0.2582 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0474 \| \| No log \| 66.0 \| 462 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0883 \| 0.85 \| 0.8374 \| 0.1849 \| 0.0472 \| \| No log \| 67.0 \| 469 \| 0.4917 \| 0.85 \| 0.2584 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| No log \| 68.0 \| 476 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0888 \| 0.85 \| 0.8374 \| 0.2011 \| 0.0471 \| \| No log \| 69.0 \| 483 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0889 \| 0.85 \| 0.8374 \| 0.2007 \| 0.0471 \| \| No log \| 70.0 \| 490 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0886 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0474 \| \| No log \| 71.0 \| 497 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0888 \| 0.85 \| 0.8374 \| 0.1940 \| 0.0474 \| \| 0.1824 \| 72.0 \| 504 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0889 \| 0.85 \| 0.8374 \| 0.2011 \| 0.0471 \| \| 0.1824 \| 73.0 \| 511 \| 0.4917 \| 0.85 \| 0.2583 \| 1.0887 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| 0.1824 \| 74.0 \| 518 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1848 \| 0.0472 \| \| 0.1824 \| 75.0 \| 525 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1846 \| 0.0472 \| \| 0.1824 \| 76.0 \| 532 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0889 \| 0.85 \| 0.8374 \| 0.1930 \| 0.0472 \| \| 0.1824 \| 77.0 \| 539 \| 0.4917 \| 0.85 \| 0.2582 \| 1.0891 \| 0.85 \| 0.8374 \| 0.2005 \| 0.0472 \| \| 0.1824 \| 78.0 \| 546 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1928 \| 0.0472 \| \| 0.1824 \| 79.0 \| 553 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1845 \| 0.0473 \| \| 0.1824 \| 80.0 \| 560 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1929 \| 0.0473 \| \| 0.1824 \| 81.0 \| 567 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1925 \| 0.0471 \| \| 0.1824 \| 82.0 \| 574 \| 0.4920 \| 0.85 \| 0.2585 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 83.0 \| 581 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 84.0 \| 588 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0890 \| 0.85 \| 0.8374 \| 0.1926 \| 0.0471 \| \| 0.1824 \| 85.0 \| 595 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 86.0 \| 602 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1927 \| 0.0472 \| \| 0.1824 \| 87.0 \| 609 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 88.0 \| 616 \| 0.4918 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 89.0 \| 623 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0471 \| \| 0.1824 \| 90.0 \| 630 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0892 \| 0.85 \| 0.8374 \| 0.1998 \| 0.0471 \| \| 0.1824 \| 91.0 \| 637 \| 0.4919 \| 0.85 \| 0.2584 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 92.0 \| 644 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 93.0 \| 651 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0893 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0471 \| \| 0.1824 \| 94.0 \| 658 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1844 \| 0.0471 \| \| 0.1824 \| 95.0 \| 665 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 96.0 \| 672 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 97.0 \| 679 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1916 \| 0.0471 \| \| 0.1824 \| 98.0 \| 686 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0895 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 99.0 \| 693 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| \| 0.1824 \| 100.0 \| 700 \| 0.4918 \| 0.85 \| 0.2583 \| 1.0894 \| 0.85 \| 0.8374 \| 0.1917 \| 0.0470 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
umanlp/babelbert-ft-xlm-r	umanlp	2023-07-10T21:57:04Z	160	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	feature-extraction	2023-07-07T21:22:09Z	This model is one of the artifacts of the paper [Massively Multilingual Lexical Specialization of Multilingual Transformers](https://aclanthology.org/2023.acl-long.426/). It was obtained by fine-tuning the representations of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dataset [babelbert-dataset](https://huggingface.co/datasets/umanlp/babelbert-dataset).
BernardOng/Banking-FT-Bong-v1	BernardOng	2023-07-10T21:29:24Z	13	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-05-30T02:19:43Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [h2oai/h2ogpt-oig-oasst1-512-6.9b](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-512-6.9b) - Caution: This is only an experimental model used mainly for research and testing purposes. It is not meant for production use. ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.28.1 pip install accelerate==0.18.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="BernardOng/Banking-FT-Bong-v1", torch_dtype=torch.float16, trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<\|endoftext\|><\|answer\|> ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "BernardOng/Banking-FT-Bong-v1", use_fast=True, padding_side="left" ) model = AutoModelForCausalLM.from_pretrained( "BernardOng/Banking-FT-Bong-v1", torch_dtype=torch.float16, device_map={"": "cuda:0"} ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "BernardOng/Banking-FT-Bong-v1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<\|endoftext\|><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) model = AutoModelForCausalLM.from_pretrained(model_name) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( (embed_in): Embedding(50432, 4096) (layers): ModuleList( (0-31): 32 x GPTNeoXLayer( (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (attention): GPTNeoXAttention( (rotary_emb): RotaryEmbedding() (query_key_value): Linear(in_features=4096, out_features=12288, bias=True) (dense): Linear(in_features=4096, out_features=4096, bias=True) ) (mlp): GPTNeoXMLP( (dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True) (dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True) (act): GELUActivation() ) ) ) (final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) ) (embed_out): Linear(in_features=4096, out_features=50432, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). ```bash CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BernardOng/Banking-FT-Bong-v1 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log ``` ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
vk21/ppo-PyramidRND-unit5	vk21	2023-07-10T21:25:11Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-10T21:25:05Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # 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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-PyramidRND-unit5 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
voyzan/unit1-bonus1-Huggy-A01	voyzan	2023-07-10T21:19:37Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T21:19:36Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: voyzan/unit1-bonus1-Huggy-A01 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE	jordyvl	2023-07-10T21:13:05Z	164	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T20:08:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE 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. --> # vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7723 - Accuracy: 0.6025 - Brier Loss: 0.5295 - Nll: 3.6748 - F1 Micro: 0.6025 - F1 Macro: 0.6055 - Ece: 0.1688 - Aurc: 0.1708 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:-------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 25 \| 4.7870 \| 0.065 \| 0.9655 \| 17.0930 \| 0.065 \| 0.0550 \| 0.1747 \| 0.9357 \| \| No log \| 2.0 \| 50 \| 3.9498 \| 0.205 \| 0.8858 \| 9.5780 \| 0.205 \| 0.1863 \| 0.1692 \| 0.6618 \| \| No log \| 3.0 \| 75 \| 3.3698 \| 0.3675 \| 0.7672 \| 6.4908 \| 0.3675 \| 0.3392 \| 0.1676 \| 0.4195 \| \| No log \| 4.0 \| 100 \| 2.9935 \| 0.4075 \| 0.6958 \| 5.5595 \| 0.4075 \| 0.3820 \| 0.1828 \| 0.3327 \| \| No log \| 5.0 \| 125 \| 2.8351 \| 0.455 \| 0.6591 \| 4.8619 \| 0.455 \| 0.4351 \| 0.1561 \| 0.2833 \| \| No log \| 6.0 \| 150 \| 2.8196 \| 0.4725 \| 0.6595 \| 4.7785 \| 0.4725 \| 0.4367 \| 0.1808 \| 0.2790 \| \| No log \| 7.0 \| 175 \| 2.6352 \| 0.5075 \| 0.6234 \| 4.9881 \| 0.5075 \| 0.4886 \| 0.1563 \| 0.2493 \| \| No log \| 8.0 \| 200 \| 2.5325 \| 0.525 \| 0.6162 \| 4.3297 \| 0.525 \| 0.5026 \| 0.1724 \| 0.2365 \| \| No log \| 9.0 \| 225 \| 2.5459 \| 0.53 \| 0.6099 \| 5.1608 \| 0.53 \| 0.5148 \| 0.1944 \| 0.2350 \| \| No log \| 10.0 \| 250 \| 2.5573 \| 0.5325 \| 0.6161 \| 5.4495 \| 0.5325 \| 0.5212 \| 0.2052 \| 0.2397 \| \| No log \| 11.0 \| 275 \| 2.3199 \| 0.5675 \| 0.5828 \| 4.1247 \| 0.5675 \| 0.5626 \| 0.1849 \| 0.2071 \| \| No log \| 12.0 \| 300 \| 2.2917 \| 0.565 \| 0.5758 \| 4.1738 \| 0.565 \| 0.5694 \| 0.1992 \| 0.2023 \| \| No log \| 13.0 \| 325 \| 2.2744 \| 0.555 \| 0.5974 \| 4.2323 \| 0.555 \| 0.5544 \| 0.1982 \| 0.2203 \| \| No log \| 14.0 \| 350 \| 2.1638 \| 0.5625 \| 0.5807 \| 4.2049 \| 0.5625 \| 0.5629 \| 0.1868 \| 0.2049 \| \| No log \| 15.0 \| 375 \| 2.1934 \| 0.5575 \| 0.5903 \| 4.3813 \| 0.5575 \| 0.5614 \| 0.1868 \| 0.2022 \| \| No log \| 16.0 \| 400 \| 2.1092 \| 0.5625 \| 0.5702 \| 3.6094 \| 0.5625 \| 0.5700 \| 0.1846 \| 0.2011 \| \| No log \| 17.0 \| 425 \| 2.0379 \| 0.5875 \| 0.5642 \| 4.4351 \| 0.5875 \| 0.5822 \| 0.2036 \| 0.1959 \| \| No log \| 18.0 \| 450 \| 2.0303 \| 0.5825 \| 0.5558 \| 3.6847 \| 0.5825 \| 0.5820 \| 0.1684 \| 0.1881 \| \| No log \| 19.0 \| 475 \| 2.0506 \| 0.57 \| 0.5749 \| 4.0014 \| 0.57 \| 0.5708 \| 0.1725 \| 0.2027 \| \| 1.5026 \| 20.0 \| 500 \| 1.9932 \| 0.5875 \| 0.5524 \| 3.8003 \| 0.5875 \| 0.5914 \| 0.1843 \| 0.1831 \| \| 1.5026 \| 21.0 \| 525 \| 2.0131 \| 0.565 \| 0.5643 \| 4.0681 \| 0.565 \| 0.5635 \| 0.1776 \| 0.1957 \| \| 1.5026 \| 22.0 \| 550 \| 2.0162 \| 0.5725 \| 0.5712 \| 3.7068 \| 0.5725 \| 0.5766 \| 0.1934 \| 0.1955 \| \| 1.5026 \| 23.0 \| 575 \| 1.9093 \| 0.605 \| 0.5381 \| 3.7930 \| 0.605 \| 0.6032 \| 0.1539 \| 0.1749 \| \| 1.5026 \| 24.0 \| 600 \| 1.9607 \| 0.575 \| 0.5561 \| 4.5740 \| 0.575 \| 0.5789 \| 0.1782 \| 0.1902 \| \| 1.5026 \| 25.0 \| 625 \| 1.8971 \| 0.5825 \| 0.5408 \| 3.7290 \| 0.5825 \| 0.5754 \| 0.1836 \| 0.1751 \| \| 1.5026 \| 26.0 \| 650 \| 1.9217 \| 0.5775 \| 0.5537 \| 3.8085 \| 0.5775 \| 0.5844 \| 0.1725 \| 0.1843 \| \| 1.5026 \| 27.0 \| 675 \| 1.9493 \| 0.585 \| 0.5606 \| 3.6743 \| 0.585 \| 0.5953 \| 0.1755 \| 0.1882 \| \| 1.5026 \| 28.0 \| 700 \| 1.8884 \| 0.585 \| 0.5437 \| 3.7865 \| 0.585 \| 0.5828 \| 0.1801 \| 0.1822 \| \| 1.5026 \| 29.0 \| 725 \| 1.9242 \| 0.585 \| 0.5479 \| 3.9607 \| 0.585 \| 0.5856 \| 0.1619 \| 0.1817 \| \| 1.5026 \| 30.0 \| 750 \| 1.8767 \| 0.5975 \| 0.5470 \| 3.7995 \| 0.5975 \| 0.5966 \| 0.1599 \| 0.1790 \| \| 1.5026 \| 31.0 \| 775 \| 1.8723 \| 0.5925 \| 0.5337 \| 3.8962 \| 0.5925 \| 0.5972 \| 0.1678 \| 0.1729 \| \| 1.5026 \| 32.0 \| 800 \| 1.9093 \| 0.585 \| 0.5545 \| 3.8776 \| 0.585 \| 0.5830 \| 0.1902 \| 0.1841 \| \| 1.5026 \| 33.0 \| 825 \| 1.8667 \| 0.595 \| 0.5363 \| 3.8926 \| 0.595 \| 0.5917 \| 0.1772 \| 0.1745 \| \| 1.5026 \| 34.0 \| 850 \| 1.8403 \| 0.59 \| 0.5521 \| 3.8560 \| 0.59 \| 0.5953 \| 0.1711 \| 0.1800 \| \| 1.5026 \| 35.0 \| 875 \| 1.8464 \| 0.5925 \| 0.5380 \| 4.0376 \| 0.5925 \| 0.5970 \| 0.1719 \| 0.1756 \| \| 1.5026 \| 36.0 \| 900 \| 1.8441 \| 0.5975 \| 0.5411 \| 3.7193 \| 0.5975 \| 0.6008 \| 0.1569 \| 0.1753 \| \| 1.5026 \| 37.0 \| 925 \| 1.8599 \| 0.5875 \| 0.5402 \| 3.9139 \| 0.5875 \| 0.5908 \| 0.1779 \| 0.1789 \| \| 1.5026 \| 38.0 \| 950 \| 1.8559 \| 0.6 \| 0.5458 \| 3.8970 \| 0.6 \| 0.5991 \| 0.1583 \| 0.1804 \| \| 1.5026 \| 39.0 \| 975 \| 1.8285 \| 0.61 \| 0.5370 \| 3.6292 \| 0.61 \| 0.6155 \| 0.1623 \| 0.1722 \| \| 0.0745 \| 40.0 \| 1000 \| 1.8309 \| 0.5975 \| 0.5432 \| 3.6865 \| 0.5975 \| 0.6017 \| 0.1663 \| 0.1821 \| \| 0.0745 \| 41.0 \| 1025 \| 1.8237 \| 0.59 \| 0.5348 \| 3.6213 \| 0.59 \| 0.5921 \| 0.1695 \| 0.1738 \| \| 0.0745 \| 42.0 \| 1050 \| 1.8421 \| 0.605 \| 0.5360 \| 3.8592 \| 0.605 \| 0.6048 \| 0.1601 \| 0.1743 \| \| 0.0745 \| 43.0 \| 1075 \| 1.8158 \| 0.5975 \| 0.5300 \| 3.4537 \| 0.5975 \| 0.5953 \| 0.1696 \| 0.1707 \| \| 0.0745 \| 44.0 \| 1100 \| 1.8238 \| 0.5875 \| 0.5358 \| 3.7706 \| 0.5875 \| 0.5923 \| 0.1797 \| 0.1754 \| \| 0.0745 \| 45.0 \| 1125 \| 1.8214 \| 0.595 \| 0.5463 \| 3.4742 \| 0.595 \| 0.5981 \| 0.1800 \| 0.1770 \| \| 0.0745 \| 46.0 \| 1150 \| 1.8162 \| 0.5925 \| 0.5317 \| 3.9260 \| 0.5925 \| 0.5950 \| 0.1646 \| 0.1733 \| \| 0.0745 \| 47.0 \| 1175 \| 1.8050 \| 0.5975 \| 0.5392 \| 3.8322 \| 0.5975 \| 0.5979 \| 0.1794 \| 0.1763 \| \| 0.0745 \| 48.0 \| 1200 \| 1.8214 \| 0.5975 \| 0.5347 \| 3.7965 \| 0.5975 \| 0.6009 \| 0.1555 \| 0.1746 \| \| 0.0745 \| 49.0 \| 1225 \| 1.7813 \| 0.6 \| 0.5294 \| 3.8398 \| 0.6 \| 0.6005 \| 0.1674 \| 0.1688 \| \| 0.0745 \| 50.0 \| 1250 \| 1.8179 \| 0.6075 \| 0.5336 \| 3.4690 \| 0.6075 \| 0.6112 \| 0.1743 \| 0.1748 \| \| 0.0745 \| 51.0 \| 1275 \| 1.7953 \| 0.595 \| 0.5380 \| 3.7781 \| 0.595 \| 0.5990 \| 0.1380 \| 0.1727 \| \| 0.0745 \| 52.0 \| 1300 \| 1.7897 \| 0.6 \| 0.5323 \| 3.7412 \| 0.6 \| 0.6013 \| 0.1603 \| 0.1707 \| \| 0.0745 \| 53.0 \| 1325 \| 1.8072 \| 0.59 \| 0.5428 \| 3.5993 \| 0.59 \| 0.5947 \| 0.1571 \| 0.1773 \| \| 0.0745 \| 54.0 \| 1350 \| 1.7834 \| 0.605 \| 0.5219 \| 3.7600 \| 0.605 \| 0.6049 \| 0.1563 \| 0.1671 \| \| 0.0745 \| 55.0 \| 1375 \| 1.7920 \| 0.595 \| 0.5361 \| 3.5986 \| 0.595 \| 0.5978 \| 0.1512 \| 0.1717 \| \| 0.0745 \| 56.0 \| 1400 \| 1.8074 \| 0.5925 \| 0.5387 \| 3.5383 \| 0.5925 \| 0.5962 \| 0.1669 \| 0.1741 \| \| 0.0745 \| 57.0 \| 1425 \| 1.7893 \| 0.605 \| 0.5346 \| 3.6929 \| 0.605 \| 0.6039 \| 0.1641 \| 0.1681 \| \| 0.0745 \| 58.0 \| 1450 \| 1.7787 \| 0.6 \| 0.5317 \| 3.7652 \| 0.6 \| 0.6004 \| 0.1850 \| 0.1726 \| \| 0.0745 \| 59.0 \| 1475 \| 1.7888 \| 0.595 \| 0.5323 \| 3.4558 \| 0.595 \| 0.5975 \| 0.1797 \| 0.1732 \| \| 0.0231 \| 60.0 \| 1500 \| 1.8064 \| 0.58 \| 0.5332 \| 3.7773 \| 0.58 \| 0.5839 \| 0.1819 \| 0.1762 \| \| 0.0231 \| 61.0 \| 1525 \| 1.7795 \| 0.6075 \| 0.5298 \| 3.7998 \| 0.6075 \| 0.6086 \| 0.1678 \| 0.1704 \| \| 0.0231 \| 62.0 \| 1550 \| 1.7826 \| 0.595 \| 0.5318 \| 3.6741 \| 0.595 \| 0.5916 \| 0.1550 \| 0.1715 \| \| 0.0231 \| 63.0 \| 1575 \| 1.7704 \| 0.5925 \| 0.5325 \| 3.5942 \| 0.5925 \| 0.5941 \| 0.1619 \| 0.1712 \| \| 0.0231 \| 64.0 \| 1600 \| 1.7901 \| 0.6025 \| 0.5289 \| 3.4459 \| 0.6025 \| 0.6054 \| 0.2022 \| 0.1712 \| \| 0.0231 \| 65.0 \| 1625 \| 1.7944 \| 0.59 \| 0.5381 \| 3.7591 \| 0.59 \| 0.5910 \| 0.1599 \| 0.1756 \| \| 0.0231 \| 66.0 \| 1650 \| 1.7721 \| 0.605 \| 0.5256 \| 3.5227 \| 0.605 \| 0.6045 \| 0.1525 \| 0.1677 \| \| 0.0231 \| 67.0 \| 1675 \| 1.7779 \| 0.5975 \| 0.5306 \| 3.6792 \| 0.5975 \| 0.5994 \| 0.1667 \| 0.1714 \| \| 0.0231 \| 68.0 \| 1700 \| 1.7724 \| 0.6 \| 0.5250 \| 3.7552 \| 0.6 \| 0.6022 \| 0.1818 \| 0.1683 \| \| 0.0231 \| 69.0 \| 1725 \| 1.7765 \| 0.6025 \| 0.5283 \| 3.4264 \| 0.6025 \| 0.6019 \| 0.1671 \| 0.1700 \| \| 0.0231 \| 70.0 \| 1750 \| 1.7784 \| 0.6 \| 0.5276 \| 3.6887 \| 0.6 \| 0.6053 \| 0.1715 \| 0.1703 \| \| 0.0231 \| 71.0 \| 1775 \| 1.7659 \| 0.6 \| 0.5282 \| 3.6051 \| 0.6 \| 0.6006 \| 0.1722 \| 0.1691 \| \| 0.0231 \| 72.0 \| 1800 \| 1.7882 \| 0.5975 \| 0.5329 \| 3.5950 \| 0.5975 \| 0.6016 \| 0.1981 \| 0.1716 \| \| 0.0231 \| 73.0 \| 1825 \| 1.7678 \| 0.6 \| 0.5287 \| 3.6691 \| 0.6 \| 0.6032 \| 0.1733 \| 0.1696 \| \| 0.0231 \| 74.0 \| 1850 \| 1.7716 \| 0.6 \| 0.5286 \| 3.7576 \| 0.6 \| 0.6013 \| 0.1734 \| 0.1692 \| \| 0.0231 \| 75.0 \| 1875 \| 1.7704 \| 0.6 \| 0.5299 \| 3.5917 \| 0.6 \| 0.6016 \| 0.1645 \| 0.1709 \| \| 0.0231 \| 76.0 \| 1900 \| 1.7729 \| 0.6 \| 0.5298 \| 3.6758 \| 0.6 \| 0.6024 \| 0.1766 \| 0.1710 \| \| 0.0231 \| 77.0 \| 1925 \| 1.7749 \| 0.6 \| 0.5308 \| 3.6022 \| 0.6 \| 0.6030 \| 0.1604 \| 0.1717 \| \| 0.0231 \| 78.0 \| 1950 \| 1.7720 \| 0.6 \| 0.5294 \| 3.6759 \| 0.6 \| 0.6017 \| 0.1786 \| 0.1708 \| \| 0.0231 \| 79.0 \| 1975 \| 1.7734 \| 0.6025 \| 0.5288 \| 3.6765 \| 0.6025 \| 0.6048 \| 0.1673 \| 0.1698 \| \| 0.0059 \| 80.0 \| 2000 \| 1.7709 \| 0.6 \| 0.5286 \| 3.6755 \| 0.6 \| 0.6020 \| 0.1749 \| 0.1704 \| \| 0.0059 \| 81.0 \| 2025 \| 1.7730 \| 0.6 \| 0.5295 \| 3.6760 \| 0.6 \| 0.6020 \| 0.1677 \| 0.1708 \| \| 0.0059 \| 82.0 \| 2050 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6756 \| 0.6025 \| 0.6055 \| 0.1626 \| 0.1708 \| \| 0.0059 \| 83.0 \| 2075 \| 1.7721 \| 0.6025 \| 0.5295 \| 3.6741 \| 0.6025 \| 0.6055 \| 0.1709 \| 0.1708 \| \| 0.0059 \| 84.0 \| 2100 \| 1.7725 \| 0.6025 \| 0.5297 \| 3.6747 \| 0.6025 \| 0.6048 \| 0.1627 \| 0.1709 \| \| 0.0059 \| 85.0 \| 2125 \| 1.7724 \| 0.6025 \| 0.5295 \| 3.6751 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1707 \| \| 0.0059 \| 86.0 \| 2150 \| 1.7724 \| 0.6025 \| 0.5296 \| 3.6751 \| 0.6025 \| 0.6055 \| 0.1630 \| 0.1708 \| \| 0.0059 \| 87.0 \| 2175 \| 1.7724 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1638 \| 0.1707 \| \| 0.0059 \| 88.0 \| 2200 \| 1.7722 \| 0.6025 \| 0.5295 \| 3.6752 \| 0.6025 \| 0.6055 \| 0.1645 \| 0.1708 \| \| 0.0059 \| 89.0 \| 2225 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6747 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1708 \| \| 0.0059 \| 90.0 \| 2250 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6750 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 91.0 \| 2275 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6750 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 92.0 \| 2300 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6747 \| 0.6025 \| 0.6055 \| 0.1639 \| 0.1708 \| \| 0.0059 \| 93.0 \| 2325 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1637 \| 0.1707 \| \| 0.0059 \| 94.0 \| 2350 \| 1.7722 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0059 \| 95.0 \| 2375 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 96.0 \| 2400 \| 1.7723 \| 0.6025 \| 0.5294 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1707 \| \| 0.0059 \| 97.0 \| 2425 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0059 \| 98.0 \| 2450 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1643 \| 0.1708 \| \| 0.0059 \| 99.0 \| 2475 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6749 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| \| 0.0 \| 100.0 \| 2500 \| 1.7723 \| 0.6025 \| 0.5295 \| 3.6748 \| 0.6025 \| 0.6055 \| 0.1688 \| 0.1708 \| ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1
ruggedmug/ppo-Huggy	ruggedmug	2023-07-10T21:08:52Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T21:08:43Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ruggedmug/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Belphegor/ppo-LunarLander-v2	Belphegor	2023-07-10T21:08:44Z	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T21:08:27Z	--- 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: 268.37 +/- 18.85 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 ... ```
skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO	skrl	2023-07-10T21:06:55Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:47:47Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -8.89 +/- 10.3 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltScrew type: IsaacGymEnvs-FactoryTaskNutBoltScrew --- <!-- --- torch: -21.51 +/- 14.99 jax: -35.77 +/- 0.39 numpy: -8.89 +/- 10.3 --- --> # IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FactoryTaskNutBoltScrew - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 128 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 32 # 128 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
WALIDALI/bekinorrev	WALIDALI	2023-07-10T21:00:29Z	5	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T20:57:08Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekinorrev Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
Henk717/chronoboros-33B	Henk717	2023-07-10T20:48:47Z	1,410	9	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-09T21:00:09Z	--- license: other --- This model was the result of a 50/50 average weight merge between Airoboros-33B-1.4 and Chronos-33B. After prolonged testing we concluded that while this merge is highly flexible and capable of many different tasks, it has to much variation in how it answers to be reliable. Because of this the model relies on some luck to get good results, and is therefore not recommended to people seeking a consistent experience, or people sensitive to anticipation based addictions. If you would like an improved version of this model that is more stable check out my Airochronos-33B merge.
voyzan/unit1-lunar_lander_v2-A02	voyzan	2023-07-10T20:47:07Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T20:46:49Z	--- 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: 255.03 +/- 23.01 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 ... ```
jliu596/flappybirdknockoff	jliu596	2023-07-10T20:45:22Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T20:40:49Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: flappybirdknockoff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.40 +/- 11.34 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jordyvl/vit-small_tobacco3482_kd_CEKD_t2.5_a0.9	jordyvl	2023-07-10T20:38:28Z	163	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T19:59:22Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t2.5_a0.9 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. --> # vit-small_tobacco3482_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5446 - Accuracy: 0.85 - Brier Loss: 0.2446 - Nll: 1.0816 - F1 Micro: 0.85 - F1 Macro: 0.8348 - Ece: 0.1474 - Aurc: 0.0436 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 2.1216 \| 0.215 \| 0.8751 \| 5.2864 \| 0.2150 \| 0.1264 \| 0.2697 \| 0.6907 \| \| No log \| 2.0 \| 14 \| 1.7056 \| 0.405 \| 0.7400 \| 3.5721 \| 0.405 \| 0.2275 \| 0.2995 \| 0.4011 \| \| No log \| 3.0 \| 21 \| 1.1857 \| 0.62 \| 0.5612 \| 2.0143 \| 0.62 \| 0.5712 \| 0.2994 \| 0.2024 \| \| No log \| 4.0 \| 28 \| 0.8767 \| 0.705 \| 0.4085 \| 1.6918 \| 0.705 \| 0.6436 \| 0.2231 \| 0.1152 \| \| No log \| 5.0 \| 35 \| 0.8620 \| 0.72 \| 0.3878 \| 1.7931 \| 0.72 \| 0.7294 \| 0.2233 \| 0.1076 \| \| No log \| 6.0 \| 42 \| 0.7517 \| 0.775 \| 0.3252 \| 1.5573 \| 0.775 \| 0.7600 \| 0.1970 \| 0.0790 \| \| No log \| 7.0 \| 49 \| 0.7280 \| 0.79 \| 0.3175 \| 1.5140 \| 0.79 \| 0.7742 \| 0.1903 \| 0.0826 \| \| No log \| 8.0 \| 56 \| 0.6848 \| 0.8 \| 0.2942 \| 1.4438 \| 0.8000 \| 0.7902 \| 0.1828 \| 0.0866 \| \| No log \| 9.0 \| 63 \| 0.6744 \| 0.81 \| 0.2889 \| 1.4703 \| 0.81 \| 0.7969 \| 0.1989 \| 0.0692 \| \| No log \| 10.0 \| 70 \| 0.8432 \| 0.74 \| 0.3859 \| 1.3134 \| 0.74 \| 0.7206 \| 0.1959 \| 0.1051 \| \| No log \| 11.0 \| 77 \| 0.7424 \| 0.765 \| 0.3294 \| 1.5162 \| 0.765 \| 0.7792 \| 0.2005 \| 0.1048 \| \| No log \| 12.0 \| 84 \| 0.6953 \| 0.79 \| 0.3194 \| 1.2233 \| 0.79 \| 0.7850 \| 0.1800 \| 0.0922 \| \| No log \| 13.0 \| 91 \| 0.5703 \| 0.845 \| 0.2538 \| 1.2355 \| 0.845 \| 0.8372 \| 0.1739 \| 0.0447 \| \| No log \| 14.0 \| 98 \| 0.6439 \| 0.795 \| 0.2924 \| 1.2777 \| 0.795 \| 0.7743 \| 0.1771 \| 0.0534 \| \| No log \| 15.0 \| 105 \| 0.5895 \| 0.825 \| 0.2650 \| 1.2086 \| 0.825 \| 0.8071 \| 0.1665 \| 0.0566 \| \| No log \| 16.0 \| 112 \| 0.5973 \| 0.81 \| 0.2753 \| 1.0959 \| 0.81 \| 0.8013 \| 0.1839 \| 0.0534 \| \| No log \| 17.0 \| 119 \| 0.5825 \| 0.795 \| 0.2722 \| 1.1565 \| 0.795 \| 0.7886 \| 0.1855 \| 0.0534 \| \| No log \| 18.0 \| 126 \| 0.5854 \| 0.845 \| 0.2661 \| 1.1223 \| 0.845 \| 0.8424 \| 0.1981 \| 0.0549 \| \| No log \| 19.0 \| 133 \| 0.5514 \| 0.82 \| 0.2553 \| 0.9585 \| 0.82 \| 0.8150 \| 0.1600 \| 0.0481 \| \| No log \| 20.0 \| 140 \| 0.5600 \| 0.835 \| 0.2443 \| 1.2692 \| 0.835 \| 0.8232 \| 0.1657 \| 0.0469 \| \| No log \| 21.0 \| 147 \| 0.5592 \| 0.845 \| 0.2473 \| 1.1658 \| 0.845 \| 0.8331 \| 0.1683 \| 0.0493 \| \| No log \| 22.0 \| 154 \| 0.5507 \| 0.845 \| 0.2411 \| 1.1403 \| 0.845 \| 0.8311 \| 0.1797 \| 0.0450 \| \| No log \| 23.0 \| 161 \| 0.5305 \| 0.84 \| 0.2361 \| 1.1509 \| 0.8400 \| 0.8287 \| 0.1650 \| 0.0409 \| \| No log \| 24.0 \| 168 \| 0.5352 \| 0.835 \| 0.2378 \| 1.2208 \| 0.835 \| 0.8201 \| 0.1515 \| 0.0420 \| \| No log \| 25.0 \| 175 \| 0.5425 \| 0.845 \| 0.2420 \| 1.1208 \| 0.845 \| 0.8321 \| 0.1776 \| 0.0430 \| \| No log \| 26.0 \| 182 \| 0.5396 \| 0.84 \| 0.2409 \| 1.1230 \| 0.8400 \| 0.8286 \| 0.1647 \| 0.0446 \| \| No log \| 27.0 \| 189 \| 0.5436 \| 0.85 \| 0.2401 \| 1.1179 \| 0.85 \| 0.8387 \| 0.1568 \| 0.0427 \| \| No log \| 28.0 \| 196 \| 0.5373 \| 0.835 \| 0.2415 \| 1.1092 \| 0.835 \| 0.8141 \| 0.1641 \| 0.0427 \| \| No log \| 29.0 \| 203 \| 0.5420 \| 0.845 \| 0.2436 \| 1.0988 \| 0.845 \| 0.8326 \| 0.1551 \| 0.0444 \| \| No log \| 30.0 \| 210 \| 0.5413 \| 0.845 \| 0.2420 \| 1.1064 \| 0.845 \| 0.8312 \| 0.1486 \| 0.0440 \| \| No log \| 31.0 \| 217 \| 0.5411 \| 0.84 \| 0.2418 \| 1.1024 \| 0.8400 \| 0.8286 \| 0.1565 \| 0.0435 \| \| No log \| 32.0 \| 224 \| 0.5426 \| 0.845 \| 0.2429 \| 1.0993 \| 0.845 \| 0.8322 \| 0.1631 \| 0.0433 \| \| No log \| 33.0 \| 231 \| 0.5424 \| 0.85 \| 0.2426 \| 1.0989 \| 0.85 \| 0.8348 \| 0.1615 \| 0.0436 \| \| No log \| 34.0 \| 238 \| 0.5406 \| 0.84 \| 0.2419 \| 1.0979 \| 0.8400 \| 0.8251 \| 0.1640 \| 0.0440 \| \| No log \| 35.0 \| 245 \| 0.5438 \| 0.85 \| 0.2436 \| 1.0953 \| 0.85 \| 0.8348 \| 0.1595 \| 0.0438 \| \| No log \| 36.0 \| 252 \| 0.5429 \| 0.85 \| 0.2429 \| 1.0970 \| 0.85 \| 0.8348 \| 0.1495 \| 0.0433 \| \| No log \| 37.0 \| 259 \| 0.5431 \| 0.85 \| 0.2427 \| 1.0951 \| 0.85 \| 0.8348 \| 0.1617 \| 0.0435 \| \| No log \| 38.0 \| 266 \| 0.5424 \| 0.85 \| 0.2426 \| 1.0959 \| 0.85 \| 0.8348 \| 0.1587 \| 0.0434 \| \| No log \| 39.0 \| 273 \| 0.5428 \| 0.85 \| 0.2432 \| 1.0924 \| 0.85 \| 0.8348 \| 0.1512 \| 0.0433 \| \| No log \| 40.0 \| 280 \| 0.5437 \| 0.85 \| 0.2438 \| 1.0911 \| 0.85 \| 0.8348 \| 0.1726 \| 0.0438 \| \| No log \| 41.0 \| 287 \| 0.5438 \| 0.85 \| 0.2434 \| 1.0925 \| 0.85 \| 0.8348 \| 0.1704 \| 0.0433 \| \| No log \| 42.0 \| 294 \| 0.5428 \| 0.85 \| 0.2432 \| 1.0927 \| 0.85 \| 0.8348 \| 0.1585 \| 0.0436 \| \| No log \| 43.0 \| 301 \| 0.5455 \| 0.85 \| 0.2443 \| 1.0907 \| 0.85 \| 0.8348 \| 0.1756 \| 0.0437 \| \| No log \| 44.0 \| 308 \| 0.5427 \| 0.85 \| 0.2433 \| 1.0908 \| 0.85 \| 0.8348 \| 0.1616 \| 0.0433 \| \| No log \| 45.0 \| 315 \| 0.5456 \| 0.85 \| 0.2446 \| 1.0878 \| 0.85 \| 0.8348 \| 0.1767 \| 0.0437 \| \| No log \| 46.0 \| 322 \| 0.5439 \| 0.85 \| 0.2438 \| 1.0895 \| 0.85 \| 0.8348 \| 0.1503 \| 0.0435 \| \| No log \| 47.0 \| 329 \| 0.5448 \| 0.85 \| 0.2443 \| 1.0891 \| 0.85 \| 0.8348 \| 0.1674 \| 0.0439 \| \| No log \| 48.0 \| 336 \| 0.5440 \| 0.85 \| 0.2437 \| 1.0898 \| 0.85 \| 0.8348 \| 0.1768 \| 0.0437 \| \| No log \| 49.0 \| 343 \| 0.5443 \| 0.85 \| 0.2441 \| 1.0883 \| 0.85 \| 0.8348 \| 0.1433 \| 0.0432 \| \| No log \| 50.0 \| 350 \| 0.5449 \| 0.85 \| 0.2444 \| 1.0877 \| 0.85 \| 0.8348 \| 0.1722 \| 0.0436 \| \| No log \| 51.0 \| 357 \| 0.5443 \| 0.85 \| 0.2442 \| 1.0871 \| 0.85 \| 0.8348 \| 0.1606 \| 0.0434 \| \| No log \| 52.0 \| 364 \| 0.5453 \| 0.85 \| 0.2444 \| 1.0865 \| 0.85 \| 0.8348 \| 0.1729 \| 0.0436 \| \| No log \| 53.0 \| 371 \| 0.5433 \| 0.845 \| 0.2438 \| 1.0873 \| 0.845 \| 0.8287 \| 0.1570 \| 0.0434 \| \| No log \| 54.0 \| 378 \| 0.5453 \| 0.85 \| 0.2447 \| 1.0854 \| 0.85 \| 0.8348 \| 0.1606 \| 0.0435 \| \| No log \| 55.0 \| 385 \| 0.5438 \| 0.85 \| 0.2439 \| 1.0868 \| 0.85 \| 0.8348 \| 0.1721 \| 0.0434 \| \| No log \| 56.0 \| 392 \| 0.5455 \| 0.85 \| 0.2447 \| 1.0853 \| 0.85 \| 0.8348 \| 0.1710 \| 0.0437 \| \| No log \| 57.0 \| 399 \| 0.5435 \| 0.85 \| 0.2439 \| 1.0864 \| 0.85 \| 0.8348 \| 0.1540 \| 0.0434 \| \| No log \| 58.0 \| 406 \| 0.5451 \| 0.85 \| 0.2447 \| 1.0844 \| 0.85 \| 0.8348 \| 0.1636 \| 0.0436 \| \| No log \| 59.0 \| 413 \| 0.5442 \| 0.85 \| 0.2441 \| 1.0858 \| 0.85 \| 0.8348 \| 0.1556 \| 0.0435 \| \| No log \| 60.0 \| 420 \| 0.5453 \| 0.85 \| 0.2447 \| 1.0843 \| 0.85 \| 0.8348 \| 0.1717 \| 0.0437 \| \| No log \| 61.0 \| 427 \| 0.5439 \| 0.85 \| 0.2442 \| 1.0847 \| 0.85 \| 0.8348 \| 0.1541 \| 0.0432 \| \| No log \| 62.0 \| 434 \| 0.5455 \| 0.85 \| 0.2449 \| 1.0839 \| 0.85 \| 0.8348 \| 0.1550 \| 0.0435 \| \| No log \| 63.0 \| 441 \| 0.5446 \| 0.85 \| 0.2445 \| 1.0843 \| 0.85 \| 0.8348 \| 0.1553 \| 0.0435 \| \| No log \| 64.0 \| 448 \| 0.5448 \| 0.85 \| 0.2446 \| 1.0833 \| 0.85 \| 0.8348 \| 0.1634 \| 0.0435 \| \| No log \| 65.0 \| 455 \| 0.5443 \| 0.85 \| 0.2443 \| 1.0847 \| 0.85 \| 0.8348 \| 0.1554 \| 0.0435 \| \| No log \| 66.0 \| 462 \| 0.5448 \| 0.85 \| 0.2447 \| 1.0831 \| 0.85 \| 0.8348 \| 0.1547 \| 0.0436 \| \| No log \| 67.0 \| 469 \| 0.5452 \| 0.85 \| 0.2448 \| 1.0828 \| 0.85 \| 0.8348 \| 0.1563 \| 0.0436 \| \| No log \| 68.0 \| 476 \| 0.5443 \| 0.85 \| 0.2444 \| 1.0834 \| 0.85 \| 0.8348 \| 0.1472 \| 0.0434 \| \| No log \| 69.0 \| 483 \| 0.5447 \| 0.85 \| 0.2445 \| 1.0832 \| 0.85 \| 0.8348 \| 0.1632 \| 0.0434 \| \| No log \| 70.0 \| 490 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0831 \| 0.85 \| 0.8348 \| 0.1559 \| 0.0435 \| \| No log \| 71.0 \| 497 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0829 \| 0.85 \| 0.8348 \| 0.1473 \| 0.0435 \| \| 0.1823 \| 72.0 \| 504 \| 0.5443 \| 0.85 \| 0.2444 \| 1.0828 \| 0.85 \| 0.8348 \| 0.1559 \| 0.0434 \| \| 0.1823 \| 73.0 \| 511 \| 0.5447 \| 0.85 \| 0.2447 \| 1.0825 \| 0.85 \| 0.8348 \| 0.1472 \| 0.0434 \| \| 0.1823 \| 74.0 \| 518 \| 0.5444 \| 0.85 \| 0.2444 \| 1.0829 \| 0.85 \| 0.8348 \| 0.1559 \| 0.0436 \| \| 0.1823 \| 75.0 \| 525 \| 0.5446 \| 0.85 \| 0.2445 \| 1.0829 \| 0.85 \| 0.8348 \| 0.1557 \| 0.0435 \| \| 0.1823 \| 76.0 \| 532 \| 0.5448 \| 0.85 \| 0.2445 \| 1.0825 \| 0.85 \| 0.8348 \| 0.1559 \| 0.0435 \| \| 0.1823 \| 77.0 \| 539 \| 0.5443 \| 0.85 \| 0.2444 \| 1.0827 \| 0.85 \| 0.8348 \| 0.1558 \| 0.0435 \| \| 0.1823 \| 78.0 \| 546 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0824 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0436 \| \| 0.1823 \| 79.0 \| 553 \| 0.5450 \| 0.85 \| 0.2448 \| 1.0821 \| 0.85 \| 0.8348 \| 0.1637 \| 0.0436 \| \| 0.1823 \| 80.0 \| 560 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0823 \| 0.85 \| 0.8348 \| 0.1638 \| 0.0436 \| \| 0.1823 \| 81.0 \| 567 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0820 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0435 \| \| 0.1823 \| 82.0 \| 574 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0819 \| 0.85 \| 0.8348 \| 0.1561 \| 0.0435 \| \| 0.1823 \| 83.0 \| 581 \| 0.5448 \| 0.85 \| 0.2446 \| 1.0822 \| 0.85 \| 0.8348 \| 0.1550 \| 0.0436 \| \| 0.1823 \| 84.0 \| 588 \| 0.5445 \| 0.85 \| 0.2446 \| 1.0819 \| 0.85 \| 0.8348 \| 0.1551 \| 0.0435 \| \| 0.1823 \| 85.0 \| 595 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0818 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0436 \| \| 0.1823 \| 86.0 \| 602 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0818 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0435 \| \| 0.1823 \| 87.0 \| 609 \| 0.5448 \| 0.85 \| 0.2447 \| 1.0820 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0435 \| \| 0.1823 \| 88.0 \| 616 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0819 \| 0.85 \| 0.8348 \| 0.1551 \| 0.0435 \| \| 0.1823 \| 89.0 \| 623 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0819 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0435 \| \| 0.1823 \| 90.0 \| 630 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1638 \| 0.0436 \| \| 0.1823 \| 91.0 \| 637 \| 0.5446 \| 0.85 \| 0.2445 \| 1.0817 \| 0.85 \| 0.8348 \| 0.1474 \| 0.0435 \| \| 0.1823 \| 92.0 \| 644 \| 0.5445 \| 0.85 \| 0.2445 \| 1.0818 \| 0.85 \| 0.8348 \| 0.1551 \| 0.0436 \| \| 0.1823 \| 93.0 \| 651 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0818 \| 0.85 \| 0.8348 \| 0.1560 \| 0.0436 \| \| 0.1823 \| 94.0 \| 658 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1561 \| 0.0436 \| \| 0.1823 \| 95.0 \| 665 \| 0.5447 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1550 \| 0.0435 \| \| 0.1823 \| 96.0 \| 672 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1474 \| 0.0436 \| \| 0.1823 \| 97.0 \| 679 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0817 \| 0.85 \| 0.8348 \| 0.1551 \| 0.0436 \| \| 0.1823 \| 98.0 \| 686 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0817 \| 0.85 \| 0.8348 \| 0.1474 \| 0.0436 \| \| 0.1823 \| 99.0 \| 693 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1474 \| 0.0436 \| \| 0.1823 \| 100.0 \| 700 \| 0.5446 \| 0.85 \| 0.2446 \| 1.0816 \| 0.85 \| 0.8348 \| 0.1474 \| 0.0436 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
vk21/ppo-SnowballTarget-unit5	vk21	2023-07-10T20:34:28Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-10T20:34:22Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-SnowballTarget-unit5 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DarkAirforce/dqn-SpaceInvadersNoFrameskip-v4	DarkAirforce	2023-07-10T20:33:23Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T19:24:04Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 534.00 +/- 175.24 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DarkAirforce -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DarkAirforce -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DarkAirforce ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO	skrl	2023-07-10T20:15:49Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T19:47:18Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -38.54 +/- 17.49 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPlace type: IsaacGymEnvs-FactoryTaskNutBoltPlace --- <!-- --- torch: -38.54 +/- 17.49 jax: -60.9 +/- 0.84 numpy: -58.9 +/- 1.8 --- --> # IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FactoryTaskNutBoltPlace - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2	ALM-AHME	2023-07-10T20:09:00Z	213	0	transformers	[ "transformers", "pytorch", "convnextv2", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T20:08:11Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2 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. --> # convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2 This model is a fine-tuned version of [ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20](https://huggingface.co/ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20) 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ruggedmug/ppo-LunarLander-v2	ruggedmug	2023-07-10T20:06:30Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-07T20:09:57Z	--- 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: 280.76 +/- 15.33 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 ... ```
BigSalmon/InformalToFormalLincoln103Paraphrase	BigSalmon	2023-07-10T19:36:48Z	209	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-08T22:41:06Z	data: https://github.com/BigSalmon2/InformalToFormalDataset Text Generation Informal Formal ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase") ``` ``` 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. ``` ``` 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. ``` ``` 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. ``` ``` ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions * ideas: i know this one guy who retired so young, attesting to how careful they were with money. related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion ``` ``` less specific: actors and musicians should ( support democracy ). clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ). * less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ). clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ). ``` ``` dull: clean emotional heft: spotless, immaculate, pristine * dull: hot emotional heft: scorching, searing, blistering * dull: happy emotional heft: euphoric ``` ``` text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ). ``` ``` system: i'm not sure i agree with your assessment of the situation. user: replace "assessment" with a more casual term. chatbot: ( take / read / view ). system: i've been feeling really busy lately, trying to balance work and family responsibilities. user: replace "busy" with a more intense word that conveys it is too much. chatbot: overwhelmed. system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected. user: replace "nebulous" with a word that conveys a lack of detail. chatbot: ( vague / ambiguous ). ``` ``` user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression] chatbot: intellectual horizons. user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”? chatbot: academic enlightenment. ``` ``` key: calculate. syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute. ant: guess, conjecture, hit, chance, risk, stake, miscalculate. ``` ``` description: more forceful version of curious that is less forceful than nosy answer: inquisitive description: more forceful version of hopeful that is less forceful than overconfident answer: optimistic ``` ``` key: inquisitive positive: curious, interested negative: nosy, prying * key: witty positive: clever, humorous negative: sarcastic, caustic *** key: influential positive: impactful, powerful negative: overbearing, domineering ``` ``` defective: the blogger's { use of language imprecise } confused an already complicated issue. precise: the blogger's ( vague wording ) confused an already complicated issue. defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals. precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals. ``` ``` example: the new car uses gas. boring: uses stronger: guzzles example: he hates people that are rude. boring: hates stronger: loathes, abhors, despises, scorns, detests ``` ``` initial: The music at the party was [ loud; replace with a word that suggests a more uncomfortable noise level ] and overwhelming. modified: The music at the party was { ear-splitting } and overwhelming. initial: their house is [ small; replace with a positive spin ]. modified: their house is { cozy }. ``` ``` defective: they spent the weekend enjoying { time do what you want }. precise: they spent the weekend enjoying ( leisure activities). defective: the author rightly notes the inequities perpetuated by { employment based on who you know }. precise: the author rightly notes the inequities perpetuated by ( nepotism ). defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals. precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals. ``` ``` persona: human resources manager buzzwords: pipeline, talent, retention, compensation, flexible, recruitment, personnel, resume, competitive, quality, onboard ```
NasimB/gpt2-cocnat-aochildes-mod-sub-length-10k	NasimB	2023-07-10T19:27:45Z	16	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T17:32:01Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-aochildes-mod-sub-length-10k 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. --> # gpt2-cocnat-aochildes-mod-sub-length-10k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3425 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6933 \| 0.29 \| 500 \| 5.6341 \| \| 5.3469 \| 0.59 \| 1000 \| 5.1996 \| \| 4.9864 \| 0.88 \| 1500 \| 4.9580 \| \| 4.7189 \| 1.18 \| 2000 \| 4.8083 \| \| 4.5609 \| 1.47 \| 2500 \| 4.6850 \| \| 4.4523 \| 1.77 \| 3000 \| 4.5821 \| \| 4.317 \| 2.06 \| 3500 \| 4.5146 \| \| 4.1329 \| 2.35 \| 4000 \| 4.4652 \| \| 4.1086 \| 2.65 \| 4500 \| 4.4071 \| \| 4.0635 \| 2.94 \| 5000 \| 4.3601 \| \| 3.8482 \| 3.24 \| 5500 \| 4.3553 \| \| 3.8055 \| 3.53 \| 6000 \| 4.3282 \| \| 3.7859 \| 3.83 \| 6500 \| 4.2926 \| \| 3.6619 \| 4.12 \| 7000 \| 4.2970 \| \| 3.5196 \| 4.41 \| 7500 \| 4.2933 \| \| 3.5139 \| 4.71 \| 8000 \| 4.2857 \| \| 3.4905 \| 5.0 \| 8500 \| 4.2710 \| \| 3.3203 \| 5.3 \| 9000 \| 4.2871 \| \| 3.322 \| 5.59 \| 9500 \| 4.2867 \| \| 3.3172 \| 5.89 \| 10000 \| 4.2863 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
grace-pro/afriberta-small-finetuned-hausa	grace-pro	2023-07-10T19:21:16Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T18:52:05Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-small-finetuned-hausa 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. --> # afriberta-small-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1444 - Precision: 0.6873 - Recall: 0.4713 - F1: 0.5592 - Accuracy: 0.9618 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1493 \| 1.0 \| 2624 \| 0.1382 \| 0.6423 \| 0.3968 \| 0.4905 \| 0.9572 \| \| 0.1259 \| 2.0 \| 5248 \| 0.1319 \| 0.6734 \| 0.4415 \| 0.5333 \| 0.9603 \| \| 0.106 \| 3.0 \| 7872 \| 0.1385 \| 0.6908 \| 0.4502 \| 0.5452 \| 0.9611 \| \| 0.0949 \| 4.0 \| 10496 \| 0.1377 \| 0.6752 \| 0.4759 \| 0.5583 \| 0.9613 \| \| 0.086 \| 5.0 \| 13120 \| 0.1444 \| 0.6873 \| 0.4713 \| 0.5592 \| 0.9618 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jordyvl/vit-small_tobacco3482_kd_CEKD_t2.5_a0.5	jordyvl	2023-07-10T19:16:42Z	166	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T18:37:44Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t2.5_a0.5 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. --> # vit-small_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4300 - Accuracy: 0.83 - Brier Loss: 0.2807 - Nll: 1.0350 - F1 Micro: 0.83 - F1 Macro: 0.8295 - Ece: 0.2287 - Aurc: 0.0560 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.6525 \| 0.225 \| 0.8757 \| 5.3231 \| 0.225 \| 0.1387 \| 0.2689 \| 0.6977 \| \| No log \| 2.0 \| 14 \| 1.3106 \| 0.405 \| 0.7470 \| 3.3487 \| 0.405 \| 0.2195 \| 0.2936 \| 0.4032 \| \| No log \| 3.0 \| 21 \| 0.9127 \| 0.585 \| 0.5785 \| 1.8686 \| 0.585 \| 0.5142 \| 0.2974 \| 0.2067 \| \| No log \| 4.0 \| 28 \| 0.7280 \| 0.715 \| 0.4339 \| 1.6780 \| 0.715 \| 0.6761 \| 0.2672 \| 0.1204 \| \| No log \| 5.0 \| 35 \| 0.6523 \| 0.775 \| 0.3676 \| 1.6537 \| 0.775 \| 0.7619 \| 0.2554 \| 0.0929 \| \| No log \| 6.0 \| 42 \| 0.5888 \| 0.785 \| 0.3502 \| 1.3926 \| 0.785 \| 0.7538 \| 0.2277 \| 0.0908 \| \| No log \| 7.0 \| 49 \| 0.6113 \| 0.805 \| 0.3326 \| 1.7118 \| 0.805 \| 0.7903 \| 0.2428 \| 0.0803 \| \| No log \| 8.0 \| 56 \| 0.5404 \| 0.785 \| 0.3178 \| 1.1557 \| 0.785 \| 0.7671 \| 0.2183 \| 0.0716 \| \| No log \| 9.0 \| 63 \| 0.5380 \| 0.82 \| 0.3051 \| 1.3231 \| 0.82 \| 0.8072 \| 0.2168 \| 0.0773 \| \| No log \| 10.0 \| 70 \| 0.6035 \| 0.775 \| 0.3508 \| 1.3888 \| 0.775 \| 0.7682 \| 0.2191 \| 0.0812 \| \| No log \| 11.0 \| 77 \| 0.5473 \| 0.795 \| 0.3202 \| 1.2622 \| 0.795 \| 0.7740 \| 0.2303 \| 0.0626 \| \| No log \| 12.0 \| 84 \| 0.4860 \| 0.825 \| 0.2937 \| 1.3575 \| 0.825 \| 0.8053 \| 0.2392 \| 0.0727 \| \| No log \| 13.0 \| 91 \| 0.5046 \| 0.81 \| 0.3032 \| 1.1857 \| 0.81 \| 0.8086 \| 0.2248 \| 0.0564 \| \| No log \| 14.0 \| 98 \| 0.4745 \| 0.825 \| 0.2870 \| 1.2338 \| 0.825 \| 0.8089 \| 0.2441 \| 0.0518 \| \| No log \| 15.0 \| 105 \| 0.4764 \| 0.81 \| 0.2943 \| 1.0325 \| 0.81 \| 0.8110 \| 0.1935 \| 0.0556 \| \| No log \| 16.0 \| 112 \| 0.4918 \| 0.81 \| 0.3062 \| 1.0551 \| 0.81 \| 0.8015 \| 0.2198 \| 0.0587 \| \| No log \| 17.0 \| 119 \| 0.4757 \| 0.815 \| 0.2970 \| 1.4203 \| 0.815 \| 0.7965 \| 0.2263 \| 0.0850 \| \| No log \| 18.0 \| 126 \| 0.4586 \| 0.825 \| 0.2926 \| 1.0361 \| 0.825 \| 0.8268 \| 0.2279 \| 0.0583 \| \| No log \| 19.0 \| 133 \| 0.4503 \| 0.835 \| 0.2855 \| 1.1476 \| 0.835 \| 0.8301 \| 0.2392 \| 0.0589 \| \| No log \| 20.0 \| 140 \| 0.4780 \| 0.805 \| 0.3105 \| 0.9928 \| 0.805 \| 0.7902 \| 0.1988 \| 0.0775 \| \| No log \| 21.0 \| 147 \| 0.4965 \| 0.8 \| 0.3205 \| 1.1887 \| 0.8000 \| 0.8029 \| 0.2410 \| 0.0702 \| \| No log \| 22.0 \| 154 \| 0.4753 \| 0.815 \| 0.3016 \| 0.9609 \| 0.815 \| 0.8169 \| 0.2163 \| 0.0580 \| \| No log \| 23.0 \| 161 \| 0.4733 \| 0.8 \| 0.3074 \| 1.2566 \| 0.8000 \| 0.8001 \| 0.2162 \| 0.0704 \| \| No log \| 24.0 \| 168 \| 0.4472 \| 0.815 \| 0.2888 \| 1.0352 \| 0.815 \| 0.8187 \| 0.2317 \| 0.0590 \| \| No log \| 25.0 \| 175 \| 0.4434 \| 0.815 \| 0.2854 \| 0.9874 \| 0.815 \| 0.8186 \| 0.2149 \| 0.0554 \| \| No log \| 26.0 \| 182 \| 0.4316 \| 0.82 \| 0.2754 \| 1.0477 \| 0.82 \| 0.8267 \| 0.2195 \| 0.0508 \| \| No log \| 27.0 \| 189 \| 0.4276 \| 0.83 \| 0.2751 \| 1.1016 \| 0.83 \| 0.8336 \| 0.2050 \| 0.0525 \| \| No log \| 28.0 \| 196 \| 0.4329 \| 0.82 \| 0.2795 \| 1.0537 \| 0.82 \| 0.8220 \| 0.2158 \| 0.0611 \| \| No log \| 29.0 \| 203 \| 0.4327 \| 0.82 \| 0.2827 \| 1.1766 \| 0.82 \| 0.8237 \| 0.2024 \| 0.0603 \| \| No log \| 30.0 \| 210 \| 0.4317 \| 0.82 \| 0.2820 \| 1.0331 \| 0.82 \| 0.8219 \| 0.2083 \| 0.0611 \| \| No log \| 31.0 \| 217 \| 0.4316 \| 0.82 \| 0.2803 \| 1.0974 \| 0.82 \| 0.8263 \| 0.1984 \| 0.0575 \| \| No log \| 32.0 \| 224 \| 0.4340 \| 0.82 \| 0.2833 \| 1.0384 \| 0.82 \| 0.8240 \| 0.2202 \| 0.0590 \| \| No log \| 33.0 \| 231 \| 0.4333 \| 0.81 \| 0.2824 \| 1.0355 \| 0.81 \| 0.8160 \| 0.2103 \| 0.0586 \| \| No log \| 34.0 \| 238 \| 0.4309 \| 0.83 \| 0.2817 \| 1.1015 \| 0.83 \| 0.8307 \| 0.2107 \| 0.0577 \| \| No log \| 35.0 \| 245 \| 0.4321 \| 0.82 \| 0.2817 \| 1.0359 \| 0.82 \| 0.8229 \| 0.2147 \| 0.0590 \| \| No log \| 36.0 \| 252 \| 0.4304 \| 0.825 \| 0.2802 \| 1.1016 \| 0.825 \| 0.8257 \| 0.2137 \| 0.0569 \| \| No log \| 37.0 \| 259 \| 0.4303 \| 0.825 \| 0.2811 \| 1.0990 \| 0.825 \| 0.8268 \| 0.2149 \| 0.0581 \| \| No log \| 38.0 \| 266 \| 0.4314 \| 0.825 \| 0.2814 \| 1.1003 \| 0.825 \| 0.8257 \| 0.2163 \| 0.0581 \| \| No log \| 39.0 \| 273 \| 0.4302 \| 0.82 \| 0.2806 \| 1.1007 \| 0.82 \| 0.8226 \| 0.2102 \| 0.0576 \| \| No log \| 40.0 \| 280 \| 0.4307 \| 0.825 \| 0.2809 \| 1.0376 \| 0.825 \| 0.8264 \| 0.2049 \| 0.0573 \| \| No log \| 41.0 \| 287 \| 0.4303 \| 0.82 \| 0.2808 \| 1.0434 \| 0.82 \| 0.8226 \| 0.2096 \| 0.0574 \| \| No log \| 42.0 \| 294 \| 0.4310 \| 0.825 \| 0.2817 \| 1.0376 \| 0.825 \| 0.8268 \| 0.2140 \| 0.0580 \| \| No log \| 43.0 \| 301 \| 0.4310 \| 0.825 \| 0.2813 \| 1.0391 \| 0.825 \| 0.8257 \| 0.2147 \| 0.0580 \| \| No log \| 44.0 \| 308 \| 0.4301 \| 0.825 \| 0.2808 \| 1.0389 \| 0.825 \| 0.8257 \| 0.2064 \| 0.0573 \| \| No log \| 45.0 \| 315 \| 0.4305 \| 0.83 \| 0.2811 \| 1.0419 \| 0.83 \| 0.8307 \| 0.2300 \| 0.0577 \| \| No log \| 46.0 \| 322 \| 0.4303 \| 0.82 \| 0.2808 \| 1.0423 \| 0.82 \| 0.8226 \| 0.2197 \| 0.0582 \| \| No log \| 47.0 \| 329 \| 0.4304 \| 0.825 \| 0.2811 \| 1.0405 \| 0.825 \| 0.8257 \| 0.2240 \| 0.0580 \| \| No log \| 48.0 \| 336 \| 0.4300 \| 0.82 \| 0.2805 \| 1.0407 \| 0.82 \| 0.8226 \| 0.2105 \| 0.0574 \| \| No log \| 49.0 \| 343 \| 0.4307 \| 0.825 \| 0.2812 \| 1.0381 \| 0.825 \| 0.8257 \| 0.2252 \| 0.0577 \| \| No log \| 50.0 \| 350 \| 0.4304 \| 0.82 \| 0.2810 \| 1.0422 \| 0.82 \| 0.8226 \| 0.2353 \| 0.0578 \| \| No log \| 51.0 \| 357 \| 0.4310 \| 0.825 \| 0.2813 \| 1.0382 \| 0.825 \| 0.8264 \| 0.2153 \| 0.0569 \| \| No log \| 52.0 \| 364 \| 0.4309 \| 0.82 \| 0.2814 \| 1.0380 \| 0.82 \| 0.8226 \| 0.2282 \| 0.0574 \| \| No log \| 53.0 \| 371 \| 0.4307 \| 0.825 \| 0.2813 \| 1.0357 \| 0.825 \| 0.8264 \| 0.2250 \| 0.0568 \| \| No log \| 54.0 \| 378 \| 0.4305 \| 0.82 \| 0.2810 \| 1.0366 \| 0.82 \| 0.8226 \| 0.2284 \| 0.0575 \| \| No log \| 55.0 \| 385 \| 0.4304 \| 0.825 \| 0.2811 \| 1.0351 \| 0.825 \| 0.8264 \| 0.2241 \| 0.0566 \| \| No log \| 56.0 \| 392 \| 0.4308 \| 0.825 \| 0.2813 \| 1.0369 \| 0.825 \| 0.8257 \| 0.2414 \| 0.0572 \| \| No log \| 57.0 \| 399 \| 0.4305 \| 0.825 \| 0.2810 \| 1.0356 \| 0.825 \| 0.8257 \| 0.2322 \| 0.0571 \| \| No log \| 58.0 \| 406 \| 0.4302 \| 0.82 \| 0.2808 \| 1.0359 \| 0.82 \| 0.8226 \| 0.2368 \| 0.0569 \| \| No log \| 59.0 \| 413 \| 0.4302 \| 0.82 \| 0.2809 \| 1.0346 \| 0.82 \| 0.8226 \| 0.2271 \| 0.0569 \| \| No log \| 60.0 \| 420 \| 0.4303 \| 0.82 \| 0.2809 \| 1.0357 \| 0.82 \| 0.8226 \| 0.2272 \| 0.0570 \| \| No log \| 61.0 \| 427 \| 0.4304 \| 0.825 \| 0.2810 \| 1.0360 \| 0.825 \| 0.8257 \| 0.2325 \| 0.0569 \| \| No log \| 62.0 \| 434 \| 0.4303 \| 0.825 \| 0.2809 \| 1.0360 \| 0.825 \| 0.8257 \| 0.2321 \| 0.0568 \| \| No log \| 63.0 \| 441 \| 0.4303 \| 0.83 \| 0.2809 \| 1.0356 \| 0.83 \| 0.8295 \| 0.2300 \| 0.0562 \| \| No log \| 64.0 \| 448 \| 0.4304 \| 0.825 \| 0.2810 \| 1.0347 \| 0.825 \| 0.8264 \| 0.2242 \| 0.0564 \| \| No log \| 65.0 \| 455 \| 0.4301 \| 0.83 \| 0.2808 \| 1.0361 \| 0.83 \| 0.8295 \| 0.2384 \| 0.0564 \| \| No log \| 66.0 \| 462 \| 0.4303 \| 0.83 \| 0.2810 \| 1.0359 \| 0.83 \| 0.8295 \| 0.2293 \| 0.0563 \| \| No log \| 67.0 \| 469 \| 0.4302 \| 0.83 \| 0.2809 \| 1.0360 \| 0.83 \| 0.8295 \| 0.2386 \| 0.0564 \| \| No log \| 68.0 \| 476 \| 0.4304 \| 0.83 \| 0.2810 \| 1.0360 \| 0.83 \| 0.8295 \| 0.2384 \| 0.0563 \| \| No log \| 69.0 \| 483 \| 0.4305 \| 0.83 \| 0.2812 \| 1.0355 \| 0.83 \| 0.8295 \| 0.2295 \| 0.0564 \| \| No log \| 70.0 \| 490 \| 0.4302 \| 0.825 \| 0.2808 \| 1.0354 \| 0.825 \| 0.8264 \| 0.2239 \| 0.0561 \| \| No log \| 71.0 \| 497 \| 0.4305 \| 0.83 \| 0.2812 \| 1.0352 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0564 \| \| 0.1776 \| 72.0 \| 504 \| 0.4303 \| 0.83 \| 0.2808 \| 1.0356 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0561 \| \| 0.1776 \| 73.0 \| 511 \| 0.4301 \| 0.825 \| 0.2807 \| 1.0351 \| 0.825 \| 0.8264 \| 0.2348 \| 0.0563 \| \| 0.1776 \| 74.0 \| 518 \| 0.4304 \| 0.83 \| 0.2811 \| 1.0353 \| 0.83 \| 0.8295 \| 0.2195 \| 0.0562 \| \| 0.1776 \| 75.0 \| 525 \| 0.4301 \| 0.825 \| 0.2808 \| 1.0355 \| 0.825 \| 0.8257 \| 0.2320 \| 0.0568 \| \| 0.1776 \| 76.0 \| 532 \| 0.4302 \| 0.83 \| 0.2808 \| 1.0348 \| 0.83 \| 0.8295 \| 0.2289 \| 0.0561 \| \| 0.1776 \| 77.0 \| 539 \| 0.4301 \| 0.83 \| 0.2808 \| 1.0355 \| 0.83 \| 0.8295 \| 0.2300 \| 0.0562 \| \| 0.1776 \| 78.0 \| 546 \| 0.4301 \| 0.83 \| 0.2808 \| 1.0354 \| 0.83 \| 0.8295 \| 0.2394 \| 0.0563 \| \| 0.1776 \| 79.0 \| 553 \| 0.4302 \| 0.83 \| 0.2809 \| 1.0346 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0560 \| \| 0.1776 \| 80.0 \| 560 \| 0.4302 \| 0.83 \| 0.2809 \| 1.0353 \| 0.83 \| 0.8295 \| 0.2299 \| 0.0563 \| \| 0.1776 \| 81.0 \| 567 \| 0.4302 \| 0.83 \| 0.2809 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2299 \| 0.0563 \| \| 0.1776 \| 82.0 \| 574 \| 0.4302 \| 0.83 \| 0.2808 \| 1.0354 \| 0.83 \| 0.8295 \| 0.2298 \| 0.0560 \| \| 0.1776 \| 83.0 \| 581 \| 0.4302 \| 0.83 \| 0.2809 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2299 \| 0.0561 \| \| 0.1776 \| 84.0 \| 588 \| 0.4299 \| 0.83 \| 0.2807 \| 1.0352 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0561 \| \| 0.1776 \| 85.0 \| 595 \| 0.4301 \| 0.83 \| 0.2808 \| 1.0349 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0562 \| \| 0.1776 \| 86.0 \| 602 \| 0.4301 \| 0.83 \| 0.2808 \| 1.0351 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0562 \| \| 0.1776 \| 87.0 \| 609 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0351 \| 0.83 \| 0.8295 \| 0.2297 \| 0.0561 \| \| 0.1776 \| 88.0 \| 616 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0349 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0562 \| \| 0.1776 \| 89.0 \| 623 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0353 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0560 \| \| 0.1776 \| 90.0 \| 630 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0349 \| 0.83 \| 0.8295 \| 0.2297 \| 0.0559 \| \| 0.1776 \| 91.0 \| 637 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0352 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0562 \| \| 0.1776 \| 92.0 \| 644 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0351 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0561 \| \| 0.1776 \| 93.0 \| 651 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0351 \| 0.83 \| 0.8295 \| 0.2297 \| 0.0562 \| \| 0.1776 \| 94.0 \| 658 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0349 \| 0.83 \| 0.8295 \| 0.2297 \| 0.0560 \| \| 0.1776 \| 95.0 \| 665 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2297 \| 0.0562 \| \| 0.1776 \| 96.0 \| 672 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0349 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0561 \| \| 0.1776 \| 97.0 \| 679 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0560 \| \| 0.1776 \| 98.0 \| 686 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2296 \| 0.0560 \| \| 0.1776 \| 99.0 \| 693 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0560 \| \| 0.1776 \| 100.0 \| 700 \| 0.4300 \| 0.83 \| 0.2807 \| 1.0350 \| 0.83 \| 0.8295 \| 0.2287 \| 0.0560 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
NasimB/gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k	NasimB	2023-07-10T19:10:16Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T16:50:38Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k 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. --> # gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4226 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.7606 \| 0.29 \| 500 \| 5.6940 \| \| 5.4347 \| 0.59 \| 1000 \| 5.2560 \| \| 5.0945 \| 0.88 \| 1500 \| 5.0226 \| \| 4.8232 \| 1.18 \| 2000 \| 4.8777 \| \| 4.675 \| 1.47 \| 2500 \| 4.7626 \| \| 4.5767 \| 1.77 \| 3000 \| 4.6625 \| \| 4.4488 \| 2.06 \| 3500 \| 4.5933 \| \| 4.2612 \| 2.36 \| 4000 \| 4.5563 \| \| 4.245 \| 2.65 \| 4500 \| 4.4882 \| \| 4.208 \| 2.94 \| 5000 \| 4.4332 \| \| 3.9773 \| 3.24 \| 5500 \| 4.4362 \| \| 3.9484 \| 3.53 \| 6000 \| 4.4046 \| \| 3.9304 \| 3.83 \| 6500 \| 4.3669 \| \| 3.7943 \| 4.12 \| 7000 \| 4.3731 \| \| 3.6517 \| 4.42 \| 7500 \| 4.3646 \| \| 3.646 \| 4.71 \| 8000 \| 4.3456 \| \| 3.6381 \| 5.01 \| 8500 \| 4.3333 \| \| 3.3812 \| 5.3 \| 9000 \| 4.3586 \| \| 3.3875 \| 5.59 \| 9500 \| 4.3536 \| \| 3.3847 \| 5.89 \| 10000 \| 4.3483 \| \| 3.2816 \| 6.18 \| 10500 \| 4.3600 \| \| 3.2295 \| 6.48 \| 11000 \| 4.3636 \| \| 3.223 \| 6.77 \| 11500 \| 4.3630 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
darthPanda/ppo-Huggy-v0	darthPanda	2023-07-10T18:57:02Z	14	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-10T18:55:55Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: darthPanda/ppo-Huggy-v0 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
43m1m4n/jpbrinx	43m1m4n	2023-07-10T18:53:46Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-06-04T20:40:20Z	--- license: creativeml-openrail-m ---
MaitreHibou/Reinforce-Cartpole-v1	MaitreHibou	2023-07-10T18:49:32Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T18:49:23Z	--- 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: 500.00 +/- 0.00 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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
DavidSolan0/coverart	DavidSolan0	2023-07-10T18:34:53Z	9	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T18:30:01Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### coverart Dreambooth model trained by DavidSolan0 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
simonestradasch/COMPner-bert-base-spanish-wwm-cased	simonestradasch	2023-07-10T18:28:38Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:simonestradasch/NERcomp", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-10T18:07:06Z	--- language: - es tags: - generated_from_trainer datasets: - simonestradasch/NERcomp model-index: - name: COMPner-bert-base-spanish-wwm-cased 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. --> # COMPner-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the simonestradasch/NERcomp dataset. It achieves the following results on the evaluation set: - Loss: 0.2793 - Body Part Precision: 0.6700 - Body Part Recall: 0.7186 - Body Part F1: 0.6934 - Body Part Number: 565 - Disease Precision: 0.6966 - Disease Recall: 0.7533 - Disease F1: 0.7238 - Disease Number: 1350 - Family Member Precision: 0.9 - Family Member Recall: 0.75 - Family Member F1: 0.8182 - Family Member Number: 24 - Medication Precision: 0.7143 - Medication Recall: 0.6190 - Medication F1: 0.6633 - Medication Number: 105 - Procedure Precision: 0.5233 - Procedure Recall: 0.5125 - Procedure F1: 0.5178 - Procedure Number: 439 - Overall Precision: 0.6640 - Overall Recall: 0.6971 - Overall F1: 0.6802 - Overall Accuracy: 0.9136 ## 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: 13 - 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 \| Body Part Precision \| Body Part Recall \| Body Part F1 \| Body Part Number \| Disease Precision \| Disease Recall \| Disease F1 \| Disease Number \| Family Member Precision \| Family Member Recall \| Family Member F1 \| Family Member Number \| Medication Precision \| Medication Recall \| Medication F1 \| Medication Number \| Procedure Precision \| Procedure Recall \| Procedure F1 \| Procedure Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:--------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:--------------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.4741 \| 1.0 \| 703 \| 0.2932 \| 0.6449 \| 0.6301 \| 0.6374 \| 565 \| 0.6984 \| 0.7170 \| 0.7076 \| 1350 \| 0.9412 \| 0.6667 \| 0.7805 \| 24 \| 0.8551 \| 0.5619 \| 0.6782 \| 105 \| 0.5113 \| 0.3599 \| 0.4225 \| 439 \| 0.6674 \| 0.6271 \| 0.6466 \| 0.9091 \| \| 0.259 \| 2.0 \| 1406 \| 0.2793 \| 0.6700 \| 0.7186 \| 0.6934 \| 565 \| 0.6966 \| 0.7533 \| 0.7238 \| 1350 \| 0.9 \| 0.75 \| 0.8182 \| 24 \| 0.7143 \| 0.6190 \| 0.6633 \| 105 \| 0.5233 \| 0.5125 \| 0.5178 \| 439 \| 0.6640 \| 0.6971 \| 0.6802 \| 0.9136 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
PraveenJesu/openai-whisper-medium-peft-lora-v2.2.5	PraveenJesu	2023-07-10T18:28:05Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-10T18:28:04Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
MaitreHibou/dqn-SpaceInvadersNoFrameskip-v4	MaitreHibou	2023-07-10T18:21:47Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T18:21:06Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 656.50 +/- 140.98 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MaitreHibou -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MaitreHibou -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MaitreHibou ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0002), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
skrl/IsaacGymEnvs-AnymalTerrain-PPO	skrl	2023-07-10T18:15:29Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T20:41:55Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 19.88 +/- 0.5 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-AnymalTerrain type: IsaacGymEnvs-AnymalTerrain --- <!-- --- torch: 19.88 +/- 0.5 jax: 17.24 +/- 0.62 numpy: 17.8 +/- 0.29 --- --> # IsaacGymEnvs-AnymalTerrain-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: AnymalTerrain - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 24 # memory_size cfg["learning_epochs"] = 5 cfg["mini_batches"] = 6 # 24 * 4096 / 16384 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 3e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.001 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
FerhatDk/wav2vec2-base-finetuned-ks	FerhatDk	2023-07-10T18:08:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-09-22T08:59:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks 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-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3550 - Accuracy: 0.8727 ## 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: 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: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 8 \| 0.6840 \| 0.6 \| \| 0.6867 \| 2.0 \| 16 \| 0.6780 \| 0.6364 \| \| 0.6742 \| 3.0 \| 24 \| 0.6601 \| 0.6182 \| \| 0.6446 \| 4.0 \| 32 \| 0.6294 \| 0.6364 \| \| 0.6299 \| 5.0 \| 40 \| 0.6002 \| 0.6727 \| \| 0.6299 \| 6.0 \| 48 \| 0.5755 \| 0.7091 \| \| 0.6021 \| 7.0 \| 56 \| 0.5530 \| 0.7273 \| \| 0.5678 \| 8.0 \| 64 \| 0.5036 \| 0.8182 \| \| 0.5512 \| 9.0 \| 72 \| 0.4753 \| 0.8545 \| \| 0.4784 \| 10.0 \| 80 \| 0.4184 \| 0.9273 \| \| 0.4784 \| 11.0 \| 88 \| 0.4102 \| 0.8909 \| \| 0.4515 \| 12.0 \| 96 \| 0.4444 \| 0.8182 \| \| 0.4878 \| 13.0 \| 104 \| 0.3780 \| 0.9091 \| \| 0.4418 \| 14.0 \| 112 \| 0.4570 \| 0.8 \| \| 0.4746 \| 15.0 \| 120 \| 0.3870 \| 0.8545 \| \| 0.4746 \| 16.0 \| 128 \| 0.3932 \| 0.8364 \| \| 0.4226 \| 17.0 \| 136 \| 0.2779 \| 0.9636 \| \| 0.4301 \| 18.0 \| 144 \| 0.3125 \| 0.9455 \| \| 0.3482 \| 19.0 \| 152 \| 0.3212 \| 0.9091 \| \| 0.3611 \| 20.0 \| 160 \| 0.3925 \| 0.8364 \| \| 0.3611 \| 21.0 \| 168 \| 0.3389 \| 0.8909 \| \| 0.3507 \| 22.0 \| 176 \| 0.3099 \| 0.8727 \| \| 0.3241 \| 23.0 \| 184 \| 0.3120 \| 0.8727 \| \| 0.2533 \| 24.0 \| 192 \| 0.2313 \| 0.9455 \| \| 0.2466 \| 25.0 \| 200 \| 0.3550 \| 0.8727 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Bellaaazzzzz/model_archive	Bellaaazzzzz	2023-07-10T18:00:43Z	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-10T17:41:57Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_archive These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png)
jordyvl/vit-small_tobacco3482_kd_CEKD_t1.5_a0.7	jordyvl	2023-07-10T17:57:06Z	166	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-10T17:18:33Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t1.5_a0.7 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. --> # vit-small_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4797 - Accuracy: 0.835 - Brier Loss: 0.2522 - Nll: 0.8627 - F1 Micro: 0.835 - F1 Macro: 0.8222 - Ece: 0.1830 - Aurc: 0.0434 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 7 \| 1.9341 \| 0.215 \| 0.8749 \| 5.3238 \| 0.2150 \| 0.1264 \| 0.2642 \| 0.6914 \| \| No log \| 2.0 \| 14 \| 1.5320 \| 0.405 \| 0.7410 \| 3.5078 \| 0.405 \| 0.2276 \| 0.2957 \| 0.4015 \| \| No log \| 3.0 \| 21 \| 1.0532 \| 0.635 \| 0.5629 \| 2.0153 \| 0.635 \| 0.5844 \| 0.3037 \| 0.2006 \| \| No log \| 4.0 \| 28 \| 0.7915 \| 0.715 \| 0.4093 \| 1.6974 \| 0.715 \| 0.6762 \| 0.2420 \| 0.1131 \| \| No log \| 5.0 \| 35 \| 0.8024 \| 0.745 \| 0.3869 \| 1.7109 \| 0.745 \| 0.7548 \| 0.2160 \| 0.1006 \| \| No log \| 6.0 \| 42 \| 0.7162 \| 0.765 \| 0.3351 \| 1.8105 \| 0.765 \| 0.7599 \| 0.2216 \| 0.0874 \| \| No log \| 7.0 \| 49 \| 0.6966 \| 0.785 \| 0.3304 \| 1.5292 \| 0.785 \| 0.7682 \| 0.2058 \| 0.0979 \| \| No log \| 8.0 \| 56 \| 0.6317 \| 0.805 \| 0.2995 \| 1.3486 \| 0.805 \| 0.7887 \| 0.2266 \| 0.0721 \| \| No log \| 9.0 \| 63 \| 0.6903 \| 0.805 \| 0.3304 \| 1.5866 \| 0.805 \| 0.7971 \| 0.2371 \| 0.0995 \| \| No log \| 10.0 \| 70 \| 0.6223 \| 0.805 \| 0.2940 \| 1.3478 \| 0.805 \| 0.8114 \| 0.2281 \| 0.0697 \| \| No log \| 11.0 \| 77 \| 0.6350 \| 0.795 \| 0.3145 \| 1.3386 \| 0.795 \| 0.7730 \| 0.2063 \| 0.0962 \| \| No log \| 12.0 \| 84 \| 0.5570 \| 0.835 \| 0.2666 \| 1.2662 \| 0.835 \| 0.8181 \| 0.1951 \| 0.0553 \| \| No log \| 13.0 \| 91 \| 0.5610 \| 0.81 \| 0.2858 \| 1.2619 \| 0.81 \| 0.8002 \| 0.1884 \| 0.0626 \| \| No log \| 14.0 \| 98 \| 0.5843 \| 0.8 \| 0.2961 \| 1.0782 \| 0.8000 \| 0.8083 \| 0.1993 \| 0.0683 \| \| No log \| 15.0 \| 105 \| 0.5918 \| 0.78 \| 0.2965 \| 1.1207 \| 0.78 \| 0.7861 \| 0.1895 \| 0.0634 \| \| No log \| 16.0 \| 112 \| 0.5541 \| 0.84 \| 0.2765 \| 1.3189 \| 0.8400 \| 0.8455 \| 0.1969 \| 0.0597 \| \| No log \| 17.0 \| 119 \| 0.5037 \| 0.835 \| 0.2568 \| 0.9024 \| 0.835 \| 0.8248 \| 0.2083 \| 0.0499 \| \| No log \| 18.0 \| 126 \| 0.5050 \| 0.85 \| 0.2563 \| 1.0032 \| 0.85 \| 0.8441 \| 0.2147 \| 0.0580 \| \| No log \| 19.0 \| 133 \| 0.5430 \| 0.815 \| 0.2779 \| 1.1046 \| 0.815 \| 0.8044 \| 0.1906 \| 0.0562 \| \| No log \| 20.0 \| 140 \| 0.5276 \| 0.84 \| 0.2743 \| 0.9964 \| 0.8400 \| 0.8144 \| 0.2104 \| 0.0597 \| \| No log \| 21.0 \| 147 \| 0.5155 \| 0.835 \| 0.2686 \| 0.9556 \| 0.835 \| 0.8210 \| 0.1962 \| 0.0572 \| \| No log \| 22.0 \| 154 \| 0.4937 \| 0.835 \| 0.2581 \| 1.0079 \| 0.835 \| 0.8172 \| 0.1975 \| 0.0479 \| \| No log \| 23.0 \| 161 \| 0.4931 \| 0.845 \| 0.2533 \| 1.0021 \| 0.845 \| 0.8270 \| 0.1884 \| 0.0503 \| \| No log \| 24.0 \| 168 \| 0.4869 \| 0.83 \| 0.2554 \| 0.9660 \| 0.83 \| 0.8084 \| 0.1945 \| 0.0481 \| \| No log \| 25.0 \| 175 \| 0.4843 \| 0.845 \| 0.2512 \| 0.9979 \| 0.845 \| 0.8316 \| 0.1746 \| 0.0466 \| \| No log \| 26.0 \| 182 \| 0.4866 \| 0.835 \| 0.2531 \| 0.9006 \| 0.835 \| 0.8188 \| 0.1833 \| 0.0472 \| \| No log \| 27.0 \| 189 \| 0.4882 \| 0.825 \| 0.2562 \| 0.8929 \| 0.825 \| 0.8043 \| 0.2023 \| 0.0469 \| \| No log \| 28.0 \| 196 \| 0.4814 \| 0.82 \| 0.2494 \| 0.9122 \| 0.82 \| 0.8060 \| 0.1773 \| 0.0451 \| \| No log \| 29.0 \| 203 \| 0.4749 \| 0.835 \| 0.2501 \| 0.8770 \| 0.835 \| 0.8252 \| 0.1688 \| 0.0442 \| \| No log \| 30.0 \| 210 \| 0.4761 \| 0.84 \| 0.2490 \| 0.8848 \| 0.8400 \| 0.8250 \| 0.2068 \| 0.0443 \| \| No log \| 31.0 \| 217 \| 0.4787 \| 0.845 \| 0.2508 \| 0.8754 \| 0.845 \| 0.8309 \| 0.1635 \| 0.0438 \| \| No log \| 32.0 \| 224 \| 0.4791 \| 0.835 \| 0.2521 \| 0.8711 \| 0.835 \| 0.8224 \| 0.1876 \| 0.0446 \| \| No log \| 33.0 \| 231 \| 0.4779 \| 0.84 \| 0.2509 \| 0.8650 \| 0.8400 \| 0.8252 \| 0.1813 \| 0.0436 \| \| No log \| 34.0 \| 238 \| 0.4774 \| 0.84 \| 0.2513 \| 0.8662 \| 0.8400 \| 0.8252 \| 0.1919 \| 0.0441 \| \| No log \| 35.0 \| 245 \| 0.4760 \| 0.835 \| 0.2502 \| 0.8636 \| 0.835 \| 0.8224 \| 0.1840 \| 0.0434 \| \| No log \| 36.0 \| 252 \| 0.4784 \| 0.84 \| 0.2509 \| 0.8688 \| 0.8400 \| 0.8281 \| 0.1691 \| 0.0437 \| \| No log \| 37.0 \| 259 \| 0.4771 \| 0.835 \| 0.2507 \| 0.8670 \| 0.835 \| 0.8224 \| 0.1936 \| 0.0440 \| \| No log \| 38.0 \| 266 \| 0.4764 \| 0.835 \| 0.2499 \| 0.8614 \| 0.835 \| 0.8224 \| 0.1830 \| 0.0434 \| \| No log \| 39.0 \| 273 \| 0.4769 \| 0.835 \| 0.2503 \| 0.8651 \| 0.835 \| 0.8224 \| 0.2001 \| 0.0438 \| \| No log \| 40.0 \| 280 \| 0.4777 \| 0.84 \| 0.2514 \| 0.8608 \| 0.8400 \| 0.8281 \| 0.1832 \| 0.0435 \| \| No log \| 41.0 \| 287 \| 0.4777 \| 0.835 \| 0.2504 \| 0.8650 \| 0.835 \| 0.8224 \| 0.1953 \| 0.0437 \| \| No log \| 42.0 \| 294 \| 0.4779 \| 0.835 \| 0.2511 \| 0.8629 \| 0.835 \| 0.8224 \| 0.1944 \| 0.0440 \| \| No log \| 43.0 \| 301 \| 0.4790 \| 0.835 \| 0.2519 \| 0.8631 \| 0.835 \| 0.8222 \| 0.1808 \| 0.0439 \| \| No log \| 44.0 \| 308 \| 0.4777 \| 0.835 \| 0.2509 \| 0.8604 \| 0.835 \| 0.8222 \| 0.1886 \| 0.0435 \| \| No log \| 45.0 \| 315 \| 0.4787 \| 0.835 \| 0.2517 \| 0.8620 \| 0.835 \| 0.8222 \| 0.1940 \| 0.0437 \| \| No log \| 46.0 \| 322 \| 0.4774 \| 0.84 \| 0.2509 \| 0.8614 \| 0.8400 \| 0.8281 \| 0.1779 \| 0.0433 \| \| No log \| 47.0 \| 329 \| 0.4785 \| 0.835 \| 0.2517 \| 0.8609 \| 0.835 \| 0.8222 \| 0.1811 \| 0.0438 \| \| No log \| 48.0 \| 336 \| 0.4792 \| 0.835 \| 0.2521 \| 0.8611 \| 0.835 \| 0.8222 \| 0.1849 \| 0.0436 \| \| No log \| 49.0 \| 343 \| 0.4771 \| 0.84 \| 0.2509 \| 0.8623 \| 0.8400 \| 0.8281 \| 0.1908 \| 0.0430 \| \| No log \| 50.0 \| 350 \| 0.4793 \| 0.835 \| 0.2520 \| 0.8633 \| 0.835 \| 0.8222 \| 0.1900 \| 0.0435 \| \| No log \| 51.0 \| 357 \| 0.4786 \| 0.83 \| 0.2517 \| 0.8654 \| 0.83 \| 0.8159 \| 0.1684 \| 0.0437 \| \| No log \| 52.0 \| 364 \| 0.4792 \| 0.83 \| 0.2521 \| 0.8625 \| 0.83 \| 0.8166 \| 0.1915 \| 0.0430 \| \| No log \| 53.0 \| 371 \| 0.4785 \| 0.835 \| 0.2513 \| 0.8652 \| 0.835 \| 0.8222 \| 0.1853 \| 0.0434 \| \| No log \| 54.0 \| 378 \| 0.4798 \| 0.835 \| 0.2523 \| 0.8652 \| 0.835 \| 0.8222 \| 0.1767 \| 0.0437 \| \| No log \| 55.0 \| 385 \| 0.4791 \| 0.835 \| 0.2519 \| 0.8637 \| 0.835 \| 0.8222 \| 0.1891 \| 0.0435 \| \| No log \| 56.0 \| 392 \| 0.4790 \| 0.835 \| 0.2519 \| 0.8614 \| 0.835 \| 0.8222 \| 0.1749 \| 0.0429 \| \| No log \| 57.0 \| 399 \| 0.4782 \| 0.835 \| 0.2513 \| 0.8625 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0433 \| \| No log \| 58.0 \| 406 \| 0.4794 \| 0.835 \| 0.2521 \| 0.8602 \| 0.835 \| 0.8222 \| 0.1758 \| 0.0435 \| \| No log \| 59.0 \| 413 \| 0.4790 \| 0.835 \| 0.2517 \| 0.8617 \| 0.835 \| 0.8222 \| 0.1754 \| 0.0432 \| \| No log \| 60.0 \| 420 \| 0.4791 \| 0.835 \| 0.2520 \| 0.8614 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0430 \| \| No log \| 61.0 \| 427 \| 0.4789 \| 0.835 \| 0.2518 \| 0.8612 \| 0.835 \| 0.8222 \| 0.1870 \| 0.0432 \| \| No log \| 62.0 \| 434 \| 0.4792 \| 0.835 \| 0.2520 \| 0.8620 \| 0.835 \| 0.8222 \| 0.1902 \| 0.0433 \| \| No log \| 63.0 \| 441 \| 0.4789 \| 0.835 \| 0.2518 \| 0.8619 \| 0.835 \| 0.8222 \| 0.1997 \| 0.0431 \| \| No log \| 64.0 \| 448 \| 0.4797 \| 0.835 \| 0.2523 \| 0.8607 \| 0.835 \| 0.8222 \| 0.1833 \| 0.0434 \| \| No log \| 65.0 \| 455 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8624 \| 0.835 \| 0.8222 \| 0.1922 \| 0.0434 \| \| No log \| 66.0 \| 462 \| 0.4791 \| 0.835 \| 0.2519 \| 0.8620 \| 0.835 \| 0.8222 \| 0.1894 \| 0.0430 \| \| No log \| 67.0 \| 469 \| 0.4792 \| 0.835 \| 0.2520 \| 0.8612 \| 0.835 \| 0.8222 \| 0.1885 \| 0.0433 \| \| No log \| 68.0 \| 476 \| 0.4796 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1918 \| 0.0433 \| \| No log \| 69.0 \| 483 \| 0.4793 \| 0.835 \| 0.2521 \| 0.8628 \| 0.835 \| 0.8222 \| 0.1828 \| 0.0433 \| \| No log \| 70.0 \| 490 \| 0.4792 \| 0.835 \| 0.2519 \| 0.8622 \| 0.835 \| 0.8222 \| 0.1918 \| 0.0432 \| \| No log \| 71.0 \| 497 \| 0.4797 \| 0.835 \| 0.2523 \| 0.8615 \| 0.835 \| 0.8222 \| 0.1836 \| 0.0434 \| \| 0.194 \| 72.0 \| 504 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8618 \| 0.835 \| 0.8222 \| 0.1842 \| 0.0433 \| \| 0.194 \| 73.0 \| 511 \| 0.4794 \| 0.835 \| 0.2521 \| 0.8624 \| 0.835 \| 0.8222 \| 0.1914 \| 0.0432 \| \| 0.194 \| 74.0 \| 518 \| 0.4794 \| 0.835 \| 0.2521 \| 0.8617 \| 0.835 \| 0.8222 \| 0.1915 \| 0.0431 \| \| 0.194 \| 75.0 \| 525 \| 0.4796 \| 0.835 \| 0.2522 \| 0.8623 \| 0.835 \| 0.8222 \| 0.1917 \| 0.0434 \| \| 0.194 \| 76.0 \| 532 \| 0.4795 \| 0.835 \| 0.2520 \| 0.8622 \| 0.835 \| 0.8222 \| 0.1985 \| 0.0433 \| \| 0.194 \| 77.0 \| 539 \| 0.4795 \| 0.835 \| 0.2520 \| 0.8623 \| 0.835 \| 0.8222 \| 0.1985 \| 0.0432 \| \| 0.194 \| 78.0 \| 546 \| 0.4795 \| 0.835 \| 0.2522 \| 0.8621 \| 0.835 \| 0.8222 \| 0.1981 \| 0.0432 \| \| 0.194 \| 79.0 \| 553 \| 0.4798 \| 0.835 \| 0.2522 \| 0.8626 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0433 \| \| 0.194 \| 80.0 \| 560 \| 0.4796 \| 0.835 \| 0.2521 \| 0.8630 \| 0.835 \| 0.8222 \| 0.1984 \| 0.0433 \| \| 0.194 \| 81.0 \| 567 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8619 \| 0.835 \| 0.8222 \| 0.1902 \| 0.0434 \| \| 0.194 \| 82.0 \| 574 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8631 \| 0.835 \| 0.8222 \| 0.1913 \| 0.0433 \| \| 0.194 \| 83.0 \| 581 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0433 \| \| 0.194 \| 84.0 \| 588 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8623 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0433 \| \| 0.194 \| 85.0 \| 595 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8624 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0434 \| \| 0.194 \| 86.0 \| 602 \| 0.4796 \| 0.835 \| 0.2522 \| 0.8623 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0433 \| \| 0.194 \| 87.0 \| 609 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8629 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0434 \| \| 0.194 \| 88.0 \| 616 \| 0.4797 \| 0.835 \| 0.2521 \| 0.8634 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0433 \| \| 0.194 \| 89.0 \| 623 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1910 \| 0.0434 \| \| 0.194 \| 90.0 \| 630 \| 0.4798 \| 0.835 \| 0.2523 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0434 \| \| 0.194 \| 91.0 \| 637 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8625 \| 0.835 \| 0.8222 \| 0.1909 \| 0.0434 \| \| 0.194 \| 92.0 \| 644 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8630 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0434 \| \| 0.194 \| 93.0 \| 651 \| 0.4798 \| 0.835 \| 0.2522 \| 0.8629 \| 0.835 \| 0.8222 \| 0.1910 \| 0.0434 \| \| 0.194 \| 94.0 \| 658 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8628 \| 0.835 \| 0.8222 \| 0.1910 \| 0.0434 \| \| 0.194 \| 95.0 \| 665 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1910 \| 0.0434 \| \| 0.194 \| 96.0 \| 672 \| 0.4798 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1834 \| 0.0435 \| \| 0.194 \| 97.0 \| 679 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8628 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0434 \| \| 0.194 \| 98.0 \| 686 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8628 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0434 \| \| 0.194 \| 99.0 \| 693 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8628 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0434 \| \| 0.194 \| 100.0 \| 700 \| 0.4797 \| 0.835 \| 0.2522 \| 0.8627 \| 0.835 \| 0.8222 \| 0.1830 \| 0.0434 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
FerhatDk/wav2vec2-base_music_speech_both_classification	FerhatDk	2023-07-10T17:56:34Z	167	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-10T17:00:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base_music_speech_both_classification 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-base_music_speech_both_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Accuracy: 0.9847 ## 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: 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: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9458 \| 1.0 \| 66 \| 0.8468 \| 0.7405 \| \| 0.3785 \| 2.0 \| 132 \| 0.2951 \| 0.9771 \| \| 0.1762 \| 3.0 \| 198 \| 0.2639 \| 0.9313 \| \| 0.134 \| 4.0 \| 264 \| 0.1084 \| 0.9771 \| \| 0.0782 \| 5.0 \| 330 \| 0.0877 \| 0.9771 \| \| 0.0568 \| 6.0 \| 396 \| 0.0912 \| 0.9771 \| \| 0.0122 \| 7.0 \| 462 \| 0.4056 \| 0.9198 \| \| 0.059 \| 8.0 \| 528 \| 0.0586 \| 0.9847 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
simonestradasch/fake-news-bert-base-spanish-wwm-cased	simonestradasch	2023-07-10T17:35:24Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T17:29:57Z	--- tags: - generated_from_trainer metrics: - f1 model-index: - name: fake-news-bert-base-spanish-wwm-cased 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. --> # fake-news-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4163 - F1: 0.8558 ## 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: 13 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4947 \| 1.0 \| 140 \| 0.4019 \| 0.8137 \| \| 0.2068 \| 2.0 \| 280 \| 0.4163 \| 0.8558 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
grammarly/detexd-roberta-base	grammarly	2023-07-10T17:34:23Z	132	10	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T18:44:55Z	--- license: apache-2.0 language: - en pipeline_tag: text-classification --- # DeTexD-RoBERTa-base delicate text detection This is a baseline RoBERTa-base model for the delicate text detection task. * Paper: [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO) * [GitHub repository](https://github.com/grammarly/detexd) The labels meaning according to the paper: - LABEL_0 -> non-delicate (0) - LABEL_1 -> very low risk (1) - LABEL_2 -> low risk (2) - LABEL_3 -> medium risk (3) - LABEL_4 -> high risk (4) - LABEL_5 -> very high risk (5) ## Classification example code Here's a short usage example with the torch library in a binary classification task: ```python from transformers import pipeline classifier = pipeline("text-classification", model="grammarly/detexd-roberta-base") def predict_binary_score(text: str): # get multiclass probability scores scores = classifier(text, top_k=None) # convert to a single score by summing the probability scores # for the higher-index classes return sum(score['score'] for score in scores if score['label'] in ('LABEL_3', 'LABEL_4', 'LABEL_5')) def predict_delicate(text: str, threshold=0.72496545): return predict_binary_score(text) > threshold print(predict_delicate("Time flies like an arrow. Fruit flies like a banana.")) ``` Expected output: ``` False ``` ## Citation Information ``` @inproceedings{chernodub-etal-2023-detexd, title = "{D}e{T}ex{D}: A Benchmark Dataset for Delicate Text Detection", author = "Yavnyi, Serhii and Sliusarenko, Oleksii and Razzaghi, Jade and Mo, Yichen and Hovakimyan, Knar and Chernodub, Artem", booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.woah-1.2", pages = "14--28", abstract = "Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called {``}delicate text.{''} We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.", } ```
cagarraz/rl_course_vizdoom_health_gathering_supreme	cagarraz	2023-07-10T17:23:21Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T17:23:08Z	--- 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: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 3.94 +/- 0.20 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r cagarraz/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
komo-dono/harukatomatsu	komo-dono	2023-07-10T17:05:31Z	0	0	null	[ "region:us" ]	null	2023-07-10T17:03:49Z	--- license: openrail language: - ja tags: - music haruka tomatsu 600 epoch
opendiffusion/sentimentcheck	opendiffusion	2023-07-10T16:58:49Z	0	0	tf-keras	[ "tf-keras", "bert", "region:us" ]	null	2023-05-11T18:26:04Z	# Intro OpenDiffusion's SentimentCheck is an AI model built upon Tensorflow+Keras+Pickles. SentimentCheck harnesses the power of deep learning algorithms to accurately classify sentiment in text, making it a flexible tool for businesses, researchers, and developers. ## Usage --- language: - en - nl - de - fr - it - es license: mit --- # bert-base-multilingual-uncased-sentiment This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks. ## Training data Here is the number of product reviews we used for finetuning the model: \| Language \| Number of reviews \| \| -------- \| ----------------- \| \| English \| 150k \| \| Dutch \| 80k \| \| German \| 137k \| \| French \| 140k \| \| Italian \| 72k \| \| Spanish \| 50k \| ## Accuracy The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages: - Accuracy (exact) is the exact match on the number of stars. - Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer. \| Language \| Accuracy (exact) \| Accuracy (off-by-1) \| \| -------- \| ---------------------- \| ------------------- \| \| English \| 67% \| 95% \| Dutch \| 57% \| 93% \| German \| 61% \| 94% \| French \| 59% \| 94% \| Italian \| 59% \| 95% \| Spanish \| 58% \| 95%
Buth/fatuh	Buth	2023-07-10T16:50:46Z	0	0	adapter-transformers	[ "adapter-transformers", "en", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]	null	2023-07-10T16:48:59Z	--- license: openrail datasets: - Open-Orca/OpenOrca language: - en metrics: - accuracy library_name: adapter-transformers ---
svalcin/q-FrozenLake-v1-4x4-noSlippery	svalcin	2023-07-10T16:39:14Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T16:39:10Z	--- 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 playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="svalcin/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"]) ```
dashan1992/dsl2	dashan1992	2023-07-10T16:35:33Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-10T16:34:19Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
banden/ppo-LunarLander-v2	banden	2023-07-10T16:23:12Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T16:22:51Z	--- 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: 250.46 +/- 41.26 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 ... ```
uw-madison/mra-base-4096-8-d3	uw-madison	2023-07-10T16:12:42Z	495	0	transformers	[ "transformers", "pytorch", "mra", "fill-mask", "arxiv:2207.10284", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-06-23T06:36:15Z	# MRA MRA model for masked language modeling (MLM) for sequence length 512. ## About MRA The MRA model was proposed in [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh. The abstract from the paper is the following: Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention. This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/mra-attention).
tyavika/Distilbert-QA-Pytorch-seed	tyavika	2023-07-10T16:10:13Z	104	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2023-07-10T12:52:40Z	--- tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-seed 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-QA-Pytorch-seed This model is a fine-tuned version of [tyavika/Distilbert-QA-Pytorch-seed](https://huggingface.co/tyavika/Distilbert-QA-Pytorch-seed) on the None 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: 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: linear - num_epochs: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
mitra-mir/setfit_model_labelfaithful_epochs2	mitra-mir	2023-07-10T15:54:42Z	7	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-08T13:16:11Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 22 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 2, "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": 44, "warmup_steps": 5, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
sianadouglas/ensembletest	sianadouglas	2023-07-10T15:48:14Z	0	0	null	[ "en", "license:other", "region:us" ]	null	2023-07-10T15:47:23Z	--- license: other language: - en ---
mgmeskill/Pixelcopter-PLE-v0	mgmeskill	2023-07-10T15:38:32Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T15:26:11Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 37.13 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Suryabhan/openai-whisper-large-v2-LORA-colab	Suryabhan	2023-07-10T15:32:46Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-10T15:32:41Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0
tyavika/LR1E4-BS16-Bert_CNN512LSTM256NoBid	tyavika	2023-07-10T15:31:42Z	77	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-09T20:06:29Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Bert_CNN512LSTM256NoBid 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. --> # LR1E4-BS16-Bert_CNN512LSTM256NoBid 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: 1.6667 ## 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: 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.7267 \| 1.0 \| 3290 \| 1.5092 \| \| 1.2394 \| 2.0 \| 6580 \| 1.3933 \| \| 0.8348 \| 3.0 \| 9870 \| 1.5591 \| \| 0.542 \| 4.0 \| 13160 \| 1.6667 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
MnLgt/textual_inversion_muir_1_5	MnLgt	2023-07-10T15:31:36Z	1	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T14:16:45Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - jordandavis/textual_inversion_muir_1_5 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
agercas/speecht5_finetuned_voxpopuli_nl	agercas	2023-07-10T15:27:22Z	78	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-10T09:21:57Z	--- license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl 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. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4572 ## 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: 4 - eval_batch_size: 2 - 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 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5221 \| 4.3 \| 1000 \| 0.4774 \| \| 0.505 \| 8.61 \| 2000 \| 0.4648 \| \| 0.4929 \| 12.91 \| 3000 \| 0.4583 \| \| 0.4901 \| 17.21 \| 4000 \| 0.4572 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Trong-Nghia/xlnet-base-cased-detect-dep	Trong-Nghia	2023-07-10T15:17:37Z	90	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlnet", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-30T13:14:00Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: xlnet-base-cased-detect-dep 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. --> # xlnet-base-cased-detect-dep This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5555 - Accuracy: 0.744 - F1: 0.8164 ## 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-06 - 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 376 \| 0.5582 \| 0.716 \| 0.8065 \| \| 0.6188 \| 2.0 \| 752 \| 0.5479 \| 0.756 \| 0.8232 \| \| 0.5835 \| 3.0 \| 1128 \| 0.5306 \| 0.758 \| 0.8276 \| \| 0.5492 \| 4.0 \| 1504 \| 0.5555 \| 0.744 \| 0.8164 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Birchlabs/llama-13b-stepwise-embeddings	Birchlabs	2023-07-10T15:17:11Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-07-10T13:55:53Z	--- license: apache-2.0 --- Fine-tuned input (`embed_tokens: Embedding`) and output (`lm_head: Linear`) embeddings layers, for use with [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Prior to finetuning: we grew the vocabulary of the tokenizer and embeddings layers. The new embeddings were average-initialized, and needed training, so we trained them. These are the weights from that training. Ordinarily a QLoRA finetune of an LLM would not finetune the `embed_tokens: Embedding` (you'd need to get a bit creative, because not only have the dimensions changed, but also I don't believe any way has been established to train _adapters_ over `Embedding`s). Nor apparently would it finetune `lm_head: Linear`. This is harder than it sounds (i.e. you can't handle it the same way you adapt the other Linear layers), because the dimensions have grown.
EleutherAI/pythia-1b-deduped	EleutherAI	2023-07-10T15:04:31Z	22,714	18	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-02-14T00:07:42Z	--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> This is the current release. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-1B-deduped ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 2M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 2M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 2M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-1B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-1B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-1B-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-1B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-1B-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-1B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data Pythia-1B-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
NICFRU/bart-base-paraphrasing-news	NICFRU	2023-07-10T15:02:02Z	106	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-10T14:46:34Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-paraphrasing 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. --> # bart-base-paraphrasing This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6617 - Rouge1: 57.7088 - Rouge2: 51.0096 - Rougel: 54.7514 - Rougelsum: 56.3943 - Gen Len: 20.0 ## 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: 4e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 0.2 \| 10 \| 0.5263 \| 58.2676 \| 51.5842 \| 55.5057 \| 57.1584 \| 19.94 \| \| No log \| 0.4 \| 20 \| 0.5050 \| 56.1604 \| 48.7383 \| 54.0373 \| 55.372 \| 20.0 \| \| No log \| 0.6 \| 30 \| 0.4674 \| 58.0617 \| 51.4993 \| 56.0368 \| 56.9665 \| 20.0 \| \| No log \| 0.8 \| 40 \| 0.4545 \| 57.5375 \| 51.0203 \| 55.5247 \| 56.5761 \| 19.94 \| \| No log \| 1.0 \| 50 \| 0.4373 \| 57.7263 \| 50.8021 \| 55.0549 \| 56.35 \| 19.98 \| \| No log \| 1.2 \| 60 \| 0.4313 \| 57.87 \| 50.9904 \| 54.9727 \| 56.5379 \| 19.97 \| \| No log \| 1.4 \| 70 \| 0.4855 \| 56.5101 \| 49.3124 \| 54.1572 \| 55.0671 \| 20.0 \| \| No log \| 1.6 \| 80 \| 0.4202 \| 56.6535 \| 50.0302 \| 53.6891 \| 55.1016 \| 19.96 \| \| No log \| 1.8 \| 90 \| 0.4544 \| 57.315 \| 50.6289 \| 54.642 \| 55.7326 \| 19.95 \| \| 0.5858 \| 2.0 \| 100 \| 0.4157 \| 56.4569 \| 48.8105 \| 53.937 \| 55.3515 \| 20.0 \| \| 0.5858 \| 2.2 \| 110 \| 0.4555 \| 57.8424 \| 51.5966 \| 55.6655 \| 56.6862 \| 20.0 \| \| 0.5858 \| 2.4 \| 120 \| 0.4196 \| 58.2562 \| 51.7596 \| 55.5085 \| 57.1823 \| 19.97 \| \| 0.5858 \| 2.6 \| 130 \| 0.4334 \| 58.6906 \| 51.6106 \| 55.6631 \| 57.5254 \| 19.89 \| \| 0.5858 \| 2.8 \| 140 \| 0.4710 \| 56.5401 \| 49.33 \| 53.8792 \| 55.0282 \| 20.0 \| \| 0.5858 \| 3.0 \| 150 \| 0.4357 \| 58.2083 \| 52.0049 \| 55.9938 \| 57.1928 \| 20.0 \| \| 0.5858 \| 3.2 \| 160 \| 0.4735 \| 58.8112 \| 52.2196 \| 56.5004 \| 57.7703 \| 19.94 \| \| 0.5858 \| 3.4 \| 170 \| 0.4428 \| 57.6778 \| 50.6377 \| 54.8752 \| 56.4778 \| 20.0 \| \| 0.5858 \| 3.6 \| 180 \| 0.4983 \| 57.4124 \| 50.4244 \| 54.6163 \| 56.0992 \| 20.0 \| \| 0.5858 \| 3.8 \| 190 \| 0.4620 \| 58.0701 \| 51.5021 \| 55.7222 \| 56.8737 \| 20.0 \| \| 0.2865 \| 4.0 \| 200 \| 0.4502 \| 59.1191 \| 52.7516 \| 56.4389 \| 57.7153 \| 20.0 \| \| 0.2865 \| 4.2 \| 210 \| 0.4805 \| 58.9064 \| 52.7148 \| 56.1058 \| 57.6709 \| 20.0 \| \| 0.2865 \| 4.4 \| 220 \| 0.4755 \| 58.6883 \| 52.1464 \| 55.9164 \| 57.3825 \| 20.0 \| \| 0.2865 \| 4.6 \| 230 \| 0.4524 \| 58.9916 \| 52.1101 \| 56.4116 \| 57.9468 \| 19.9 \| \| 0.2865 \| 4.8 \| 240 \| 0.4726 \| 58.9953 \| 52.8173 \| 56.5846 \| 58.0805 \| 20.0 \| \| 0.2865 \| 5.0 \| 250 \| 0.4841 \| 58.1058 \| 51.614 \| 55.3374 \| 56.7617 \| 20.0 \| \| 0.2865 \| 5.2 \| 260 \| 0.5047 \| 58.2785 \| 51.1874 \| 55.5336 \| 56.8795 \| 20.0 \| \| 0.2865 \| 5.4 \| 270 \| 0.4658 \| 57.2753 \| 49.6038 \| 53.9588 \| 55.6038 \| 19.91 \| \| 0.2865 \| 5.6 \| 280 \| 0.5261 \| 58.1691 \| 51.5254 \| 55.2685 \| 56.7787 \| 20.0 \| \| 0.2865 \| 5.8 \| 290 \| 0.4833 \| 57.8088 \| 51.2838 \| 54.8739 \| 56.4374 \| 20.0 \| \| 0.1668 \| 6.0 \| 300 \| 0.5067 \| 58.2021 \| 51.3629 \| 55.3548 \| 56.9093 \| 19.99 \| \| 0.1668 \| 6.2 \| 310 \| 0.5461 \| 58.0327 \| 51.4051 \| 55.3426 \| 56.7923 \| 20.0 \| \| 0.1668 \| 6.4 \| 320 \| 0.5463 \| 58.1027 \| 51.3706 \| 55.1733 \| 56.7923 \| 19.9 \| \| 0.1668 \| 6.6 \| 330 \| 0.5837 \| 57.6284 \| 50.8245 \| 54.6253 \| 56.2127 \| 20.0 \| \| 0.1668 \| 6.8 \| 340 \| 0.5221 \| 58.0869 \| 51.5448 \| 55.4226 \| 56.7532 \| 20.0 \| \| 0.1668 \| 7.0 \| 350 \| 0.5433 \| 58.7676 \| 52.0403 \| 56.2634 \| 57.6441 \| 20.0 \| \| 0.1668 \| 7.2 \| 360 \| 0.5498 \| 57.9172 \| 50.9727 \| 55.1006 \| 56.6018 \| 20.0 \| \| 0.1668 \| 7.4 \| 370 \| 0.5581 \| 57.4669 \| 50.698 \| 54.6448 \| 56.1325 \| 20.0 \| \| 0.1668 \| 7.6 \| 380 \| 0.5526 \| 57.0821 \| 50.298 \| 54.1635 \| 55.8059 \| 20.0 \| \| 0.1668 \| 7.8 \| 390 \| 0.5548 \| 57.5422 \| 50.2734 \| 54.2446 \| 56.1223 \| 20.0 \| \| 0.1071 \| 8.0 \| 400 \| 0.5620 \| 57.4548 \| 50.2657 \| 54.5094 \| 55.9422 \| 20.0 \| \| 0.1071 \| 8.2 \| 410 \| 0.5772 \| 57.4144 \| 50.2443 \| 54.5173 \| 55.9331 \| 20.0 \| \| 0.1071 \| 8.4 \| 420 \| 0.5857 \| 57.2975 \| 50.2116 \| 54.5918 \| 55.9931 \| 20.0 \| \| 0.1071 \| 8.6 \| 430 \| 0.5827 \| 58.4767 \| 51.4318 \| 55.4792 \| 57.1284 \| 20.0 \| \| 0.1071 \| 8.8 \| 440 \| 0.5728 \| 58.4414 \| 51.3523 \| 55.2838 \| 57.202 \| 20.0 \| \| 0.1071 \| 9.0 \| 450 \| 0.5919 \| 58.0499 \| 51.3783 \| 55.0748 \| 56.6939 \| 20.0 \| \| 0.1071 \| 9.2 \| 460 \| 0.5937 \| 57.7604 \| 50.845 \| 54.8941 \| 56.351 \| 20.0 \| \| 0.1071 \| 9.4 \| 470 \| 0.5970 \| 57.3655 \| 50.4126 \| 54.4522 \| 55.7815 \| 20.0 \| \| 0.1071 \| 9.6 \| 480 \| 0.5911 \| 58.203 \| 51.0367 \| 55.3215 \| 56.8485 \| 20.0 \| \| 0.1071 \| 9.8 \| 490 \| 0.6121 \| 58.2898 \| 51.2749 \| 55.4292 \| 57.0241 \| 20.0 \| \| 0.0718 \| 10.0 \| 500 \| 0.5903 \| 58.2487 \| 51.3838 \| 55.4237 \| 56.8863 \| 20.0 \| \| 0.0718 \| 10.2 \| 510 \| 0.5983 \| 58.2681 \| 51.0925 \| 55.2887 \| 56.9562 \| 20.0 \| \| 0.0718 \| 10.4 \| 520 \| 0.6308 \| 57.9797 \| 50.7386 \| 54.995 \| 56.5939 \| 20.0 \| \| 0.0718 \| 10.6 \| 530 \| 0.6307 \| 57.6269 \| 50.5515 \| 54.446 \| 56.1544 \| 20.0 \| \| 0.0718 \| 10.8 \| 540 \| 0.6173 \| 57.9545 \| 51.1005 \| 54.9406 \| 56.5732 \| 20.0 \| \| 0.0718 \| 11.0 \| 550 \| 0.6322 \| 58.3718 \| 51.4321 \| 55.4241 \| 57.1879 \| 20.0 \| \| 0.0718 \| 11.2 \| 560 \| 0.6027 \| 58.6581 \| 51.8607 \| 55.6436 \| 57.32 \| 20.0 \| \| 0.0718 \| 11.4 \| 570 \| 0.6140 \| 58.6476 \| 51.7822 \| 55.5845 \| 57.3018 \| 20.0 \| \| 0.0718 \| 11.6 \| 580 \| 0.6184 \| 59.2454 \| 52.4204 \| 56.2174 \| 57.9278 \| 20.0 \| \| 0.0718 \| 11.8 \| 590 \| 0.6281 \| 59.2945 \| 52.8165 \| 56.547 \| 58.0674 \| 20.0 \| \| 0.0512 \| 12.0 \| 600 \| 0.6128 \| 58.2165 \| 51.3689 \| 55.37 \| 56.8342 \| 20.0 \| \| 0.0512 \| 12.2 \| 610 \| 0.6482 \| 57.9196 \| 50.9793 \| 55.0883 \| 56.6986 \| 20.0 \| \| 0.0512 \| 12.4 \| 620 \| 0.6267 \| 57.4782 \| 50.1118 \| 54.2802 \| 55.8872 \| 20.0 \| \| 0.0512 \| 12.6 \| 630 \| 0.6198 \| 57.457 \| 50.4079 \| 54.2449 \| 55.8118 \| 20.0 \| \| 0.0512 \| 12.8 \| 640 \| 0.6500 \| 57.6903 \| 51.0627 \| 55.0743 \| 56.3025 \| 20.0 \| \| 0.0512 \| 13.0 \| 650 \| 0.6265 \| 57.4394 \| 50.9013 \| 54.7936 \| 56.1688 \| 20.0 \| \| 0.0512 \| 13.2 \| 660 \| 0.6817 \| 58.4345 \| 51.7087 \| 55.291 \| 57.0057 \| 20.0 \| \| 0.0512 \| 13.4 \| 670 \| 0.6322 \| 57.869 \| 50.9503 \| 54.8937 \| 56.5178 \| 20.0 \| \| 0.0512 \| 13.6 \| 680 \| 0.6424 \| 57.8285 \| 51.1014 \| 55.0072 \| 56.5022 \| 20.0 \| \| 0.0512 \| 13.8 \| 690 \| 0.6668 \| 58.7067 \| 51.9929 \| 55.5044 \| 57.1517 \| 20.0 \| \| 0.0397 \| 14.0 \| 700 \| 0.6537 \| 58.8717 \| 52.4036 \| 55.6521 \| 57.4855 \| 20.0 \| \| 0.0397 \| 14.2 \| 710 \| 0.6463 \| 58.9623 \| 52.4749 \| 55.8145 \| 57.8095 \| 20.0 \| \| 0.0397 \| 14.4 \| 720 \| 0.6630 \| 58.8097 \| 52.1997 \| 55.8204 \| 57.6325 \| 20.0 \| \| 0.0397 \| 14.6 \| 730 \| 0.6839 \| 59.0479 \| 52.6573 \| 56.0439 \| 57.7322 \| 20.0 \| \| 0.0397 \| 14.8 \| 740 \| 0.6541 \| 59.2854 \| 52.6109 \| 56.1891 \| 57.9446 \| 20.0 \| \| 0.0397 \| 15.0 \| 750 \| 0.6486 \| 58.8419 \| 52.2004 \| 55.8071 \| 57.49 \| 20.0 \| \| 0.0397 \| 15.2 \| 760 \| 0.6578 \| 57.6161 \| 50.7276 \| 54.5514 \| 56.2359 \| 20.0 \| \| 0.0397 \| 15.4 \| 770 \| 0.6673 \| 57.5458 \| 50.8286 \| 54.4597 \| 56.1513 \| 20.0 \| \| 0.0397 \| 15.6 \| 780 \| 0.6624 \| 57.6634 \| 51.0017 \| 54.6769 \| 56.3837 \| 20.0 \| \| 0.0397 \| 15.8 \| 790 \| 0.6469 \| 57.9037 \| 51.137 \| 54.8939 \| 56.6427 \| 20.0 \| \| 0.0301 \| 16.0 \| 800 \| 0.6373 \| 57.8696 \| 51.0899 \| 54.8543 \| 56.4596 \| 20.0 \| \| 0.0301 \| 16.2 \| 810 \| 0.6712 \| 58.614 \| 52.0052 \| 55.6436 \| 57.3211 \| 20.0 \| \| 0.0301 \| 16.4 \| 820 \| 0.6812 \| 58.5214 \| 51.8911 \| 55.7447 \| 57.2663 \| 20.0 \| \| 0.0301 \| 16.6 \| 830 \| 0.6716 \| 58.5818 \| 51.929 \| 55.7993 \| 57.4064 \| 20.0 \| \| 0.0301 \| 16.8 \| 840 \| 0.6590 \| 57.745 \| 51.0481 \| 54.8545 \| 56.4781 \| 20.0 \| \| 0.0301 \| 17.0 \| 850 \| 0.6695 \| 57.6663 \| 50.9646 \| 54.7863 \| 56.3687 \| 20.0 \| \| 0.0301 \| 17.2 \| 860 \| 0.6858 \| 57.5552 \| 51.0436 \| 54.7092 \| 56.3079 \| 20.0 \| \| 0.0301 \| 17.4 \| 870 \| 0.6840 \| 57.9091 \| 51.3823 \| 54.8309 \| 56.6186 \| 20.0 \| \| 0.0301 \| 17.6 \| 880 \| 0.6751 \| 57.8223 \| 51.1688 \| 54.7562 \| 56.5558 \| 20.0 \| \| 0.0301 \| 17.8 \| 890 \| 0.6589 \| 57.9956 \| 51.1425 \| 54.9509 \| 56.6868 \| 20.0 \| \| 0.0482 \| 18.0 \| 900 \| 0.6634 \| 58.0392 \| 51.3121 \| 55.0726 \| 56.7878 \| 20.0 \| \| 0.0482 \| 18.2 \| 910 \| 0.6907 \| 58.2021 \| 51.4548 \| 55.1874 \| 56.91 \| 20.0 \| \| 0.0482 \| 18.4 \| 920 \| 0.6977 \| 58.1124 \| 51.4254 \| 55.062 \| 56.8412 \| 20.0 \| \| 0.0482 \| 18.6 \| 930 \| 0.6832 \| 58.0776 \| 51.3168 \| 55.0849 \| 56.8226 \| 20.0 \| \| 0.0482 \| 18.8 \| 940 \| 0.6672 \| 57.925 \| 51.2475 \| 54.9661 \| 56.655 \| 20.0 \| \| 0.0482 \| 19.0 \| 950 \| 0.6582 \| 57.9285 \| 51.2483 \| 54.9744 \| 56.6609 \| 20.0 \| \| 0.0482 \| 19.2 \| 960 \| 0.6575 \| 57.9285 \| 51.2483 \| 54.9744 \| 56.6609 \| 20.0 \| \| 0.0482 \| 19.4 \| 970 \| 0.6619 \| 57.8961 \| 51.2097 \| 54.9475 \| 56.6344 \| 20.0 \| \| 0.0482 \| 19.6 \| 980 \| 0.6658 \| 57.8961 \| 51.2097 \| 54.9475 \| 56.6344 \| 20.0 \| \| 0.0482 \| 19.8 \| 990 \| 0.6635 \| 57.7222 \| 51.0096 \| 54.8166 \| 56.4623 \| 20.0 \| \| 0.0201 \| 20.0 \| 1000 \| 0.6617 \| 57.7088 \| 51.0096 \| 54.7514 \| 56.3943 \| 20.0 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
alienware/layoutlmv3-finetuned-cord_100	alienware	2023-07-10T15:01:59Z	4	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:cord-layoutlmv3", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-09T12:32:12Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - cord-layoutlmv3 metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-cord_100 results: - task: name: Token Classification type: token-classification dataset: name: cord-layoutlmv3 type: cord-layoutlmv3 config: cord split: test args: cord metrics: - name: Precision type: precision value: 0.9569093610698366 - name: Recall type: recall value: 0.9640718562874252 - name: F1 type: f1 value: 0.9604772557792692 - name: Accuracy type: accuracy value: 0.9681663837011885 --- <!-- 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. --> # layoutlmv3-finetuned-cord_100 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1720 - Precision: 0.9569 - Recall: 0.9641 - F1: 0.9605 - Accuracy: 0.9682 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.56 \| 250 \| 0.3320 \| 0.9011 \| 0.9207 \| 0.9108 \| 0.9253 \| \| 0.3502 \| 3.12 \| 500 \| 0.2811 \| 0.9281 \| 0.9371 \| 0.9326 \| 0.9427 \| \| 0.3502 \| 4.69 \| 750 \| 0.2429 \| 0.9210 \| 0.9341 \| 0.9275 \| 0.9435 \| \| 0.162 \| 6.25 \| 1000 \| 0.2264 \| 0.9385 \| 0.9476 \| 0.9430 \| 0.9542 \| \| 0.162 \| 7.81 \| 1250 \| 0.1996 \| 0.9373 \| 0.9513 \| 0.9443 \| 0.9601 \| \| 0.0971 \| 9.38 \| 1500 \| 0.1686 \| 0.9569 \| 0.9633 \| 0.9601 \| 0.9690 \| \| 0.0971 \| 10.94 \| 1750 \| 0.1814 \| 0.9532 \| 0.9603 \| 0.9567 \| 0.9652 \| \| 0.0704 \| 12.5 \| 2000 \| 0.1915 \| 0.9539 \| 0.9611 \| 0.9575 \| 0.9656 \| \| 0.0704 \| 14.06 \| 2250 \| 0.1833 \| 0.9590 \| 0.9633 \| 0.9612 \| 0.9677 \| \| 0.0513 \| 15.62 \| 2500 \| 0.1720 \| 0.9569 \| 0.9641 \| 0.9605 \| 0.9682 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
SHENMU007/neunit_BASE_V10.19	SHENMU007	2023-07-10T15:01:47Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-07-07T08:50:51Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit 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. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
NasimB/gpt2-cocnat-mod-datasets-txt-processing	NasimB	2023-07-10T15:01:23Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T12:29:02Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-mod-datasets-txt-processing 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. --> # gpt2-cocnat-mod-datasets-txt-processing This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3377 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.6848 \| 0.3 \| 500 \| 5.6500 \| \| 5.3379 \| 0.59 \| 1000 \| 5.2204 \| \| 4.9909 \| 0.89 \| 1500 \| 4.9703 \| \| 4.7146 \| 1.19 \| 2000 \| 4.8200 \| \| 4.5695 \| 1.49 \| 2500 \| 4.7076 \| \| 4.4685 \| 1.78 \| 3000 \| 4.5985 \| \| 4.3237 \| 2.08 \| 3500 \| 4.5311 \| \| 4.1614 \| 2.38 \| 4000 \| 4.4731 \| \| 4.1267 \| 2.68 \| 4500 \| 4.4151 \| \| 4.082 \| 2.97 \| 5000 \| 4.3593 \| \| 3.8448 \| 3.27 \| 5500 \| 4.3575 \| \| 3.8261 \| 3.57 \| 6000 \| 4.3240 \| \| 3.8089 \| 3.86 \| 6500 \| 4.2887 \| \| 3.6462 \| 4.16 \| 7000 \| 4.2921 \| \| 3.5453 \| 4.46 \| 7500 \| 4.2840 \| \| 3.529 \| 4.76 \| 8000 \| 4.2688 \| \| 3.4926 \| 5.05 \| 8500 \| 4.2683 \| \| 3.3463 \| 5.35 \| 9000 \| 4.2715 \| \| 3.3453 \| 5.65 \| 9500 \| 4.2702 \| \| 3.3408 \| 5.95 \| 10000 \| 4.2694 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
rickareo/distilbert-base-uncased-finetuned-emotion	rickareo	2023-07-10T14:59:25Z	105	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	2023-07-10T14:44:04Z	--- 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: split metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9229910973969778 --- <!-- 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: 0.2155 - Accuracy: 0.923 - F1: 0.9230 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8271 \| 1.0 \| 250 \| 0.3166 \| 0.903 \| 0.8989 \| \| 0.2469 \| 2.0 \| 500 \| 0.2155 \| 0.923 \| 0.9230 \| ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
ericNguyen0132/DepRoBERTa-2ndStage	ericNguyen0132	2023-07-10T14:56:14Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T13:42:58Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: DepRoBERTa-2ndStage 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. --> # DepRoBERTa-2ndStage This model is a fine-tuned version of [rafalposwiata/deproberta-large-v1](https://huggingface.co/rafalposwiata/deproberta-large-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6330 - Accuracy: 0.855 - F1: 0.9134 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 469 \| 0.3572 \| 0.8617 \| 0.9224 \| \| 0.4953 \| 2.0 \| 938 \| 0.3593 \| 0.8783 \| 0.9315 \| \| 0.3493 \| 3.0 \| 1407 \| 0.4274 \| 0.8483 \| 0.9091 \| \| 0.313 \| 4.0 \| 1876 \| 0.5488 \| 0.8617 \| 0.9187 \| \| 0.2622 \| 5.0 \| 2345 \| 0.6330 \| 0.855 \| 0.9134 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
pcuenq/falcon-7b-instruct-transformers	pcuenq	2023-07-10T14:54:25Z	7	0	transformers	[ "transformers", "pytorch", "falcon", "text-generation", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-10T12:57:31Z	--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true license: apache-2.0 duplicated_from: pcuenq/falcon-7b-instruct --- # ✨ Falcon-7B-Instruct Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license. Paper coming soon 😊. 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). * Falcon-7B is a strong base model, outperforming comparable open-source models (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * It features an architecture optimized for inference, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 This is an instruct model, which may not be ideal for further finetuning. If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 Looking for an even more powerful model? [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 Falcon LLMs require PyTorch 2.0 for use with `transformers`! For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need at least 16GB of memory to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae); - Model type: Causal decoder-only; - Language(s) (NLP): English and French; - License: Apache 2.0; - Finetuned from model: [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - Paper: coming soon. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. \| Data source \| Fraction \| Tokens \| Description \| \|--------------------\|--------------\|------------\|-----------------------------------\| \| [Bai ze](https://github.com/project-baize/baize-chatbot) \| 65% \| 164M \| chat \| \| [GPT4All](https://github.com/nomic-ai/gpt4all) \| 25% \| 62M \| instruct \| \| [GPTeacher](https://github.com/teknium1/GPTeacher) \| 5% \| 11M \| instruct \| \| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) \| 5% \| 13M \| massive web crawl \| The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation Paper coming soon. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * Positionnal embeddings: rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * Attention: multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * Decoder-block: parallel attention/MLP with a single layer norm. \| Hyperparameter \| Value \| Comment \| \|--------------------\|-----------\|----------------------------------------\| \| Layers \| 32 \| \| \| `d_model` \| 4544 \| Increased to compensate for multiquery \| \| `head_dim` \| 64 \| Reduced to optimise for FlashAttention \| \| Vocabulary \| 65024 \| \| \| Sequence length \| 2048 \| \| ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation Paper coming soon 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact [email protected]
pmpc/de_pipeline	pmpc	2023-07-10T14:53:50Z	1	0	spacy	[ "spacy", "token-classification", "de", "model-index", "region:us" ]	token-classification	2023-07-10T10:51:54Z	--- tags: - spacy - token-classification language: - de model-index: - name: de_pipeline results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9573497322 - name: NER Recall type: recall value: 0.9567803331 - name: NER F Score type: f_score value: 0.9570649479 --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `de_pipeline` \| \| Version \| `0.0.0` \| \| spaCy \| `>=3.5.3,<3.6.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| n/a \| \| License \| n/a \| \| Author \| [n/a]() \| ### Label Scheme <details> <summary>View label scheme (19 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `AN`, `EUN`, `GRT`, `GS`, `INN`, `LD`, `LDS`, `LIT`, `MRK`, `ORG`, `PER`, `RR`, `RS`, `ST`, `STR`, `UN`, `VO`, `VS`, `VT` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `ENTS_F` \| 95.71 \| \| `ENTS_P` \| 95.73 \| \| `ENTS_R` \| 95.68 \| \| `TRANSFORMER_LOSS` \| 11836.63 \| \| `NER_LOSS` \| 8009.96 \|
firecoral/ppo-LunarLander-v2	firecoral	2023-07-10T14:49:32Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T14:49:10Z	--- 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: 261.63 +/- 20.29 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 ... ```
lizhuang144/flan-t5-base-factual-sg	lizhuang144	2023-07-10T14:34:47Z	109	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-06T11:13:16Z	See details at 'https://github.com/zhuang-li/FACTUAL/tree/main'
marsh5/Reinforce-cartpole	marsh5	2023-07-10T14:31:44Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T14:31:34Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Birchlabs/llama-13b-stepwise-tokenizer	Birchlabs	2023-07-10T14:25:54Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2023-07-10T13:49:42Z	--- license: apache-2.0 --- forked from https://huggingface.co/huggyllama/llama-13b/tree/main This tokenizer supports [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Adds four new tokens for stepwise reasoning: ``` <\|step_start\|> <\|step_end\|> <\|answer_start\|> <\|answer_end\|> ``` See [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter) for details of how all the parts should be used together.
JennnDexter/textual_inversion	JennnDexter	2023-07-10T14:24:31Z	29	0	diffusers	[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-07T11:57:47Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - JennnDexter/textual_inversion These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. You can find some example images in the following.
sannne990/meinahentai	sannne990	2023-07-10T14:08:40Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-10T13:56:40Z	--- license: creativeml-openrail-m ---
iammartian0/speecht5_finetuned_voxpopuli_it	iammartian0	2023-07-10T14:03:39Z	81	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli/it", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-10T11:00:58Z	--- license: mit tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli/it model-index: - name: speecht5_finetuned_voxpopuli_it 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. --> # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli/it dataset. It achieves the following results on the evaluation set: - Loss: 0.4855 ## 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: 4 - eval_batch_size: 2 - 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 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5467 \| 10.58 \| 1000 \| 0.5003 \| \| 0.5182 \| 21.16 \| 2000 \| 0.4882 \| \| 0.5046 \| 31.75 \| 3000 \| 0.4857 \| \| 0.5013 \| 42.33 \| 4000 \| 0.4855 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
dilip-reddy/ppo-LunarLander	dilip-reddy	2023-07-10T13:57:53Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T13:57:33Z	--- 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: 268.69 +/- 17.74 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 ... ```
cointegrated/rubert-base-lesha17-punctuation	cointegrated	2023-07-10T13:56:54Z	125	1	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	The model for https://github.com/Lesha17/Punctuation; all credits go to the owner of this repository.
JoaoReis/Neuronet	JoaoReis	2023-07-10T13:45:12Z	0	0	null	[ "region:us" ]	null	2023-07-10T13:29:55Z	import socket,warnings try: socket.setdefaulttimeout(1) socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect(('1.1.1.1', 53)) except socket.error as ex: raise Exception("STOP: No internet. Click '>\|' in top right and set 'Internet' switch to on") import os iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '') if iskaggle: !pip install -Uqq fastai !pip install -Uqq duckduckgo_search from duckduckgo_search import ddg_images from fastcore.all import * def search_images(term, max_images=200): return L(ddg_images(term, max_results=max_images)).itemgot('image') urls = search_images(' star fox photos', max_images=1) urls[0] from fastdownload import download_url dest = 'starfox.jpg' download_url(urls[0], dest, show_progress=False) from fastai.vision.all import * im = Image.open(dest) im.to_thumb(256,256) download_url(search_images('eva 01', max_images=1)[0], 'forest.jpg', show_progress=False) Image.open('forest.jpg').to_thumb(256,256) searches = 'eva 01','star fox' path = Path('eva 01_or_not') from time import sleep for o in searches: dest = (path/o) dest.mkdir(exist_ok=True, parents=True) download_images(dest, urls=search_images(f'{o} photo')) sleep(10) # Pause between searches to avoid over-loading server download_images(dest, urls=search_images(f'{o} sun photo')) sleep(10) download_images(dest, urls=search_images(f'{o} shade photo')) sleep(10) resize_images(path/o, max_size=400, dest=path/o) failed = verify_images(get_image_files(path)) failed.map(Path.unlink) len(failed) dls = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=[Resize(192, method='squish')] ).dataloaders(path) dls.show_batch(max_n=6) learn = vision_learner(dls, resnet18, metrics=error_rate) learn.fine_tune(3) is_bird,_,probs = learn.predict(PILImage.create('bird.jpg')) print(f"This is a: {is_bird}.") print(f"Probability it's a bird: {probs[0]:.4f}")
PKU-Alignment/beaver-dam-7b	PKU-Alignment	2023-07-10T13:42:02Z	1,707	6	safe-rlhf	[ "safe-rlhf", "pytorch", "llama", "beaver", "safety", "ai-safety", "deepspeed", "rlhf", "alpaca", "en", "dataset:PKU-Alignment/BeaverTails", "arxiv:2302.13971", "region:us" ]	null	2023-07-10T02:57:51Z	--- datasets: - PKU-Alignment/BeaverTails language: - en tags: - beaver - safety - llama - ai-safety - deepspeed - rlhf - alpaca library_name: safe-rlhf --- # 🦫 BeaverDam Model Card ## Beaver-Dam-7B Boasting 7 billion parameters, Beaver-Dam-7B is a powerful QA-Moderation model derived from the Llama-7B base model and trained on the [PKU-Alignment/BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) Classification Dataset. Beaver-Dam's key feature is its ability to analyze responses to prompts for toxicity across 14 different categories. - Developed by: [PKU-Alignment Team](https://github.com/PKU-Alignment) - Model type: QA moderation - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971) ## Model Sources - Repository: https://github.com/PKU-Alignment/beavertails - Web: https://sites.google.com/view/pku-beavertails - Paper: Coming soon ## Why Choose Beaver-Dam-7B? Traditional approaches to content moderation in Question-Answering (QA) tasks often gauge the toxicity of a QA pair by examining each utterance individually. This method, while effective to a degree, can inadvertently result in a significant number of user prompts being discarded. If the moderation system perceives them as too harmful, it may prevent the language model from generating appropriate responses, consequently interrupting the user experience and potentially hindering the evolution of a beneficial AI with human-like understanding. BeaverDam is a shift in the approach to content moderation for QA tasks - a concept we term "QA moderation": ![qa-moderation-teaser.png](qa-moderation-teaser.png) In this paradigm, a QA pair is classified as harmful or benign based on its degree of risk neutrality. Specifically, it assesses the extent to which potential risks in a potentially harmful question can be counteracted by a non-threatening response.
WALIDALI/bekiamzrev	WALIDALI	2023-07-10T13:39:54Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-10T13:33:42Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekiamzrev Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
skrl/IsaacGymEnvs-FrankaCabinet-PPO	skrl	2023-07-10T13:39:14Z	0	0	skrl	[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-01-24T20:43:14Z	--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 3368.97 +/- 117.64 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FrankaCabinet type: IsaacGymEnvs-FrankaCabinet --- <!-- --- torch: 3250.18 +/- 126.12 jax: 3368.97 +/- 117.64 numpy: 3118.77 +/- 140.06 --- --> # IsaacGymEnvs-FrankaCabinet-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - Task: FrankaCabinet - Agent: [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FrankaCabinet-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FrankaCabinet-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 16 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 8 # 16 * 4096 / 8192 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 5e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 2.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01 cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```
Winmodel/Taxi-v3	Winmodel	2023-07-10T13:32:37Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-10T13:32:35Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Winmodel/Taxi-v3", 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"]) ```
JesseJr/distilbert-base-uncased-finetuned-cola	JesseJr	2023-07-10T13:32:08Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T13:27:38Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: JesseJr/distilbert-base-uncased-finetuned-cola 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. --> # JesseJr/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2071 - Validation Loss: 0.5352 - Train Matthews Correlation: 0.5089 - Epoch: 2 ## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5299 \| 0.4596 \| 0.4739 \| 0 \| \| 0.3386 \| 0.4643 \| 0.5152 \| 1 \| \| 0.2071 \| 0.5352 \| 0.5089 \| 2 \| ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
AladarMezga/detr-resnet-50_finetuned_cppe5	AladarMezga	2023-07-10T13:26:52Z	192	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-07-10T12:06:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 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. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 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: 10 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7

jordyvl

2023-07-11T01:00:28Z

163

0

transformers

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

image-classification

2023-07-10T23:46:12Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 results: [] ---  # vit-small_rvl_cdip_100_examples_per_class_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2544 - Accuracy: 0.6375 - Brier Loss: 0.4805 - Nll: 3.0517 - F1 Micro: 0.6375 - F1 Macro: 0.6394 - Ece: 0.1654 - Aurc: 0.1376 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 3.2176 | 0.1275 | 0.9297 | 15.5568 | 0.1275 | 0.1255 | 0.1544 | 0.8595 | | No log | 2.0 | 50 | 2.4392 | 0.405 | 0.7503 | 9.6083 | 0.405 | 0.3723 | 0.1816 | 0.3640 | | No log | 3.0 | 75 | 1.9211 | 0.5025 | 0.6287 | 5.6023 | 0.5025 | 0.4930 | 0.1991 | 0.2451 | | No log | 4.0 | 100 | 1.7474 | 0.5375 | 0.5956 | 4.5712 | 0.5375 | 0.5387 | 0.1677 | 0.2244 | | No log | 5.0 | 125 | 1.7107 | 0.535 | 0.6051 | 4.3431 | 0.535 | 0.5180 | 0.1796 | 0.2269 | | No log | 6.0 | 150 | 1.7144 | 0.545 | 0.5988 | 3.6699 | 0.545 | 0.5455 | 0.1918 | 0.2253 | | No log | 7.0 | 175 | 1.9096 | 0.5625 | 0.6262 | 4.6856 | 0.5625 | 0.5459 | 0.1966 | 0.2362 | | No log | 8.0 | 200 | 1.6325 | 0.575 | 0.5815 | 3.9279 | 0.575 | 0.5705 | 0.1893 | 0.2026 | | No log | 9.0 | 225 | 1.8268 | 0.56 | 0.6088 | 4.5140 | 0.56 | 0.5482 | 0.1976 | 0.2213 | | No log | 10.0 | 250 | 1.9253 | 0.5575 | 0.6493 | 4.2860 | 0.5575 | 0.5427 | 0.2286 | 0.2445 | | No log | 11.0 | 275 | 1.6941 | 0.5725 | 0.5940 | 3.9317 | 0.5725 | 0.5827 | 0.2019 | 0.2232 | | No log | 12.0 | 300 | 1.8197 | 0.5575 | 0.6138 | 4.7928 | 0.5575 | 0.5476 | 0.2079 | 0.2240 | | No log | 13.0 | 325 | 1.8958 | 0.54 | 0.6508 | 4.2978 | 0.54 | 0.5338 | 0.2379 | 0.2357 | | No log | 14.0 | 350 | 1.8939 | 0.535 | 0.6522 | 4.5557 | 0.535 | 0.5143 | 0.2324 | 0.2350 | | No log | 15.0 | 375 | 1.8018 | 0.585 | 0.6042 | 4.4728 | 0.585 | 0.5829 | 0.2205 | 0.2182 | | No log | 16.0 | 400 | 1.7645 | 0.5975 | 0.5978 | 3.9939 | 0.5975 | 0.5992 | 0.2130 | 0.1927 | | No log | 17.0 | 425 | 1.6392 | 0.5925 | 0.5842 | 3.6783 | 0.5925 | 0.6039 | 0.1986 | 0.2017 | | No log | 18.0 | 450 | 1.6124 | 0.5875 | 0.5761 | 4.0535 | 0.5875 | 0.5721 | 0.2060 | 0.1792 | | No log | 19.0 | 475 | 1.7517 | 0.585 | 0.6102 | 3.9076 | 0.585 | 0.5786 | 0.2082 | 0.2071 | | 0.6436 | 20.0 | 500 | 1.7467 | 0.5575 | 0.6166 | 3.5052 | 0.5575 | 0.5476 | 0.2252 | 0.2247 | | 0.6436 | 21.0 | 525 | 1.6719 | 0.5825 | 0.5745 | 4.1235 | 0.5825 | 0.5877 | 0.1831 | 0.1723 | | 0.6436 | 22.0 | 550 | 1.4222 | 0.605 | 0.5237 | 3.2051 | 0.605 | 0.6083 | 0.1813 | 0.1559 | | 0.6436 | 23.0 | 575 | 1.6436 | 0.595 | 0.5701 | 4.3949 | 0.595 | 0.5834 | 0.1921 | 0.1901 | | 0.6436 | 24.0 | 600 | 1.4244 | 0.6075 | 0.5197 | 3.3207 | 0.6075 | 0.6100 | 0.1548 | 0.1616 | | 0.6436 | 25.0 | 625 | 1.4567 | 0.6075 | 0.5356 | 3.5288 | 0.6075 | 0.6107 | 0.1768 | 0.1652 | | 0.6436 | 26.0 | 650 | 1.5889 | 0.595 | 0.5587 | 4.1521 | 0.595 | 0.5907 | 0.1943 | 0.1768 | | 0.6436 | 27.0 | 675 | 1.4828 | 0.5725 | 0.5532 | 3.4259 | 0.5725 | 0.5720 | 0.2125 | 0.1803 | | 0.6436 | 28.0 | 700 | 1.4671 | 0.5975 | 0.5509 | 3.2612 | 0.5975 | 0.6006 | 0.1983 | 0.1797 | | 0.6436 | 29.0 | 725 | 1.4049 | 0.6225 | 0.5273 | 3.3136 | 0.6225 | 0.6237 | 0.1995 | 0.1600 | | 0.6436 | 30.0 | 750 | 1.4039 | 0.6175 | 0.5208 | 3.2588 | 0.6175 | 0.6063 | 0.1770 | 0.1534 | | 0.6436 | 31.0 | 775 | 1.4333 | 0.6 | 0.5378 | 3.6417 | 0.6 | 0.5995 | 0.1899 | 0.1632 | | 0.6436 | 32.0 | 800 | 1.3311 | 0.64 | 0.5032 | 3.0056 | 0.64 | 0.6394 | 0.1699 | 0.1476 | | 0.6436 | 33.0 | 825 | 1.3361 | 0.61 | 0.5079 | 3.2304 | 0.61 | 0.6123 | 0.1536 | 0.1517 | | 0.6436 | 34.0 | 850 | 1.2984 | 0.64 | 0.4982 | 3.1446 | 0.64 | 0.6444 | 0.1636 | 0.1424 | | 0.6436 | 35.0 | 875 | 1.3153 | 0.6275 | 0.4995 | 3.0722 | 0.6275 | 0.6288 | 0.1634 | 0.1486 | | 0.6436 | 36.0 | 900 | 1.2773 | 0.6375 | 0.4880 | 2.7136 | 0.6375 | 0.6422 | 0.1606 | 0.1411 | | 0.6436 | 37.0 | 925 | 1.2881 | 0.64 | 0.4946 | 3.0452 | 0.64 | 0.6437 | 0.1732 | 0.1440 | | 0.6436 | 38.0 | 950 | 1.2609 | 0.64 | 0.4824 | 2.7407 | 0.64 | 0.6430 | 0.1485 | 0.1424 | | 0.6436 | 39.0 | 975 | 1.2685 | 0.645 | 0.4869 | 2.7203 | 0.645 | 0.6484 | 0.1680 | 0.1398 | | 0.0861 | 40.0 | 1000 | 1.2546 | 0.635 | 0.4808 | 2.7042 | 0.635 | 0.6356 | 0.1669 | 0.1416 | | 0.0861 | 41.0 | 1025 | 1.2599 | 0.6425 | 0.4858 | 2.6880 | 0.6425 | 0.6457 | 0.1539 | 0.1387 | | 0.0861 | 42.0 | 1050 | 1.2413 | 0.635 | 0.4783 | 2.8343 | 0.635 | 0.6361 | 0.1679 | 0.1369 | | 0.0861 | 43.0 | 1075 | 1.2670 | 0.6325 | 0.4901 | 2.8366 | 0.6325 | 0.6337 | 0.1501 | 0.1399 | | 0.0861 | 44.0 | 1100 | 1.2793 | 0.63 | 0.4919 | 3.1711 | 0.63 | 0.6309 | 0.1672 | 0.1465 | | 0.0861 | 45.0 | 1125 | 1.2555 | 0.635 | 0.4844 | 2.9284 | 0.635 | 0.6379 | 0.1791 | 0.1401 | | 0.0861 | 46.0 | 1150 | 1.2491 | 0.635 | 0.4806 | 2.8475 | 0.635 | 0.6358 | 0.1611 | 0.1392 | | 0.0861 | 47.0 | 1175 | 1.2533 | 0.6325 | 0.4837 | 2.8229 | 0.6325 | 0.6352 | 0.1623 | 0.1378 | | 0.0861 | 48.0 | 1200 | 1.2602 | 0.635 | 0.4857 | 2.9963 | 0.635 | 0.6368 | 0.1535 | 0.1426 | | 0.0861 | 49.0 | 1225 | 1.2598 | 0.635 | 0.4848 | 2.8569 | 0.635 | 0.6370 | 0.1718 | 0.1389 | | 0.0861 | 50.0 | 1250 | 1.2577 | 0.6225 | 0.4839 | 2.8645 | 0.6225 | 0.6237 | 0.1678 | 0.1420 | | 0.0861 | 51.0 | 1275 | 1.2547 | 0.63 | 0.4817 | 2.8344 | 0.63 | 0.6314 | 0.1721 | 0.1399 | | 0.0861 | 52.0 | 1300 | 1.2525 | 0.64 | 0.4819 | 2.7720 | 0.64 | 0.6411 | 0.1567 | 0.1378 | | 0.0861 | 53.0 | 1325 | 1.2627 | 0.6325 | 0.4854 | 2.9202 | 0.6325 | 0.6337 | 0.1688 | 0.1406 | | 0.0861 | 54.0 | 1350 | 1.2565 | 0.63 | 0.4836 | 2.8392 | 0.63 | 0.6320 | 0.1612 | 0.1404 | | 0.0861 | 55.0 | 1375 | 1.2514 | 0.6325 | 0.4813 | 2.9887 | 0.6325 | 0.6343 | 0.1652 | 0.1386 | | 0.0861 | 56.0 | 1400 | 1.2541 | 0.6275 | 0.4822 | 2.9067 | 0.6275 | 0.6296 | 0.1649 | 0.1401 | | 0.0861 | 57.0 | 1425 | 1.2529 | 0.64 | 0.4810 | 2.9166 | 0.64 | 0.6432 | 0.1765 | 0.1372 | | 0.0861 | 58.0 | 1450 | 1.2464 | 0.6275 | 0.4799 | 2.9713 | 0.6275 | 0.6291 | 0.1653 | 0.1401 | | 0.0861 | 59.0 | 1475 | 1.2576 | 0.63 | 0.4826 | 2.9124 | 0.63 | 0.6323 | 0.1557 | 0.1397 | | 0.0496 | 60.0 | 1500 | 1.2494 | 0.63 | 0.4804 | 2.8355 | 0.63 | 0.6317 | 0.1672 | 0.1390 | | 0.0496 | 61.0 | 1525 | 1.2496 | 0.6325 | 0.4803 | 2.9091 | 0.6325 | 0.6352 | 0.1510 | 0.1383 | | 0.0496 | 62.0 | 1550 | 1.2592 | 0.6375 | 0.4838 | 2.8980 | 0.6375 | 0.6384 | 0.1758 | 0.1398 | | 0.0496 | 63.0 | 1575 | 1.2504 | 0.63 | 0.4806 | 2.9843 | 0.63 | 0.6316 | 0.1691 | 0.1391 | | 0.0496 | 64.0 | 1600 | 1.2528 | 0.6325 | 0.4810 | 2.9045 | 0.6325 | 0.6349 | 0.1737 | 0.1388 | | 0.0496 | 65.0 | 1625 | 1.2589 | 0.6425 | 0.4833 | 2.9817 | 0.6425 | 0.6447 | 0.1719 | 0.1380 | | 0.0496 | 66.0 | 1650 | 1.2531 | 0.63 | 0.4811 | 2.9027 | 0.63 | 0.6321 | 0.1751 | 0.1391 | | 0.0496 | 67.0 | 1675 | 1.2520 | 0.635 | 0.4808 | 2.9794 | 0.635 | 0.6379 | 0.1715 | 0.1378 | | 0.0496 | 68.0 | 1700 | 1.2543 | 0.64 | 0.4815 | 2.9771 | 0.64 | 0.6420 | 0.1562 | 0.1380 | | 0.0496 | 69.0 | 1725 | 1.2538 | 0.6325 | 0.4808 | 2.9080 | 0.6325 | 0.6345 | 0.1681 | 0.1385 | | 0.0496 | 70.0 | 1750 | 1.2543 | 0.6325 | 0.4813 | 2.9102 | 0.6325 | 0.6347 | 0.1725 | 0.1390 | | 0.0496 | 71.0 | 1775 | 1.2534 | 0.6325 | 0.4809 | 2.9778 | 0.6325 | 0.6353 | 0.1495 | 0.1385 | | 0.0496 | 72.0 | 1800 | 1.2539 | 0.6375 | 0.4809 | 2.9024 | 0.6375 | 0.6394 | 0.1588 | 0.1381 | | 0.0496 | 73.0 | 1825 | 1.2531 | 0.635 | 0.4806 | 2.9812 | 0.635 | 0.6378 | 0.1552 | 0.1380 | | 0.0496 | 74.0 | 1850 | 1.2531 | 0.635 | 0.4805 | 2.9783 | 0.635 | 0.6377 | 0.1700 | 0.1380 | | 0.0496 | 75.0 | 1875 | 1.2533 | 0.6375 | 0.4809 | 2.9772 | 0.6375 | 0.6400 | 0.1645 | 0.1372 | | 0.0496 | 76.0 | 1900 | 1.2539 | 0.6375 | 0.4808 | 2.9777 | 0.6375 | 0.6393 | 0.1675 | 0.1376 | | 0.0496 | 77.0 | 1925 | 1.2537 | 0.635 | 0.4808 | 2.9832 | 0.635 | 0.6375 | 0.1648 | 0.1381 | | 0.0496 | 78.0 | 1950 | 1.2539 | 0.6375 | 0.4807 | 2.9769 | 0.6375 | 0.6394 | 0.1636 | 0.1374 | | 0.0496 | 79.0 | 1975 | 1.2534 | 0.6375 | 0.4805 | 2.9796 | 0.6375 | 0.6399 | 0.1599 | 0.1375 | | 0.048 | 80.0 | 2000 | 1.2537 | 0.6375 | 0.4806 | 3.0539 | 0.6375 | 0.6399 | 0.1657 | 0.1375 | | 0.048 | 81.0 | 2025 | 1.2535 | 0.6375 | 0.4805 | 3.0534 | 0.6375 | 0.6399 | 0.1728 | 0.1375 | | 0.048 | 82.0 | 2050 | 1.2539 | 0.6375 | 0.4806 | 2.9831 | 0.6375 | 0.6393 | 0.1674 | 0.1375 | | 0.048 | 83.0 | 2075 | 1.2542 | 0.6375 | 0.4807 | 3.0538 | 0.6375 | 0.6399 | 0.1674 | 0.1375 | | 0.048 | 84.0 | 2100 | 1.2539 | 0.6375 | 0.4805 | 3.0531 | 0.6375 | 0.6394 | 0.1564 | 0.1375 | | 0.048 | 85.0 | 2125 | 1.2542 | 0.6375 | 0.4806 | 3.0531 | 0.6375 | 0.6393 | 0.1676 | 0.1376 | | 0.048 | 86.0 | 2150 | 1.2541 | 0.6375 | 0.4806 | 3.0527 | 0.6375 | 0.6399 | 0.1691 | 0.1375 | | 0.048 | 87.0 | 2175 | 1.2542 | 0.6375 | 0.4805 | 3.0525 | 0.6375 | 0.6394 | 0.1677 | 0.1376 | | 0.048 | 88.0 | 2200 | 1.2542 | 0.6375 | 0.4806 | 3.0525 | 0.6375 | 0.6393 | 0.1651 | 0.1375 | | 0.048 | 89.0 | 2225 | 1.2543 | 0.6375 | 0.4805 | 3.0525 | 0.6375 | 0.6394 | 0.1601 | 0.1375 | | 0.048 | 90.0 | 2250 | 1.2543 | 0.6375 | 0.4805 | 3.0521 | 0.6375 | 0.6394 | 0.1661 | 0.1375 | | 0.048 | 91.0 | 2275 | 1.2541 | 0.6375 | 0.4805 | 3.0521 | 0.6375 | 0.6394 | 0.1665 | 0.1376 | | 0.048 | 92.0 | 2300 | 1.2542 | 0.6375 | 0.4805 | 3.0521 | 0.6375 | 0.6394 | 0.1638 | 0.1375 | | 0.048 | 93.0 | 2325 | 1.2544 | 0.6375 | 0.4805 | 3.0518 | 0.6375 | 0.6394 | 0.1671 | 0.1376 | | 0.048 | 94.0 | 2350 | 1.2543 | 0.6375 | 0.4805 | 3.0519 | 0.6375 | 0.6394 | 0.1601 | 0.1376 | | 0.048 | 95.0 | 2375 | 1.2544 | 0.6375 | 0.4805 | 3.0518 | 0.6375 | 0.6394 | 0.1638 | 0.1376 | | 0.048 | 96.0 | 2400 | 1.2544 | 0.6375 | 0.4805 | 3.0518 | 0.6375 | 0.6394 | 0.1638 | 0.1376 | | 0.048 | 97.0 | 2425 | 1.2544 | 0.6375 | 0.4805 | 3.0517 | 0.6375 | 0.6394 | 0.1655 | 0.1376 | | 0.048 | 98.0 | 2450 | 1.2544 | 0.6375 | 0.4805 | 3.0517 | 0.6375 | 0.6394 | 0.1638 | 0.1376 | | 0.048 | 99.0 | 2475 | 1.2544 | 0.6375 | 0.4805 | 3.0517 | 0.6375 | 0.6394 | 0.1654 | 0.1376 | | 0.0478 | 100.0 | 2500 | 1.2544 | 0.6375 | 0.4805 | 3.0517 | 0.6375 | 0.6394 | 0.1654 | 0.1376 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

MaitreHibou/ppo-SnowballTarget

MaitreHibou

2023-07-11T01:00:11Z

16

0

ml-agents

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

reinforcement-learning

2023-07-11T01:00:06Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: MaitreHibou/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

hopkins/strict-small-4

hopkins

2023-07-11T00:43:51Z

5

0

transformers

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

text-generation

2023-06-13T21:25:31Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: strict-small-4 results: [] ---  # strict-small-4 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 3.8588 ## 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: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 9 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.9925 | 1.83 | 1000 | 4.2033 | | 3.7647 | 3.67 | 2000 | 3.9152 | | 3.3569 | 5.5 | 3000 | 3.8495 | | 3.0079 | 7.34 | 4000 | 3.8588 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

casque/CrystalMaidenv0.2

casque

2023-07-11T00:42:48Z

0

null

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

null

2023-07-11T00:39:34Z

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

ALM-AHME/swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH

ALM-AHME

2023-07-11T00:40:15Z

5

1

transformers

[ "transformers", "pytorch", "tensorboard", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T02:43:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: Augmented-Final split: train args: Augmented-Final metrics: - name: Accuracy type: accuracy value: 1.0 ---  # swinv2-large-patch4-window12to16-192to256-22kto1k-ft-finetuned-LungCancer-LC25000-AH This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0929 | 1.0 | 281 | 0.0919 | 0.9657 | | 0.0908 | 2.0 | 562 | 0.0127 | 0.9967 | | 0.0525 | 3.0 | 843 | 0.0133 | 0.9947 | | 0.1301 | 4.0 | 1125 | 0.0270 | 0.9927 | | 0.0624 | 5.0 | 1406 | 0.0064 | 0.9973 | | 0.0506 | 6.0 | 1687 | 0.0025 | 0.999 | | 0.0001 | 6.99 | 1967 | 0.0002 | 1.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

foster123/test

foster123

2023-07-11T00:39:29Z

2

0

peft

[ "peft", "region:us" ]

null

2023-07-10T06:23:46Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

layoric/openllama-7b-qlora-orca

layoric

2023-07-11T00:31:19Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-09T23:58:03Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

bobobert4/poca-SoccerTwos

bobobert4

2023-07-11T00:18:04Z

5

0

ml-agents

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

reinforcement-learning

2023-07-11T00:16:06Z

--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bobobert4/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jz0214/sd-class-butterflies-64

jz0214

2023-07-10T23:52:24Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-07-10T23:50:42Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jz0214/sd-class-butterflies-64') image = pipeline().images[0] image ```

shenyichong/ppo-LunarLander-v2

shenyichong

2023-07-10T23:37:07Z

0

stable-baselines3

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

reinforcement-learning

2023-07-10T23:36:50Z

--- 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: 263.84 +/- 7.90 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 ... ```

JBJoyce/whisper-large-v2-finetuned-gtzan

JBJoyce

2023-07-10T23:32:02Z

3

0

transformers

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

audio-classification

2023-07-10T19:35:24Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-large-v2-finetuned-gtzan results: [] ---  # whisper-large-v2-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.7142 - Accuracy: 0.9 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0464 | 1.0 | 449 | 1.6761 | 0.42 | | 0.9369 | 2.0 | 899 | 1.0398 | 0.74 | | 1.0591 | 3.0 | 1348 | 1.0710 | 0.78 | | 0.0632 | 4.0 | 1798 | 0.6605 | 0.86 | | 0.0022 | 5.0 | 2247 | 1.0940 | 0.82 | | 0.0004 | 6.0 | 2697 | 0.7089 | 0.92 | | 0.0004 | 7.0 | 3146 | 0.6176 | 0.92 | | 0.0005 | 8.0 | 3596 | 0.6688 | 0.9 | | 0.0002 | 9.0 | 4045 | 0.7052 | 0.9 | | 0.0002 | 9.99 | 4490 | 0.7142 | 0.9 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jz0214/sd-class-butterflies-32

jz0214

2023-07-10T23:09:47Z

30

0

diffusers

[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-07-10T23:08:46Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jz0214/sd-class-butterflies-32') image = pipeline().images[0] image ```

wesley7137/fal-7B-shard-quantum

wesley7137

2023-07-10T22:53:05Z

0

peft

[ "peft", "pytorch", "RefinedWebModel", "custom_code", "region:us" ]

null

2023-07-10T22:04:14Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0.dev0 - PEFT 0.4.0.dev0

jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.9

jordyvl

2023-07-10T22:40:13Z

161

0

transformers

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

image-classification

2023-07-10T22:00:19Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t5.0_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5373 - Accuracy: 0.85 - Brier Loss: 0.2432 - Nll: 1.1157 - F1 Micro: 0.85 - F1 Macro: 0.8450 - Ece: 0.1621 - Aurc: 0.0427 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.1036 | 0.215 | 0.8753 | 5.3195 | 0.2150 | 0.1264 | 0.2571 | 0.6923 | | No log | 2.0 | 14 | 1.6952 | 0.405 | 0.7407 | 3.4929 | 0.405 | 0.2416 | 0.2907 | 0.4040 | | No log | 3.0 | 21 | 1.1843 | 0.62 | 0.5633 | 2.0113 | 0.62 | 0.5725 | 0.2740 | 0.2014 | | No log | 4.0 | 28 | 0.8797 | 0.71 | 0.4080 | 1.7043 | 0.7100 | 0.6683 | 0.2024 | 0.1125 | | No log | 5.0 | 35 | 0.8570 | 0.715 | 0.3837 | 1.6476 | 0.715 | 0.7280 | 0.2189 | 0.1079 | | No log | 6.0 | 42 | 0.7484 | 0.775 | 0.3285 | 1.5962 | 0.775 | 0.7668 | 0.1873 | 0.0816 | | No log | 7.0 | 49 | 0.7337 | 0.79 | 0.3131 | 1.5377 | 0.79 | 0.7779 | 0.1904 | 0.0771 | | No log | 8.0 | 56 | 0.6709 | 0.795 | 0.3012 | 1.2156 | 0.795 | 0.7776 | 0.1939 | 0.0761 | | No log | 9.0 | 63 | 0.6901 | 0.795 | 0.3069 | 1.4725 | 0.795 | 0.7916 | 0.1882 | 0.0769 | | No log | 10.0 | 70 | 0.7960 | 0.75 | 0.3586 | 1.4426 | 0.75 | 0.7406 | 0.1868 | 0.0976 | | No log | 11.0 | 77 | 0.7489 | 0.77 | 0.3296 | 1.6202 | 0.7700 | 0.7794 | 0.2020 | 0.0878 | | No log | 12.0 | 84 | 0.7068 | 0.785 | 0.3270 | 1.4127 | 0.785 | 0.7812 | 0.1922 | 0.0759 | | No log | 13.0 | 91 | 0.6687 | 0.79 | 0.3050 | 1.3820 | 0.79 | 0.7945 | 0.1818 | 0.0625 | | No log | 14.0 | 98 | 0.6052 | 0.79 | 0.2854 | 1.0602 | 0.79 | 0.7716 | 0.1702 | 0.0590 | | No log | 15.0 | 105 | 0.6369 | 0.795 | 0.2959 | 1.0580 | 0.795 | 0.7953 | 0.1709 | 0.0603 | | No log | 16.0 | 112 | 0.6204 | 0.81 | 0.2816 | 1.1886 | 0.81 | 0.8050 | 0.1657 | 0.0702 | | No log | 17.0 | 119 | 0.5648 | 0.83 | 0.2475 | 1.2506 | 0.83 | 0.8241 | 0.1347 | 0.0612 | | No log | 18.0 | 126 | 0.5849 | 0.83 | 0.2672 | 1.2245 | 0.83 | 0.8155 | 0.1646 | 0.0601 | | No log | 19.0 | 133 | 0.5536 | 0.835 | 0.2475 | 1.0514 | 0.835 | 0.8254 | 0.1683 | 0.0531 | | No log | 20.0 | 140 | 0.5689 | 0.835 | 0.2513 | 1.2369 | 0.835 | 0.8437 | 0.1722 | 0.0489 | | No log | 21.0 | 147 | 0.5540 | 0.83 | 0.2485 | 1.2139 | 0.83 | 0.8165 | 0.1641 | 0.0608 | | No log | 22.0 | 154 | 0.5352 | 0.835 | 0.2402 | 1.0108 | 0.835 | 0.8295 | 0.1408 | 0.0430 | | No log | 23.0 | 161 | 0.5380 | 0.84 | 0.2403 | 1.2280 | 0.8400 | 0.8347 | 0.1405 | 0.0436 | | No log | 24.0 | 168 | 0.5422 | 0.835 | 0.2471 | 1.0204 | 0.835 | 0.8324 | 0.1606 | 0.0445 | | No log | 25.0 | 175 | 0.5342 | 0.85 | 0.2404 | 1.0767 | 0.85 | 0.8487 | 0.1469 | 0.0432 | | No log | 26.0 | 182 | 0.5374 | 0.84 | 0.2429 | 1.0774 | 0.8400 | 0.8334 | 0.1420 | 0.0462 | | No log | 27.0 | 189 | 0.5311 | 0.85 | 0.2395 | 1.0748 | 0.85 | 0.8487 | 0.1439 | 0.0446 | | No log | 28.0 | 196 | 0.5298 | 0.85 | 0.2384 | 1.1337 | 0.85 | 0.8487 | 0.1570 | 0.0437 | | No log | 29.0 | 203 | 0.5387 | 0.845 | 0.2435 | 1.1319 | 0.845 | 0.8424 | 0.1539 | 0.0458 | | No log | 30.0 | 210 | 0.5361 | 0.85 | 0.2430 | 1.0648 | 0.85 | 0.8450 | 0.1679 | 0.0431 | | No log | 31.0 | 217 | 0.5339 | 0.85 | 0.2413 | 1.0676 | 0.85 | 0.8487 | 0.1646 | 0.0428 | | No log | 32.0 | 224 | 0.5345 | 0.85 | 0.2421 | 1.0709 | 0.85 | 0.8487 | 0.1476 | 0.0440 | | No log | 33.0 | 231 | 0.5343 | 0.85 | 0.2421 | 1.1236 | 0.85 | 0.8450 | 0.1621 | 0.0431 | | No log | 34.0 | 238 | 0.5353 | 0.845 | 0.2426 | 1.1244 | 0.845 | 0.8424 | 0.1710 | 0.0428 | | No log | 35.0 | 245 | 0.5346 | 0.85 | 0.2423 | 1.0649 | 0.85 | 0.8487 | 0.1520 | 0.0440 | | No log | 36.0 | 252 | 0.5356 | 0.855 | 0.2422 | 1.1241 | 0.855 | 0.8517 | 0.1814 | 0.0429 | | No log | 37.0 | 259 | 0.5357 | 0.85 | 0.2426 | 1.1237 | 0.85 | 0.8450 | 0.1670 | 0.0425 | | No log | 38.0 | 266 | 0.5356 | 0.845 | 0.2426 | 1.1226 | 0.845 | 0.8419 | 0.1607 | 0.0435 | | No log | 39.0 | 273 | 0.5347 | 0.855 | 0.2420 | 1.0739 | 0.855 | 0.8517 | 0.1597 | 0.0427 | | No log | 40.0 | 280 | 0.5356 | 0.855 | 0.2423 | 1.1203 | 0.855 | 0.8517 | 0.1676 | 0.0435 | | No log | 41.0 | 287 | 0.5365 | 0.85 | 0.2431 | 1.1199 | 0.85 | 0.8450 | 0.1780 | 0.0429 | | No log | 42.0 | 294 | 0.5356 | 0.85 | 0.2426 | 1.1173 | 0.85 | 0.8450 | 0.1653 | 0.0430 | | No log | 43.0 | 301 | 0.5363 | 0.85 | 0.2428 | 1.1189 | 0.85 | 0.8450 | 0.1550 | 0.0435 | | No log | 44.0 | 308 | 0.5345 | 0.85 | 0.2418 | 1.1193 | 0.85 | 0.8450 | 0.1590 | 0.0428 | | No log | 45.0 | 315 | 0.5374 | 0.85 | 0.2435 | 1.1202 | 0.85 | 0.8450 | 0.1633 | 0.0435 | | No log | 46.0 | 322 | 0.5355 | 0.85 | 0.2423 | 1.1183 | 0.85 | 0.8450 | 0.1564 | 0.0428 | | No log | 47.0 | 329 | 0.5354 | 0.85 | 0.2425 | 1.1176 | 0.85 | 0.8450 | 0.1509 | 0.0429 | | No log | 48.0 | 336 | 0.5369 | 0.85 | 0.2433 | 1.1177 | 0.85 | 0.8450 | 0.1517 | 0.0432 | | No log | 49.0 | 343 | 0.5361 | 0.85 | 0.2428 | 1.1182 | 0.85 | 0.8450 | 0.1490 | 0.0428 | | No log | 50.0 | 350 | 0.5364 | 0.85 | 0.2431 | 1.1179 | 0.85 | 0.8450 | 0.1654 | 0.0430 | | No log | 51.0 | 357 | 0.5365 | 0.85 | 0.2428 | 1.1185 | 0.85 | 0.8450 | 0.1729 | 0.0432 | | No log | 52.0 | 364 | 0.5364 | 0.85 | 0.2430 | 1.1165 | 0.85 | 0.8450 | 0.1614 | 0.0429 | | No log | 53.0 | 371 | 0.5362 | 0.85 | 0.2429 | 1.1167 | 0.85 | 0.8450 | 0.1694 | 0.0430 | | No log | 54.0 | 378 | 0.5369 | 0.85 | 0.2432 | 1.1170 | 0.85 | 0.8450 | 0.1597 | 0.0432 | | No log | 55.0 | 385 | 0.5368 | 0.85 | 0.2430 | 1.1168 | 0.85 | 0.8450 | 0.1670 | 0.0429 | | No log | 56.0 | 392 | 0.5367 | 0.85 | 0.2430 | 1.1180 | 0.85 | 0.8450 | 0.1619 | 0.0430 | | No log | 57.0 | 399 | 0.5364 | 0.85 | 0.2429 | 1.1163 | 0.85 | 0.8450 | 0.1649 | 0.0429 | | No log | 58.0 | 406 | 0.5364 | 0.85 | 0.2430 | 1.1156 | 0.85 | 0.8450 | 0.1611 | 0.0429 | | No log | 59.0 | 413 | 0.5365 | 0.85 | 0.2428 | 1.1163 | 0.85 | 0.8450 | 0.1591 | 0.0429 | | No log | 60.0 | 420 | 0.5364 | 0.85 | 0.2429 | 1.1155 | 0.85 | 0.8450 | 0.1588 | 0.0429 | | No log | 61.0 | 427 | 0.5370 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1772 | 0.0432 | | No log | 62.0 | 434 | 0.5367 | 0.85 | 0.2429 | 1.1167 | 0.85 | 0.8450 | 0.1622 | 0.0429 | | No log | 63.0 | 441 | 0.5362 | 0.85 | 0.2428 | 1.1162 | 0.85 | 0.8450 | 0.1503 | 0.0428 | | No log | 64.0 | 448 | 0.5372 | 0.85 | 0.2433 | 1.1161 | 0.85 | 0.8450 | 0.1616 | 0.0432 | | No log | 65.0 | 455 | 0.5371 | 0.85 | 0.2431 | 1.1162 | 0.85 | 0.8450 | 0.1499 | 0.0429 | | No log | 66.0 | 462 | 0.5367 | 0.85 | 0.2430 | 1.1160 | 0.85 | 0.8450 | 0.1591 | 0.0427 | | No log | 67.0 | 469 | 0.5367 | 0.85 | 0.2430 | 1.1164 | 0.85 | 0.8450 | 0.1562 | 0.0428 | | No log | 68.0 | 476 | 0.5368 | 0.85 | 0.2430 | 1.1168 | 0.85 | 0.8450 | 0.1556 | 0.0427 | | No log | 69.0 | 483 | 0.5368 | 0.85 | 0.2431 | 1.1158 | 0.85 | 0.8450 | 0.1593 | 0.0428 | | No log | 70.0 | 490 | 0.5372 | 0.85 | 0.2432 | 1.1162 | 0.85 | 0.8450 | 0.1628 | 0.0428 | | No log | 71.0 | 497 | 0.5371 | 0.85 | 0.2432 | 1.1163 | 0.85 | 0.8450 | 0.1599 | 0.0429 | | 0.1708 | 72.0 | 504 | 0.5370 | 0.85 | 0.2430 | 1.1161 | 0.85 | 0.8450 | 0.1559 | 0.0430 | | 0.1708 | 73.0 | 511 | 0.5372 | 0.85 | 0.2433 | 1.1154 | 0.85 | 0.8450 | 0.1556 | 0.0428 | | 0.1708 | 74.0 | 518 | 0.5370 | 0.85 | 0.2429 | 1.1165 | 0.85 | 0.8450 | 0.1540 | 0.0428 | | 0.1708 | 75.0 | 525 | 0.5371 | 0.85 | 0.2431 | 1.1161 | 0.85 | 0.8450 | 0.1616 | 0.0427 | | 0.1708 | 76.0 | 532 | 0.5369 | 0.85 | 0.2431 | 1.1161 | 0.85 | 0.8450 | 0.1619 | 0.0427 | | 0.1708 | 77.0 | 539 | 0.5369 | 0.85 | 0.2430 | 1.1156 | 0.85 | 0.8450 | 0.1623 | 0.0429 | | 0.1708 | 78.0 | 546 | 0.5372 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1619 | 0.0427 | | 0.1708 | 79.0 | 553 | 0.5375 | 0.85 | 0.2433 | 1.1162 | 0.85 | 0.8450 | 0.1688 | 0.0429 | | 0.1708 | 80.0 | 560 | 0.5372 | 0.85 | 0.2432 | 1.1160 | 0.85 | 0.8450 | 0.1623 | 0.0429 | | 0.1708 | 81.0 | 567 | 0.5373 | 0.85 | 0.2432 | 1.1162 | 0.85 | 0.8450 | 0.1620 | 0.0428 | | 0.1708 | 82.0 | 574 | 0.5374 | 0.85 | 0.2433 | 1.1160 | 0.85 | 0.8450 | 0.1622 | 0.0428 | | 0.1708 | 83.0 | 581 | 0.5372 | 0.85 | 0.2432 | 1.1159 | 0.85 | 0.8450 | 0.1622 | 0.0428 | | 0.1708 | 84.0 | 588 | 0.5371 | 0.85 | 0.2431 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 85.0 | 595 | 0.5372 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1687 | 0.0426 | | 0.1708 | 86.0 | 602 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1619 | 0.0426 | | 0.1708 | 87.0 | 609 | 0.5374 | 0.85 | 0.2432 | 1.1159 | 0.85 | 0.8450 | 0.1687 | 0.0428 | | 0.1708 | 88.0 | 616 | 0.5373 | 0.85 | 0.2432 | 1.1160 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 89.0 | 623 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 90.0 | 630 | 0.5373 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 91.0 | 637 | 0.5372 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 92.0 | 644 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 93.0 | 651 | 0.5372 | 0.85 | 0.2432 | 1.1156 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 94.0 | 658 | 0.5373 | 0.85 | 0.2432 | 1.1158 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 95.0 | 665 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 96.0 | 672 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 97.0 | 679 | 0.5372 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1620 | 0.0427 | | 0.1708 | 98.0 | 686 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 99.0 | 693 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | | 0.1708 | 100.0 | 700 | 0.5373 | 0.85 | 0.2432 | 1.1157 | 0.85 | 0.8450 | 0.1621 | 0.0427 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

maidacundo/falcon_qlora_sql_r2

maidacundo

2023-07-10T22:30:14Z

0

null

[ "generated_from_trainer", "dataset:spider", "base_model:tiiuae/falcon-7b", "base_model:finetune:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]

null

2023-07-10T09:40:03Z

--- license: apache-2.0 base_model: tiiuae/falcon-7b tags: - generated_from_trainer datasets: - spider model-index: - name: falcon_qlora_sql_r2 results: [] ---  # falcon_qlora_sql_r2 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the spider dataset. It achieves the following results on the evaluation set: - Loss: 0.1735 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 43.7 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2993 | 0.23 | 100 | 0.2863 | | 0.8003 | 0.46 | 200 | 0.3358 | | 0.1872 | 0.68 | 300 | 0.2424 | | 0.1267 | 0.91 | 400 | 0.2362 | | 0.2214 | 1.14 | 500 | 0.2564 | | 0.2885 | 1.37 | 600 | 0.2187 | | 0.1654 | 1.6 | 700 | 0.1988 | | 0.1633 | 1.83 | 800 | 0.2062 | | 0.0381 | 2.05 | 900 | 0.1868 | | 0.0633 | 2.28 | 1000 | 0.1767 | | 0.163 | 2.51 | 1100 | 0.1861 | | 0.1718 | 2.74 | 1200 | 0.1875 | | 0.1743 | 2.97 | 1300 | 0.1854 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-small_rvl_cdip_100_examples_per_class_kd_MSE

jordyvl

2023-07-10T22:30:03Z

163

0

transformers

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

image-classification

2023-07-10T21:13:38Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_rvl_cdip_100_examples_per_class_kd_MSE results: [] ---  # vit-small_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4673 - Accuracy: 0.6425 - Brier Loss: 0.4763 - Nll: 3.0680 - F1 Micro: 0.6425 - F1 Macro: 0.6485 - Ece: 0.1946 - Aurc: 0.1381 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 4.4851 | 0.06 | 0.9565 | 13.8276 | 0.06 | 0.0556 | 0.1688 | 0.9385 | | No log | 2.0 | 50 | 3.5619 | 0.3775 | 0.7827 | 6.2649 | 0.3775 | 0.3611 | 0.2331 | 0.3882 | | No log | 3.0 | 75 | 2.8990 | 0.5025 | 0.6453 | 4.7376 | 0.5025 | 0.4858 | 0.1689 | 0.2658 | | No log | 4.0 | 100 | 2.5972 | 0.515 | 0.5980 | 4.4210 | 0.515 | 0.4895 | 0.1605 | 0.2249 | | No log | 5.0 | 125 | 2.4353 | 0.56 | 0.5762 | 3.4885 | 0.56 | 0.5566 | 0.1548 | 0.2100 | | No log | 6.0 | 150 | 2.4157 | 0.5475 | 0.5864 | 3.8261 | 0.5475 | 0.5323 | 0.1837 | 0.2167 | | No log | 7.0 | 175 | 2.1786 | 0.6075 | 0.5203 | 3.4565 | 0.6075 | 0.6103 | 0.1403 | 0.1670 | | No log | 8.0 | 200 | 2.1082 | 0.63 | 0.5040 | 3.3570 | 0.63 | 0.6246 | 0.1580 | 0.1530 | | No log | 9.0 | 225 | 2.0472 | 0.625 | 0.5042 | 3.8572 | 0.625 | 0.6184 | 0.1552 | 0.1530 | | No log | 10.0 | 250 | 2.0589 | 0.6025 | 0.5468 | 3.5723 | 0.6025 | 0.5982 | 0.1781 | 0.1785 | | No log | 11.0 | 275 | 1.8965 | 0.65 | 0.4755 | 3.4466 | 0.65 | 0.6497 | 0.1605 | 0.1475 | | No log | 12.0 | 300 | 1.9014 | 0.6325 | 0.5066 | 3.0881 | 0.6325 | 0.6359 | 0.1658 | 0.1591 | | No log | 13.0 | 325 | 1.7904 | 0.6175 | 0.5162 | 3.4673 | 0.6175 | 0.6141 | 0.1525 | 0.1598 | | No log | 14.0 | 350 | 1.8624 | 0.625 | 0.5173 | 3.6824 | 0.625 | 0.6179 | 0.1567 | 0.1624 | | No log | 15.0 | 375 | 1.7083 | 0.6625 | 0.4817 | 3.1296 | 0.6625 | 0.6686 | 0.1651 | 0.1405 | | No log | 16.0 | 400 | 1.8848 | 0.59 | 0.5478 | 4.3761 | 0.59 | 0.5913 | 0.2083 | 0.1696 | | No log | 17.0 | 425 | 1.7238 | 0.6125 | 0.5229 | 3.1232 | 0.6125 | 0.6052 | 0.1833 | 0.1553 | | No log | 18.0 | 450 | 1.7126 | 0.625 | 0.5152 | 2.9267 | 0.625 | 0.6284 | 0.1747 | 0.1565 | | No log | 19.0 | 475 | 1.6459 | 0.6275 | 0.5024 | 2.9078 | 0.6275 | 0.6219 | 0.1766 | 0.1527 | | 1.0542 | 20.0 | 500 | 1.6029 | 0.6275 | 0.4855 | 3.0931 | 0.6275 | 0.6316 | 0.1720 | 0.1414 | | 1.0542 | 21.0 | 525 | 1.6566 | 0.6525 | 0.4847 | 3.0998 | 0.6525 | 0.6479 | 0.1558 | 0.1438 | | 1.0542 | 22.0 | 550 | 1.6169 | 0.645 | 0.4894 | 3.0081 | 0.645 | 0.6471 | 0.1687 | 0.1400 | | 1.0542 | 23.0 | 575 | 1.5322 | 0.6525 | 0.4557 | 3.3587 | 0.6525 | 0.6520 | 0.1428 | 0.1247 | | 1.0542 | 24.0 | 600 | 1.5991 | 0.6475 | 0.4787 | 2.9349 | 0.6475 | 0.6444 | 0.1580 | 0.1450 | | 1.0542 | 25.0 | 625 | 1.5625 | 0.6375 | 0.4926 | 3.0245 | 0.6375 | 0.6378 | 0.1641 | 0.1433 | | 1.0542 | 26.0 | 650 | 1.5366 | 0.64 | 0.4884 | 3.3388 | 0.64 | 0.6461 | 0.1595 | 0.1453 | | 1.0542 | 27.0 | 675 | 1.5686 | 0.65 | 0.4765 | 3.5120 | 0.65 | 0.6504 | 0.1625 | 0.1359 | | 1.0542 | 28.0 | 700 | 1.5562 | 0.6475 | 0.4817 | 3.0348 | 0.6475 | 0.6488 | 0.1459 | 0.1388 | | 1.0542 | 29.0 | 725 | 1.5213 | 0.6475 | 0.4719 | 3.2628 | 0.6475 | 0.6475 | 0.1634 | 0.1326 | | 1.0542 | 30.0 | 750 | 1.5492 | 0.6675 | 0.4730 | 3.1693 | 0.6675 | 0.6679 | 0.1469 | 0.1415 | | 1.0542 | 31.0 | 775 | 1.5311 | 0.65 | 0.4896 | 3.0881 | 0.65 | 0.6504 | 0.1815 | 0.1380 | | 1.0542 | 32.0 | 800 | 1.5556 | 0.6475 | 0.4821 | 3.1829 | 0.6475 | 0.6491 | 0.1640 | 0.1405 | | 1.0542 | 33.0 | 825 | 1.5471 | 0.6375 | 0.4846 | 3.4190 | 0.6375 | 0.6407 | 0.1628 | 0.1415 | | 1.0542 | 34.0 | 850 | 1.4809 | 0.6575 | 0.4714 | 2.9136 | 0.6575 | 0.6612 | 0.1729 | 0.1338 | | 1.0542 | 35.0 | 875 | 1.5256 | 0.66 | 0.4773 | 3.2303 | 0.66 | 0.6650 | 0.1746 | 0.1368 | | 1.0542 | 36.0 | 900 | 1.4929 | 0.6675 | 0.4671 | 3.2360 | 0.6675 | 0.6698 | 0.1698 | 0.1309 | | 1.0542 | 37.0 | 925 | 1.4923 | 0.645 | 0.4880 | 3.0567 | 0.645 | 0.6564 | 0.1764 | 0.1395 | | 1.0542 | 38.0 | 950 | 1.5038 | 0.665 | 0.4672 | 3.2116 | 0.665 | 0.6661 | 0.1588 | 0.1343 | | 1.0542 | 39.0 | 975 | 1.4708 | 0.6625 | 0.4669 | 3.1420 | 0.6625 | 0.6675 | 0.1683 | 0.1301 | | 0.0522 | 40.0 | 1000 | 1.5153 | 0.6475 | 0.4865 | 3.1796 | 0.6475 | 0.6447 | 0.1639 | 0.1400 | | 0.0522 | 41.0 | 1025 | 1.4705 | 0.6575 | 0.4642 | 3.2196 | 0.6575 | 0.6626 | 0.1440 | 0.1308 | | 0.0522 | 42.0 | 1050 | 1.4844 | 0.6575 | 0.4722 | 3.2445 | 0.6575 | 0.6595 | 0.1746 | 0.1328 | | 0.0522 | 43.0 | 1075 | 1.4957 | 0.6425 | 0.4828 | 3.1456 | 0.6425 | 0.6468 | 0.1499 | 0.1417 | | 0.0522 | 44.0 | 1100 | 1.5179 | 0.645 | 0.4910 | 3.3921 | 0.645 | 0.6470 | 0.1861 | 0.1433 | | 0.0522 | 45.0 | 1125 | 1.4878 | 0.6425 | 0.4839 | 3.2139 | 0.6425 | 0.6478 | 0.1720 | 0.1403 | | 0.0522 | 46.0 | 1150 | 1.4666 | 0.655 | 0.4741 | 2.9333 | 0.655 | 0.6601 | 0.1813 | 0.1347 | | 0.0522 | 47.0 | 1175 | 1.4954 | 0.6575 | 0.4776 | 3.2102 | 0.6575 | 0.6604 | 0.1842 | 0.1390 | | 0.0522 | 48.0 | 1200 | 1.4976 | 0.645 | 0.4856 | 3.1539 | 0.645 | 0.6493 | 0.1549 | 0.1407 | | 0.0522 | 49.0 | 1225 | 1.4772 | 0.64 | 0.4780 | 2.9845 | 0.64 | 0.6445 | 0.1826 | 0.1388 | | 0.0522 | 50.0 | 1250 | 1.4584 | 0.65 | 0.4703 | 3.0776 | 0.65 | 0.6533 | 0.1685 | 0.1352 | | 0.0522 | 51.0 | 1275 | 1.4828 | 0.6325 | 0.4844 | 3.1425 | 0.6325 | 0.6377 | 0.1641 | 0.1409 | | 0.0522 | 52.0 | 1300 | 1.4676 | 0.6525 | 0.4737 | 3.1483 | 0.6525 | 0.6565 | 0.1773 | 0.1358 | | 0.0522 | 53.0 | 1325 | 1.4675 | 0.6475 | 0.4791 | 3.1411 | 0.6475 | 0.6515 | 0.1820 | 0.1388 | | 0.0522 | 54.0 | 1350 | 1.4724 | 0.645 | 0.4764 | 3.0744 | 0.645 | 0.6499 | 0.1847 | 0.1382 | | 0.0522 | 55.0 | 1375 | 1.4689 | 0.6425 | 0.4769 | 3.2256 | 0.6425 | 0.6476 | 0.1839 | 0.1376 | | 0.0522 | 56.0 | 1400 | 1.4660 | 0.6425 | 0.4760 | 2.9907 | 0.6425 | 0.6479 | 0.1906 | 0.1378 | | 0.0522 | 57.0 | 1425 | 1.4663 | 0.645 | 0.4757 | 3.0722 | 0.645 | 0.6514 | 0.1705 | 0.1367 | | 0.0522 | 58.0 | 1450 | 1.4678 | 0.65 | 0.4770 | 3.0710 | 0.65 | 0.6546 | 0.1794 | 0.1371 | | 0.0522 | 59.0 | 1475 | 1.4717 | 0.64 | 0.4786 | 3.0737 | 0.64 | 0.6455 | 0.1889 | 0.1392 | | 0.0064 | 60.0 | 1500 | 1.4691 | 0.645 | 0.4768 | 3.0688 | 0.645 | 0.6499 | 0.1815 | 0.1378 | | 0.0064 | 61.0 | 1525 | 1.4689 | 0.64 | 0.4767 | 3.0688 | 0.64 | 0.6452 | 0.1846 | 0.1382 | | 0.0064 | 62.0 | 1550 | 1.4689 | 0.64 | 0.4770 | 3.0674 | 0.64 | 0.6455 | 0.1937 | 0.1383 | | 0.0064 | 63.0 | 1575 | 1.4687 | 0.6425 | 0.4767 | 3.0700 | 0.6425 | 0.6485 | 0.1897 | 0.1381 | | 0.0064 | 64.0 | 1600 | 1.4674 | 0.6425 | 0.4764 | 3.0675 | 0.6425 | 0.6472 | 0.1855 | 0.1375 | | 0.0064 | 65.0 | 1625 | 1.4681 | 0.6425 | 0.4766 | 3.0694 | 0.6425 | 0.6485 | 0.1917 | 0.1381 | | 0.0064 | 66.0 | 1650 | 1.4681 | 0.6425 | 0.4766 | 3.0687 | 0.6425 | 0.6472 | 0.1905 | 0.1378 | | 0.0064 | 67.0 | 1675 | 1.4667 | 0.645 | 0.4757 | 3.0681 | 0.645 | 0.6505 | 0.1899 | 0.1375 | | 0.0064 | 68.0 | 1700 | 1.4683 | 0.6425 | 0.4771 | 3.0686 | 0.6425 | 0.6474 | 0.1871 | 0.1379 | | 0.0064 | 69.0 | 1725 | 1.4672 | 0.64 | 0.4760 | 3.0679 | 0.64 | 0.6455 | 0.1932 | 0.1380 | | 0.0064 | 70.0 | 1750 | 1.4673 | 0.6425 | 0.4763 | 3.0683 | 0.6425 | 0.6474 | 0.1955 | 0.1376 | | 0.0064 | 71.0 | 1775 | 1.4676 | 0.645 | 0.4763 | 3.0680 | 0.645 | 0.6505 | 0.1921 | 0.1376 | | 0.0064 | 72.0 | 1800 | 1.4674 | 0.6425 | 0.4763 | 3.0683 | 0.6425 | 0.6474 | 0.1946 | 0.1376 | | 0.0064 | 73.0 | 1825 | 1.4675 | 0.6425 | 0.4763 | 3.0682 | 0.6425 | 0.6474 | 0.1946 | 0.1377 | | 0.0064 | 74.0 | 1850 | 1.4674 | 0.6425 | 0.4763 | 3.0682 | 0.6425 | 0.6485 | 0.1945 | 0.1380 | | 0.0064 | 75.0 | 1875 | 1.4674 | 0.64 | 0.4763 | 3.0680 | 0.64 | 0.6455 | 0.1960 | 0.1380 | | 0.0064 | 76.0 | 1900 | 1.4675 | 0.64 | 0.4764 | 3.0682 | 0.64 | 0.6455 | 0.1972 | 0.1381 | | 0.0064 | 77.0 | 1925 | 1.4675 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1947 | 0.1380 | | 0.0064 | 78.0 | 1950 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0064 | 79.0 | 1975 | 1.4674 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6474 | 0.1935 | 0.1376 | | 0.0 | 80.0 | 2000 | 1.4673 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1380 | | 0.0 | 81.0 | 2025 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1946 | 0.1380 | | 0.0 | 82.0 | 2050 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1935 | 0.1380 | | 0.0 | 83.0 | 2075 | 1.4674 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 84.0 | 2100 | 1.4674 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0 | 85.0 | 2125 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 86.0 | 2150 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 87.0 | 2175 | 1.4673 | 0.6425 | 0.4763 | 3.0681 | 0.6425 | 0.6485 | 0.1958 | 0.1381 | | 0.0 | 88.0 | 2200 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 89.0 | 2225 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 90.0 | 2250 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 91.0 | 2275 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 92.0 | 2300 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 93.0 | 2325 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 94.0 | 2350 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1909 | 0.1381 | | 0.0 | 95.0 | 2375 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 96.0 | 2400 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 97.0 | 2425 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 98.0 | 2450 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 99.0 | 2475 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | | 0.0 | 100.0 | 2500 | 1.4673 | 0.6425 | 0.4763 | 3.0680 | 0.6425 | 0.6485 | 0.1946 | 0.1381 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

MnLgt/swivel_inversion

MnLgt

2023-07-10T22:11:42Z

0

null

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

null

2023-07-10T22:11:41Z

--- license: mit --- ### swivel_inversion on Stable Diffusion This is the `<swivel-chair>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<swivel-chair> 0](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/942493f105b42e65e9bbb2afb8fd24ee.jpg) ![<swivel-chair> 1](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/25a60f0b7e1df4480da0096f4855d3cd.jpg) ![<swivel-chair> 2](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/099ba1cdff4a7d6d76437ec3b9d48743.jpg) ![<swivel-chair> 3](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a95a8487c048359027c5dc1f2f4231cd.jpg) ![<swivel-chair> 4](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e2258228f0c125fc4f0d2b3c27c4b5b5.jpg) ![<swivel-chair> 5](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/7692300d5457b1ad0b9b77bb4370a7b5.jpg) ![<swivel-chair> 6](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3b7fb905cd512af41d664db5b5c9c489.jpg) ![<swivel-chair> 7](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/89803df4905f81d2c1f70a1a7faf68fd.jpg) ![<swivel-chair> 8](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/570e6f83c4b0cd052893aee8e7030521.jpg) ![<swivel-chair> 9](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/465e22dc7bbfd6f42a803e8ab35c0609.jpg) ![<swivel-chair> 10](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/a6490c193d2815bd520a2478fcdb543f.jpg) ![<swivel-chair> 11](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/c789ecc814b35df75187611633dbd84a.jpg) ![<swivel-chair> 12](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/11ca822a037aa86f3316564ac212ac1c.jpg) ![<swivel-chair> 13](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/5dea3e2b3148e21a4cb4dfe8dea7af08.jpg) ![<swivel-chair> 14](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/46b89058d8c2342f3c152b50039cb0c9.jpg) ![<swivel-chair> 15](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/749bf590f4dd9f4c3ef1ffd58e7db3e8.jpg) ![<swivel-chair> 16](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6e32521663ac1cd6d0999e4a09dbf5a1.jpg) ![<swivel-chair> 17](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/bd3580a999d8ec073f2e9e7584fb1479.jpg) ![<swivel-chair> 18](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/fa116ff22175ba831f641af9bc1b44c8.jpg) ![<swivel-chair> 19](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/f32510be7c6e3d2d540d53ef0c0b5536.jpg) ![<swivel-chair> 20](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/3aab159b96dbcc1d403eeeea81191fb2.jpg) ![<swivel-chair> 21](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/6405e9bae158bf99eab384a36468c0cc.jpg) ![<swivel-chair> 22](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/e727d064c7c19b510acaacb2637c195e.jpg) ![<swivel-chair> 23](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ebf0269fca456ea8e2e307f6de6222ef.jpg) ![<swivel-chair> 24](https://huggingface.co/jordandavis/swivel_inversion/resolve/main/concept_images/ac4a58c646a756d07608c485bbe7fa45.jpg)

carova/ppo-LunarLander-v2

carova

2023-07-10T22:11:10Z

2

0

stable-baselines3

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

reinforcement-learning

2023-07-10T17:56:56Z

--- 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: 233.36 +/- 68.33 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 ... ```

TheBloke/airochronos-33B-GGML

TheBloke

2023-07-10T22:07:18Z

0

18

null

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

null

2023-07-10T21:14:18Z

--- inference: false license: other ---  <div style="width: 100%;"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div>  # Henk717's Airochronos 33B GGML These files are GGML format model files for [Henk717's Airochronos 33B](https://huggingface.co/Henk717/airochronos-33B). GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server. ## Repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/airochronos-33B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/airochronos-33B-GGML) * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Henk717/airochronos-33B) ## Prompt template: Alpaca ```Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ```  ## Compatibility ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0` These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods. ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K` These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`. They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details>  ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | airochronos-33b.ggmlv3.q2_K.bin | q2_K | 2 | 13.71 GB| 16.21 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | airochronos-33b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 14.06 GB| 16.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | airochronos-33b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.72 GB| 18.22 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | airochronos-33b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.28 GB| 19.78 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | airochronos-33b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.36 GB| 20.86 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | airochronos-33b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.62 GB| 22.12 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | airochronos-33b.ggmlv3.q4_0.bin | q4_0 | 4 | 18.30 GB| 20.80 GB | Original quant method, 4-bit. | | airochronos-33b.ggmlv3.q4_1.bin | q4_1 | 4 | 20.33 GB| 22.83 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | airochronos-33b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.40 GB| 24.90 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | airochronos-33b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.05 GB| 25.55 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | | airochronos-33b.ggmlv3.q5_0.bin | q5_0 | 5 | 22.37 GB| 24.87 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | airochronos-33b.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB| 26.90 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. | | airochronos-33b.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB| 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors | | airochronos-33b.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB| 37.06 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m airochronos-33b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).  ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Luke from CarbonQuill, Aemon Algiz. **Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang. Thank you to all my generous patrons and donaters!  # Original model card: Henk717's Airochronos 33B After the initial experiment with chronoboros-33B it was evident that the merge was to unpredictable to be useful, testing the individual models it became clear that the bias should be weighted towards Chronos. This is the new release of the merge with 75% chronos 33B, and 25% airoboros-1.4 33B. Model has been tested with the Alpaca prompting format combined with KoboldAI Lite's instruct and chat modes, as well as regular story writing. It has also been tested on basic reasoning tasks, but has not seen much testing for factual information.

jordyvl/vit-small_tobacco3482_kd_CEKD_t5.0_a0.7

jordyvl

2023-07-10T21:59:33Z

161

0

transformers

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

image-classification

2023-07-10T21:19:56Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t5.0_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4918 - Accuracy: 0.85 - Brier Loss: 0.2583 - Nll: 1.0894 - F1 Micro: 0.85 - F1 Macro: 0.8374 - Ece: 0.1917 - Aurc: 0.0470 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.8329 | 0.225 | 0.8761 | 5.2731 | 0.225 | 0.1384 | 0.2607 | 0.6977 | | No log | 2.0 | 14 | 1.4785 | 0.405 | 0.7460 | 3.4067 | 0.405 | 0.2289 | 0.3097 | 0.4085 | | No log | 3.0 | 21 | 1.0406 | 0.6 | 0.5725 | 1.8722 | 0.6 | 0.5345 | 0.3050 | 0.2010 | | No log | 4.0 | 28 | 0.8087 | 0.725 | 0.4192 | 1.6096 | 0.7250 | 0.6767 | 0.2345 | 0.1149 | | No log | 5.0 | 35 | 0.7666 | 0.735 | 0.3731 | 1.6189 | 0.735 | 0.7350 | 0.2377 | 0.1011 | | No log | 6.0 | 42 | 0.6960 | 0.78 | 0.3413 | 1.5230 | 0.78 | 0.7592 | 0.2295 | 0.0868 | | No log | 7.0 | 49 | 0.6490 | 0.805 | 0.3110 | 1.4861 | 0.805 | 0.7864 | 0.2138 | 0.0785 | | No log | 8.0 | 56 | 0.6238 | 0.795 | 0.3069 | 1.2098 | 0.795 | 0.7816 | 0.2065 | 0.0698 | | No log | 9.0 | 63 | 0.5755 | 0.83 | 0.2866 | 1.1943 | 0.83 | 0.8117 | 0.1937 | 0.0694 | | No log | 10.0 | 70 | 0.6360 | 0.77 | 0.3164 | 1.2608 | 0.7700 | 0.7550 | 0.1785 | 0.0677 | | No log | 11.0 | 77 | 0.6548 | 0.785 | 0.3335 | 1.4895 | 0.785 | 0.7707 | 0.2281 | 0.0885 | | No log | 12.0 | 84 | 0.5847 | 0.805 | 0.3002 | 1.4317 | 0.805 | 0.7807 | 0.2264 | 0.0756 | | No log | 13.0 | 91 | 0.5956 | 0.81 | 0.3040 | 1.2590 | 0.81 | 0.7928 | 0.2241 | 0.0556 | | No log | 14.0 | 98 | 0.5692 | 0.81 | 0.3025 | 1.2119 | 0.81 | 0.8043 | 0.2235 | 0.0665 | | No log | 15.0 | 105 | 0.5223 | 0.83 | 0.2762 | 1.1162 | 0.83 | 0.8221 | 0.1798 | 0.0552 | | No log | 16.0 | 112 | 0.4981 | 0.84 | 0.2523 | 1.0864 | 0.8400 | 0.8372 | 0.1868 | 0.0396 | | No log | 17.0 | 119 | 0.5207 | 0.805 | 0.2741 | 1.0416 | 0.805 | 0.7897 | 0.1960 | 0.0551 | | No log | 18.0 | 126 | 0.5165 | 0.84 | 0.2723 | 1.1596 | 0.8400 | 0.8325 | 0.1942 | 0.0506 | | No log | 19.0 | 133 | 0.4979 | 0.845 | 0.2573 | 1.2329 | 0.845 | 0.8297 | 0.1825 | 0.0444 | | No log | 20.0 | 140 | 0.4953 | 0.855 | 0.2565 | 1.1213 | 0.855 | 0.8442 | 0.1844 | 0.0474 | | No log | 21.0 | 147 | 0.5296 | 0.82 | 0.2792 | 1.0000 | 0.82 | 0.8218 | 0.1768 | 0.0523 | | No log | 22.0 | 154 | 0.5027 | 0.835 | 0.2625 | 0.9926 | 0.835 | 0.8238 | 0.2035 | 0.0481 | | No log | 23.0 | 161 | 0.5027 | 0.84 | 0.2642 | 1.0500 | 0.8400 | 0.8299 | 0.1616 | 0.0482 | | No log | 24.0 | 168 | 0.5017 | 0.84 | 0.2616 | 1.0560 | 0.8400 | 0.8314 | 0.1819 | 0.0497 | | No log | 25.0 | 175 | 0.4942 | 0.85 | 0.2594 | 1.1003 | 0.85 | 0.8407 | 0.1793 | 0.0483 | | No log | 26.0 | 182 | 0.4943 | 0.83 | 0.2586 | 1.0436 | 0.83 | 0.8140 | 0.1869 | 0.0518 | | No log | 27.0 | 189 | 0.4950 | 0.835 | 0.2613 | 1.0817 | 0.835 | 0.8224 | 0.2039 | 0.0504 | | No log | 28.0 | 196 | 0.4957 | 0.85 | 0.2599 | 1.1109 | 0.85 | 0.8309 | 0.2058 | 0.0485 | | No log | 29.0 | 203 | 0.4956 | 0.845 | 0.2599 | 1.0914 | 0.845 | 0.8304 | 0.1916 | 0.0492 | | No log | 30.0 | 210 | 0.4893 | 0.84 | 0.2561 | 1.0890 | 0.8400 | 0.8214 | 0.2071 | 0.0482 | | No log | 31.0 | 217 | 0.4920 | 0.835 | 0.2587 | 1.0907 | 0.835 | 0.8270 | 0.2031 | 0.0482 | | No log | 32.0 | 224 | 0.4927 | 0.83 | 0.2601 | 1.0879 | 0.83 | 0.8157 | 0.2093 | 0.0500 | | No log | 33.0 | 231 | 0.4925 | 0.835 | 0.2593 | 1.0886 | 0.835 | 0.8270 | 0.1810 | 0.0484 | | No log | 34.0 | 238 | 0.4909 | 0.845 | 0.2578 | 1.0871 | 0.845 | 0.8304 | 0.1916 | 0.0478 | | No log | 35.0 | 245 | 0.4927 | 0.845 | 0.2591 | 1.0866 | 0.845 | 0.8378 | 0.1943 | 0.0473 | | No log | 36.0 | 252 | 0.4919 | 0.85 | 0.2581 | 1.0891 | 0.85 | 0.8342 | 0.2193 | 0.0475 | | No log | 37.0 | 259 | 0.4908 | 0.845 | 0.2579 | 1.0867 | 0.845 | 0.8346 | 0.2215 | 0.0474 | | No log | 38.0 | 266 | 0.4929 | 0.85 | 0.2590 | 1.0873 | 0.85 | 0.8407 | 0.1884 | 0.0471 | | No log | 39.0 | 273 | 0.4913 | 0.85 | 0.2584 | 1.0861 | 0.85 | 0.8374 | 0.1944 | 0.0474 | | No log | 40.0 | 280 | 0.4933 | 0.835 | 0.2595 | 1.0871 | 0.835 | 0.8248 | 0.1893 | 0.0491 | | No log | 41.0 | 287 | 0.4936 | 0.84 | 0.2599 | 1.0863 | 0.8400 | 0.8276 | 0.1860 | 0.0486 | | No log | 42.0 | 294 | 0.4911 | 0.85 | 0.2580 | 1.0861 | 0.85 | 0.8374 | 0.2186 | 0.0474 | | No log | 43.0 | 301 | 0.4915 | 0.85 | 0.2581 | 1.0860 | 0.85 | 0.8374 | 0.2023 | 0.0475 | | No log | 44.0 | 308 | 0.4921 | 0.85 | 0.2586 | 1.0874 | 0.85 | 0.8374 | 0.2013 | 0.0477 | | No log | 45.0 | 315 | 0.4915 | 0.85 | 0.2583 | 1.0862 | 0.85 | 0.8374 | 0.1941 | 0.0475 | | No log | 46.0 | 322 | 0.4918 | 0.85 | 0.2584 | 1.0878 | 0.85 | 0.8374 | 0.1852 | 0.0473 | | No log | 47.0 | 329 | 0.4916 | 0.85 | 0.2583 | 1.0873 | 0.85 | 0.8374 | 0.2089 | 0.0473 | | No log | 48.0 | 336 | 0.4921 | 0.85 | 0.2586 | 1.0879 | 0.85 | 0.8374 | 0.2026 | 0.0477 | | No log | 49.0 | 343 | 0.4918 | 0.845 | 0.2584 | 1.0884 | 0.845 | 0.8282 | 0.1963 | 0.0478 | | No log | 50.0 | 350 | 0.4922 | 0.85 | 0.2587 | 1.0871 | 0.85 | 0.8374 | 0.2102 | 0.0474 | | No log | 51.0 | 357 | 0.4920 | 0.85 | 0.2585 | 1.0879 | 0.85 | 0.8374 | 0.2095 | 0.0474 | | No log | 52.0 | 364 | 0.4926 | 0.85 | 0.2589 | 1.0878 | 0.85 | 0.8374 | 0.2022 | 0.0477 | | No log | 53.0 | 371 | 0.4920 | 0.85 | 0.2586 | 1.0888 | 0.85 | 0.8374 | 0.2027 | 0.0475 | | No log | 54.0 | 378 | 0.4921 | 0.85 | 0.2586 | 1.0886 | 0.85 | 0.8374 | 0.2020 | 0.0474 | | No log | 55.0 | 385 | 0.4921 | 0.85 | 0.2587 | 1.0890 | 0.85 | 0.8374 | 0.1929 | 0.0471 | | No log | 56.0 | 392 | 0.4925 | 0.85 | 0.2589 | 1.0881 | 0.85 | 0.8374 | 0.1946 | 0.0473 | | No log | 57.0 | 399 | 0.4917 | 0.85 | 0.2583 | 1.0893 | 0.85 | 0.8374 | 0.1932 | 0.0472 | | No log | 58.0 | 406 | 0.4921 | 0.85 | 0.2586 | 1.0877 | 0.85 | 0.8374 | 0.1948 | 0.0476 | | No log | 59.0 | 413 | 0.4917 | 0.85 | 0.2583 | 1.0883 | 0.85 | 0.8374 | 0.1931 | 0.0472 | | No log | 60.0 | 420 | 0.4918 | 0.85 | 0.2583 | 1.0882 | 0.85 | 0.8374 | 0.1945 | 0.0475 | | No log | 61.0 | 427 | 0.4916 | 0.85 | 0.2582 | 1.0883 | 0.85 | 0.8374 | 0.1936 | 0.0472 | | No log | 62.0 | 434 | 0.4920 | 0.85 | 0.2586 | 1.0882 | 0.85 | 0.8374 | 0.1942 | 0.0473 | | No log | 63.0 | 441 | 0.4922 | 0.85 | 0.2587 | 1.0889 | 0.85 | 0.8374 | 0.1935 | 0.0473 | | No log | 64.0 | 448 | 0.4921 | 0.85 | 0.2586 | 1.0885 | 0.85 | 0.8374 | 0.1848 | 0.0473 | | No log | 65.0 | 455 | 0.4916 | 0.85 | 0.2582 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0474 | | No log | 66.0 | 462 | 0.4917 | 0.85 | 0.2583 | 1.0883 | 0.85 | 0.8374 | 0.1849 | 0.0472 | | No log | 67.0 | 469 | 0.4917 | 0.85 | 0.2584 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | No log | 68.0 | 476 | 0.4920 | 0.85 | 0.2585 | 1.0888 | 0.85 | 0.8374 | 0.2011 | 0.0471 | | No log | 69.0 | 483 | 0.4918 | 0.85 | 0.2584 | 1.0889 | 0.85 | 0.8374 | 0.2007 | 0.0471 | | No log | 70.0 | 490 | 0.4919 | 0.85 | 0.2584 | 1.0886 | 0.85 | 0.8374 | 0.1848 | 0.0474 | | No log | 71.0 | 497 | 0.4920 | 0.85 | 0.2585 | 1.0888 | 0.85 | 0.8374 | 0.1940 | 0.0474 | | 0.1824 | 72.0 | 504 | 0.4919 | 0.85 | 0.2584 | 1.0889 | 0.85 | 0.8374 | 0.2011 | 0.0471 | | 0.1824 | 73.0 | 511 | 0.4917 | 0.85 | 0.2583 | 1.0887 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | 0.1824 | 74.0 | 518 | 0.4920 | 0.85 | 0.2585 | 1.0890 | 0.85 | 0.8374 | 0.1848 | 0.0472 | | 0.1824 | 75.0 | 525 | 0.4920 | 0.85 | 0.2585 | 1.0892 | 0.85 | 0.8374 | 0.1846 | 0.0472 | | 0.1824 | 76.0 | 532 | 0.4918 | 0.85 | 0.2583 | 1.0889 | 0.85 | 0.8374 | 0.1930 | 0.0472 | | 0.1824 | 77.0 | 539 | 0.4917 | 0.85 | 0.2582 | 1.0891 | 0.85 | 0.8374 | 0.2005 | 0.0472 | | 0.1824 | 78.0 | 546 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1928 | 0.0472 | | 0.1824 | 79.0 | 553 | 0.4920 | 0.85 | 0.2585 | 1.0893 | 0.85 | 0.8374 | 0.1845 | 0.0473 | | 0.1824 | 80.0 | 560 | 0.4919 | 0.85 | 0.2584 | 1.0890 | 0.85 | 0.8374 | 0.1929 | 0.0473 | | 0.1824 | 81.0 | 567 | 0.4920 | 0.85 | 0.2585 | 1.0892 | 0.85 | 0.8374 | 0.1925 | 0.0471 | | 0.1824 | 82.0 | 574 | 0.4920 | 0.85 | 0.2585 | 1.0895 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 83.0 | 581 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 84.0 | 588 | 0.4918 | 0.85 | 0.2584 | 1.0890 | 0.85 | 0.8374 | 0.1926 | 0.0471 | | 0.1824 | 85.0 | 595 | 0.4918 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 86.0 | 602 | 0.4918 | 0.85 | 0.2584 | 1.0893 | 0.85 | 0.8374 | 0.1927 | 0.0472 | | 0.1824 | 87.0 | 609 | 0.4918 | 0.85 | 0.2584 | 1.0895 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 88.0 | 616 | 0.4918 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 89.0 | 623 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0471 | | 0.1824 | 90.0 | 630 | 0.4919 | 0.85 | 0.2584 | 1.0892 | 0.85 | 0.8374 | 0.1998 | 0.0471 | | 0.1824 | 91.0 | 637 | 0.4919 | 0.85 | 0.2584 | 1.0894 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 92.0 | 644 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 93.0 | 651 | 0.4918 | 0.85 | 0.2583 | 1.0893 | 0.85 | 0.8374 | 0.1917 | 0.0471 | | 0.1824 | 94.0 | 658 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1844 | 0.0471 | | 0.1824 | 95.0 | 665 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 96.0 | 672 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 97.0 | 679 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1916 | 0.0471 | | 0.1824 | 98.0 | 686 | 0.4918 | 0.85 | 0.2583 | 1.0895 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 99.0 | 693 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | | 0.1824 | 100.0 | 700 | 0.4918 | 0.85 | 0.2583 | 1.0894 | 0.85 | 0.8374 | 0.1917 | 0.0470 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

umanlp/babelbert-ft-xlm-r

umanlp

2023-07-10T21:57:04Z

160

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]

feature-extraction

2023-07-07T21:22:09Z

This model is one of the artifacts of the paper [Massively Multilingual Lexical Specialization of Multilingual Transformers](https://aclanthology.org/2023.acl-long.426/). It was obtained by fine-tuning the representations of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the dataset [babelbert-dataset](https://huggingface.co/datasets/umanlp/babelbert-dataset).

BernardOng/Banking-FT-Bong-v1

BernardOng

2023-07-10T21:29:24Z

13

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-05-30T02:19:43Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [h2oai/h2ogpt-oig-oasst1-512-6.9b](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-512-6.9b) - Caution: This is only an experimental model used mainly for research and testing purposes. It is not meant for production use. ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.28.1 pip install accelerate==0.18.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="BernardOng/Banking-FT-Bong-v1", torch_dtype=torch.float16, trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|> ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "BernardOng/Banking-FT-Bong-v1", use_fast=True, padding_side="left" ) model = AutoModelForCausalLM.from_pretrained( "BernardOng/Banking-FT-Bong-v1", torch_dtype=torch.float16, device_map={"": "cuda:0"} ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "BernardOng/Banking-FT-Bong-v1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?<|endoftext|><|answer|>" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) model = AutoModelForCausalLM.from_pretrained(model_name) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=2, temperature=float(8.0), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( (embed_in): Embedding(50432, 4096) (layers): ModuleList( (0-31): 32 x GPTNeoXLayer( (input_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (post_attention_layernorm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) (attention): GPTNeoXAttention( (rotary_emb): RotaryEmbedding() (query_key_value): Linear(in_features=4096, out_features=12288, bias=True) (dense): Linear(in_features=4096, out_features=4096, bias=True) ) (mlp): GPTNeoXMLP( (dense_h_to_4h): Linear(in_features=4096, out_features=16384, bias=True) (dense_4h_to_h): Linear(in_features=16384, out_features=4096, bias=True) (act): GELUActivation() ) ) ) (final_layer_norm): LayerNorm((4096,), eps=1e-05, elementwise_affine=True) ) (embed_out): Linear(in_features=4096, out_features=50432, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). ```bash CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=BernardOng/Banking-FT-Bong-v1 --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log ``` ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

vk21/ppo-PyramidRND-unit5

vk21

2023-07-10T21:25:11Z

0

ml-agents

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

reinforcement-learning

2023-07-10T21:25:05Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-PyramidRND-unit5 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

voyzan/unit1-bonus1-Huggy-A01

voyzan

2023-07-10T21:19:37Z

0

ml-agents

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

reinforcement-learning

2023-07-10T21:19:36Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: voyzan/unit1-bonus1-Huggy-A01 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jordyvl/vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE

jordyvl

2023-07-10T21:13:05Z

164

0

transformers

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

image-classification

2023-07-10T20:08:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE results: [] ---  # vit-tiny_rvl_cdip_100_examples_per_class_kd_MSE This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7723 - Accuracy: 0.6025 - Brier Loss: 0.5295 - Nll: 3.6748 - F1 Micro: 0.6025 - F1 Macro: 0.6055 - Ece: 0.1688 - Aurc: 0.1708 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:-------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 25 | 4.7870 | 0.065 | 0.9655 | 17.0930 | 0.065 | 0.0550 | 0.1747 | 0.9357 | | No log | 2.0 | 50 | 3.9498 | 0.205 | 0.8858 | 9.5780 | 0.205 | 0.1863 | 0.1692 | 0.6618 | | No log | 3.0 | 75 | 3.3698 | 0.3675 | 0.7672 | 6.4908 | 0.3675 | 0.3392 | 0.1676 | 0.4195 | | No log | 4.0 | 100 | 2.9935 | 0.4075 | 0.6958 | 5.5595 | 0.4075 | 0.3820 | 0.1828 | 0.3327 | | No log | 5.0 | 125 | 2.8351 | 0.455 | 0.6591 | 4.8619 | 0.455 | 0.4351 | 0.1561 | 0.2833 | | No log | 6.0 | 150 | 2.8196 | 0.4725 | 0.6595 | 4.7785 | 0.4725 | 0.4367 | 0.1808 | 0.2790 | | No log | 7.0 | 175 | 2.6352 | 0.5075 | 0.6234 | 4.9881 | 0.5075 | 0.4886 | 0.1563 | 0.2493 | | No log | 8.0 | 200 | 2.5325 | 0.525 | 0.6162 | 4.3297 | 0.525 | 0.5026 | 0.1724 | 0.2365 | | No log | 9.0 | 225 | 2.5459 | 0.53 | 0.6099 | 5.1608 | 0.53 | 0.5148 | 0.1944 | 0.2350 | | No log | 10.0 | 250 | 2.5573 | 0.5325 | 0.6161 | 5.4495 | 0.5325 | 0.5212 | 0.2052 | 0.2397 | | No log | 11.0 | 275 | 2.3199 | 0.5675 | 0.5828 | 4.1247 | 0.5675 | 0.5626 | 0.1849 | 0.2071 | | No log | 12.0 | 300 | 2.2917 | 0.565 | 0.5758 | 4.1738 | 0.565 | 0.5694 | 0.1992 | 0.2023 | | No log | 13.0 | 325 | 2.2744 | 0.555 | 0.5974 | 4.2323 | 0.555 | 0.5544 | 0.1982 | 0.2203 | | No log | 14.0 | 350 | 2.1638 | 0.5625 | 0.5807 | 4.2049 | 0.5625 | 0.5629 | 0.1868 | 0.2049 | | No log | 15.0 | 375 | 2.1934 | 0.5575 | 0.5903 | 4.3813 | 0.5575 | 0.5614 | 0.1868 | 0.2022 | | No log | 16.0 | 400 | 2.1092 | 0.5625 | 0.5702 | 3.6094 | 0.5625 | 0.5700 | 0.1846 | 0.2011 | | No log | 17.0 | 425 | 2.0379 | 0.5875 | 0.5642 | 4.4351 | 0.5875 | 0.5822 | 0.2036 | 0.1959 | | No log | 18.0 | 450 | 2.0303 | 0.5825 | 0.5558 | 3.6847 | 0.5825 | 0.5820 | 0.1684 | 0.1881 | | No log | 19.0 | 475 | 2.0506 | 0.57 | 0.5749 | 4.0014 | 0.57 | 0.5708 | 0.1725 | 0.2027 | | 1.5026 | 20.0 | 500 | 1.9932 | 0.5875 | 0.5524 | 3.8003 | 0.5875 | 0.5914 | 0.1843 | 0.1831 | | 1.5026 | 21.0 | 525 | 2.0131 | 0.565 | 0.5643 | 4.0681 | 0.565 | 0.5635 | 0.1776 | 0.1957 | | 1.5026 | 22.0 | 550 | 2.0162 | 0.5725 | 0.5712 | 3.7068 | 0.5725 | 0.5766 | 0.1934 | 0.1955 | | 1.5026 | 23.0 | 575 | 1.9093 | 0.605 | 0.5381 | 3.7930 | 0.605 | 0.6032 | 0.1539 | 0.1749 | | 1.5026 | 24.0 | 600 | 1.9607 | 0.575 | 0.5561 | 4.5740 | 0.575 | 0.5789 | 0.1782 | 0.1902 | | 1.5026 | 25.0 | 625 | 1.8971 | 0.5825 | 0.5408 | 3.7290 | 0.5825 | 0.5754 | 0.1836 | 0.1751 | | 1.5026 | 26.0 | 650 | 1.9217 | 0.5775 | 0.5537 | 3.8085 | 0.5775 | 0.5844 | 0.1725 | 0.1843 | | 1.5026 | 27.0 | 675 | 1.9493 | 0.585 | 0.5606 | 3.6743 | 0.585 | 0.5953 | 0.1755 | 0.1882 | | 1.5026 | 28.0 | 700 | 1.8884 | 0.585 | 0.5437 | 3.7865 | 0.585 | 0.5828 | 0.1801 | 0.1822 | | 1.5026 | 29.0 | 725 | 1.9242 | 0.585 | 0.5479 | 3.9607 | 0.585 | 0.5856 | 0.1619 | 0.1817 | | 1.5026 | 30.0 | 750 | 1.8767 | 0.5975 | 0.5470 | 3.7995 | 0.5975 | 0.5966 | 0.1599 | 0.1790 | | 1.5026 | 31.0 | 775 | 1.8723 | 0.5925 | 0.5337 | 3.8962 | 0.5925 | 0.5972 | 0.1678 | 0.1729 | | 1.5026 | 32.0 | 800 | 1.9093 | 0.585 | 0.5545 | 3.8776 | 0.585 | 0.5830 | 0.1902 | 0.1841 | | 1.5026 | 33.0 | 825 | 1.8667 | 0.595 | 0.5363 | 3.8926 | 0.595 | 0.5917 | 0.1772 | 0.1745 | | 1.5026 | 34.0 | 850 | 1.8403 | 0.59 | 0.5521 | 3.8560 | 0.59 | 0.5953 | 0.1711 | 0.1800 | | 1.5026 | 35.0 | 875 | 1.8464 | 0.5925 | 0.5380 | 4.0376 | 0.5925 | 0.5970 | 0.1719 | 0.1756 | | 1.5026 | 36.0 | 900 | 1.8441 | 0.5975 | 0.5411 | 3.7193 | 0.5975 | 0.6008 | 0.1569 | 0.1753 | | 1.5026 | 37.0 | 925 | 1.8599 | 0.5875 | 0.5402 | 3.9139 | 0.5875 | 0.5908 | 0.1779 | 0.1789 | | 1.5026 | 38.0 | 950 | 1.8559 | 0.6 | 0.5458 | 3.8970 | 0.6 | 0.5991 | 0.1583 | 0.1804 | | 1.5026 | 39.0 | 975 | 1.8285 | 0.61 | 0.5370 | 3.6292 | 0.61 | 0.6155 | 0.1623 | 0.1722 | | 0.0745 | 40.0 | 1000 | 1.8309 | 0.5975 | 0.5432 | 3.6865 | 0.5975 | 0.6017 | 0.1663 | 0.1821 | | 0.0745 | 41.0 | 1025 | 1.8237 | 0.59 | 0.5348 | 3.6213 | 0.59 | 0.5921 | 0.1695 | 0.1738 | | 0.0745 | 42.0 | 1050 | 1.8421 | 0.605 | 0.5360 | 3.8592 | 0.605 | 0.6048 | 0.1601 | 0.1743 | | 0.0745 | 43.0 | 1075 | 1.8158 | 0.5975 | 0.5300 | 3.4537 | 0.5975 | 0.5953 | 0.1696 | 0.1707 | | 0.0745 | 44.0 | 1100 | 1.8238 | 0.5875 | 0.5358 | 3.7706 | 0.5875 | 0.5923 | 0.1797 | 0.1754 | | 0.0745 | 45.0 | 1125 | 1.8214 | 0.595 | 0.5463 | 3.4742 | 0.595 | 0.5981 | 0.1800 | 0.1770 | | 0.0745 | 46.0 | 1150 | 1.8162 | 0.5925 | 0.5317 | 3.9260 | 0.5925 | 0.5950 | 0.1646 | 0.1733 | | 0.0745 | 47.0 | 1175 | 1.8050 | 0.5975 | 0.5392 | 3.8322 | 0.5975 | 0.5979 | 0.1794 | 0.1763 | | 0.0745 | 48.0 | 1200 | 1.8214 | 0.5975 | 0.5347 | 3.7965 | 0.5975 | 0.6009 | 0.1555 | 0.1746 | | 0.0745 | 49.0 | 1225 | 1.7813 | 0.6 | 0.5294 | 3.8398 | 0.6 | 0.6005 | 0.1674 | 0.1688 | | 0.0745 | 50.0 | 1250 | 1.8179 | 0.6075 | 0.5336 | 3.4690 | 0.6075 | 0.6112 | 0.1743 | 0.1748 | | 0.0745 | 51.0 | 1275 | 1.7953 | 0.595 | 0.5380 | 3.7781 | 0.595 | 0.5990 | 0.1380 | 0.1727 | | 0.0745 | 52.0 | 1300 | 1.7897 | 0.6 | 0.5323 | 3.7412 | 0.6 | 0.6013 | 0.1603 | 0.1707 | | 0.0745 | 53.0 | 1325 | 1.8072 | 0.59 | 0.5428 | 3.5993 | 0.59 | 0.5947 | 0.1571 | 0.1773 | | 0.0745 | 54.0 | 1350 | 1.7834 | 0.605 | 0.5219 | 3.7600 | 0.605 | 0.6049 | 0.1563 | 0.1671 | | 0.0745 | 55.0 | 1375 | 1.7920 | 0.595 | 0.5361 | 3.5986 | 0.595 | 0.5978 | 0.1512 | 0.1717 | | 0.0745 | 56.0 | 1400 | 1.8074 | 0.5925 | 0.5387 | 3.5383 | 0.5925 | 0.5962 | 0.1669 | 0.1741 | | 0.0745 | 57.0 | 1425 | 1.7893 | 0.605 | 0.5346 | 3.6929 | 0.605 | 0.6039 | 0.1641 | 0.1681 | | 0.0745 | 58.0 | 1450 | 1.7787 | 0.6 | 0.5317 | 3.7652 | 0.6 | 0.6004 | 0.1850 | 0.1726 | | 0.0745 | 59.0 | 1475 | 1.7888 | 0.595 | 0.5323 | 3.4558 | 0.595 | 0.5975 | 0.1797 | 0.1732 | | 0.0231 | 60.0 | 1500 | 1.8064 | 0.58 | 0.5332 | 3.7773 | 0.58 | 0.5839 | 0.1819 | 0.1762 | | 0.0231 | 61.0 | 1525 | 1.7795 | 0.6075 | 0.5298 | 3.7998 | 0.6075 | 0.6086 | 0.1678 | 0.1704 | | 0.0231 | 62.0 | 1550 | 1.7826 | 0.595 | 0.5318 | 3.6741 | 0.595 | 0.5916 | 0.1550 | 0.1715 | | 0.0231 | 63.0 | 1575 | 1.7704 | 0.5925 | 0.5325 | 3.5942 | 0.5925 | 0.5941 | 0.1619 | 0.1712 | | 0.0231 | 64.0 | 1600 | 1.7901 | 0.6025 | 0.5289 | 3.4459 | 0.6025 | 0.6054 | 0.2022 | 0.1712 | | 0.0231 | 65.0 | 1625 | 1.7944 | 0.59 | 0.5381 | 3.7591 | 0.59 | 0.5910 | 0.1599 | 0.1756 | | 0.0231 | 66.0 | 1650 | 1.7721 | 0.605 | 0.5256 | 3.5227 | 0.605 | 0.6045 | 0.1525 | 0.1677 | | 0.0231 | 67.0 | 1675 | 1.7779 | 0.5975 | 0.5306 | 3.6792 | 0.5975 | 0.5994 | 0.1667 | 0.1714 | | 0.0231 | 68.0 | 1700 | 1.7724 | 0.6 | 0.5250 | 3.7552 | 0.6 | 0.6022 | 0.1818 | 0.1683 | | 0.0231 | 69.0 | 1725 | 1.7765 | 0.6025 | 0.5283 | 3.4264 | 0.6025 | 0.6019 | 0.1671 | 0.1700 | | 0.0231 | 70.0 | 1750 | 1.7784 | 0.6 | 0.5276 | 3.6887 | 0.6 | 0.6053 | 0.1715 | 0.1703 | | 0.0231 | 71.0 | 1775 | 1.7659 | 0.6 | 0.5282 | 3.6051 | 0.6 | 0.6006 | 0.1722 | 0.1691 | | 0.0231 | 72.0 | 1800 | 1.7882 | 0.5975 | 0.5329 | 3.5950 | 0.5975 | 0.6016 | 0.1981 | 0.1716 | | 0.0231 | 73.0 | 1825 | 1.7678 | 0.6 | 0.5287 | 3.6691 | 0.6 | 0.6032 | 0.1733 | 0.1696 | | 0.0231 | 74.0 | 1850 | 1.7716 | 0.6 | 0.5286 | 3.7576 | 0.6 | 0.6013 | 0.1734 | 0.1692 | | 0.0231 | 75.0 | 1875 | 1.7704 | 0.6 | 0.5299 | 3.5917 | 0.6 | 0.6016 | 0.1645 | 0.1709 | | 0.0231 | 76.0 | 1900 | 1.7729 | 0.6 | 0.5298 | 3.6758 | 0.6 | 0.6024 | 0.1766 | 0.1710 | | 0.0231 | 77.0 | 1925 | 1.7749 | 0.6 | 0.5308 | 3.6022 | 0.6 | 0.6030 | 0.1604 | 0.1717 | | 0.0231 | 78.0 | 1950 | 1.7720 | 0.6 | 0.5294 | 3.6759 | 0.6 | 0.6017 | 0.1786 | 0.1708 | | 0.0231 | 79.0 | 1975 | 1.7734 | 0.6025 | 0.5288 | 3.6765 | 0.6025 | 0.6048 | 0.1673 | 0.1698 | | 0.0059 | 80.0 | 2000 | 1.7709 | 0.6 | 0.5286 | 3.6755 | 0.6 | 0.6020 | 0.1749 | 0.1704 | | 0.0059 | 81.0 | 2025 | 1.7730 | 0.6 | 0.5295 | 3.6760 | 0.6 | 0.6020 | 0.1677 | 0.1708 | | 0.0059 | 82.0 | 2050 | 1.7723 | 0.6025 | 0.5295 | 3.6756 | 0.6025 | 0.6055 | 0.1626 | 0.1708 | | 0.0059 | 83.0 | 2075 | 1.7721 | 0.6025 | 0.5295 | 3.6741 | 0.6025 | 0.6055 | 0.1709 | 0.1708 | | 0.0059 | 84.0 | 2100 | 1.7725 | 0.6025 | 0.5297 | 3.6747 | 0.6025 | 0.6048 | 0.1627 | 0.1709 | | 0.0059 | 85.0 | 2125 | 1.7724 | 0.6025 | 0.5295 | 3.6751 | 0.6025 | 0.6055 | 0.1639 | 0.1707 | | 0.0059 | 86.0 | 2150 | 1.7724 | 0.6025 | 0.5296 | 3.6751 | 0.6025 | 0.6055 | 0.1630 | 0.1708 | | 0.0059 | 87.0 | 2175 | 1.7724 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1638 | 0.1707 | | 0.0059 | 88.0 | 2200 | 1.7722 | 0.6025 | 0.5295 | 3.6752 | 0.6025 | 0.6055 | 0.1645 | 0.1708 | | 0.0059 | 89.0 | 2225 | 1.7723 | 0.6025 | 0.5295 | 3.6747 | 0.6025 | 0.6055 | 0.1639 | 0.1708 | | 0.0059 | 90.0 | 2250 | 1.7723 | 0.6025 | 0.5294 | 3.6750 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 91.0 | 2275 | 1.7723 | 0.6025 | 0.5294 | 3.6750 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 92.0 | 2300 | 1.7723 | 0.6025 | 0.5295 | 3.6747 | 0.6025 | 0.6055 | 0.1639 | 0.1708 | | 0.0059 | 93.0 | 2325 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1637 | 0.1707 | | 0.0059 | 94.0 | 2350 | 1.7722 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0059 | 95.0 | 2375 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 96.0 | 2400 | 1.7723 | 0.6025 | 0.5294 | 3.6748 | 0.6025 | 0.6055 | 0.1643 | 0.1707 | | 0.0059 | 97.0 | 2425 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0059 | 98.0 | 2450 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1643 | 0.1708 | | 0.0059 | 99.0 | 2475 | 1.7723 | 0.6025 | 0.5295 | 3.6749 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | | 0.0 | 100.0 | 2500 | 1.7723 | 0.6025 | 0.5295 | 3.6748 | 0.6025 | 0.6055 | 0.1688 | 0.1708 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.12.0 - Tokenizers 0.12.1

ruggedmug/ppo-Huggy

ruggedmug

2023-07-10T21:08:52Z

2

0

ml-agents

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

reinforcement-learning

2023-07-10T21:08:43Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ruggedmug/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Belphegor/ppo-LunarLander-v2

Belphegor

2023-07-10T21:08:44Z

2

0

stable-baselines3

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

reinforcement-learning

2023-07-10T21:08:27Z

--- 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: 268.37 +/- 18.85 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 ... ```

skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO

skrl

2023-07-10T21:06:55Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T19:47:47Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -8.89 +/- 10.3 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltScrew type: IsaacGymEnvs-FactoryTaskNutBoltScrew ---  # IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FactoryTaskNutBoltScrew - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltScrew-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 128 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 32 # 128 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

WALIDALI/bekinorrev

WALIDALI

2023-07-10T21:00:29Z

5

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T20:57:08Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekinorrev Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

Henk717/chronoboros-33B

Henk717

2023-07-10T20:48:47Z

1,410

9

transformers

[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-09T21:00:09Z

--- license: other --- This model was the result of a 50/50 average weight merge between Airoboros-33B-1.4 and Chronos-33B. After prolonged testing we concluded that while this merge is highly flexible and capable of many different tasks, it has to much variation in how it answers to be reliable. Because of this the model relies on some luck to get good results, and is therefore not recommended to people seeking a consistent experience, or people sensitive to anticipation based addictions. If you would like an improved version of this model that is more stable check out my Airochronos-33B merge.

voyzan/unit1-lunar_lander_v2-A02

voyzan

2023-07-10T20:47:07Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-10T20:46:49Z

--- 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: 255.03 +/- 23.01 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 ... ```

jliu596/flappybirdknockoff

jliu596

2023-07-10T20:45:22Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T20:40:49Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: flappybirdknockoff results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.40 +/- 11.34 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

jordyvl/vit-small_tobacco3482_kd_CEKD_t2.5_a0.9

jordyvl

2023-07-10T20:38:28Z

163

0

transformers

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

image-classification

2023-07-10T19:59:22Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t2.5_a0.9 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t2.5_a0.9 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5446 - Accuracy: 0.85 - Brier Loss: 0.2446 - Nll: 1.0816 - F1 Micro: 0.85 - F1 Macro: 0.8348 - Ece: 0.1474 - Aurc: 0.0436 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 2.1216 | 0.215 | 0.8751 | 5.2864 | 0.2150 | 0.1264 | 0.2697 | 0.6907 | | No log | 2.0 | 14 | 1.7056 | 0.405 | 0.7400 | 3.5721 | 0.405 | 0.2275 | 0.2995 | 0.4011 | | No log | 3.0 | 21 | 1.1857 | 0.62 | 0.5612 | 2.0143 | 0.62 | 0.5712 | 0.2994 | 0.2024 | | No log | 4.0 | 28 | 0.8767 | 0.705 | 0.4085 | 1.6918 | 0.705 | 0.6436 | 0.2231 | 0.1152 | | No log | 5.0 | 35 | 0.8620 | 0.72 | 0.3878 | 1.7931 | 0.72 | 0.7294 | 0.2233 | 0.1076 | | No log | 6.0 | 42 | 0.7517 | 0.775 | 0.3252 | 1.5573 | 0.775 | 0.7600 | 0.1970 | 0.0790 | | No log | 7.0 | 49 | 0.7280 | 0.79 | 0.3175 | 1.5140 | 0.79 | 0.7742 | 0.1903 | 0.0826 | | No log | 8.0 | 56 | 0.6848 | 0.8 | 0.2942 | 1.4438 | 0.8000 | 0.7902 | 0.1828 | 0.0866 | | No log | 9.0 | 63 | 0.6744 | 0.81 | 0.2889 | 1.4703 | 0.81 | 0.7969 | 0.1989 | 0.0692 | | No log | 10.0 | 70 | 0.8432 | 0.74 | 0.3859 | 1.3134 | 0.74 | 0.7206 | 0.1959 | 0.1051 | | No log | 11.0 | 77 | 0.7424 | 0.765 | 0.3294 | 1.5162 | 0.765 | 0.7792 | 0.2005 | 0.1048 | | No log | 12.0 | 84 | 0.6953 | 0.79 | 0.3194 | 1.2233 | 0.79 | 0.7850 | 0.1800 | 0.0922 | | No log | 13.0 | 91 | 0.5703 | 0.845 | 0.2538 | 1.2355 | 0.845 | 0.8372 | 0.1739 | 0.0447 | | No log | 14.0 | 98 | 0.6439 | 0.795 | 0.2924 | 1.2777 | 0.795 | 0.7743 | 0.1771 | 0.0534 | | No log | 15.0 | 105 | 0.5895 | 0.825 | 0.2650 | 1.2086 | 0.825 | 0.8071 | 0.1665 | 0.0566 | | No log | 16.0 | 112 | 0.5973 | 0.81 | 0.2753 | 1.0959 | 0.81 | 0.8013 | 0.1839 | 0.0534 | | No log | 17.0 | 119 | 0.5825 | 0.795 | 0.2722 | 1.1565 | 0.795 | 0.7886 | 0.1855 | 0.0534 | | No log | 18.0 | 126 | 0.5854 | 0.845 | 0.2661 | 1.1223 | 0.845 | 0.8424 | 0.1981 | 0.0549 | | No log | 19.0 | 133 | 0.5514 | 0.82 | 0.2553 | 0.9585 | 0.82 | 0.8150 | 0.1600 | 0.0481 | | No log | 20.0 | 140 | 0.5600 | 0.835 | 0.2443 | 1.2692 | 0.835 | 0.8232 | 0.1657 | 0.0469 | | No log | 21.0 | 147 | 0.5592 | 0.845 | 0.2473 | 1.1658 | 0.845 | 0.8331 | 0.1683 | 0.0493 | | No log | 22.0 | 154 | 0.5507 | 0.845 | 0.2411 | 1.1403 | 0.845 | 0.8311 | 0.1797 | 0.0450 | | No log | 23.0 | 161 | 0.5305 | 0.84 | 0.2361 | 1.1509 | 0.8400 | 0.8287 | 0.1650 | 0.0409 | | No log | 24.0 | 168 | 0.5352 | 0.835 | 0.2378 | 1.2208 | 0.835 | 0.8201 | 0.1515 | 0.0420 | | No log | 25.0 | 175 | 0.5425 | 0.845 | 0.2420 | 1.1208 | 0.845 | 0.8321 | 0.1776 | 0.0430 | | No log | 26.0 | 182 | 0.5396 | 0.84 | 0.2409 | 1.1230 | 0.8400 | 0.8286 | 0.1647 | 0.0446 | | No log | 27.0 | 189 | 0.5436 | 0.85 | 0.2401 | 1.1179 | 0.85 | 0.8387 | 0.1568 | 0.0427 | | No log | 28.0 | 196 | 0.5373 | 0.835 | 0.2415 | 1.1092 | 0.835 | 0.8141 | 0.1641 | 0.0427 | | No log | 29.0 | 203 | 0.5420 | 0.845 | 0.2436 | 1.0988 | 0.845 | 0.8326 | 0.1551 | 0.0444 | | No log | 30.0 | 210 | 0.5413 | 0.845 | 0.2420 | 1.1064 | 0.845 | 0.8312 | 0.1486 | 0.0440 | | No log | 31.0 | 217 | 0.5411 | 0.84 | 0.2418 | 1.1024 | 0.8400 | 0.8286 | 0.1565 | 0.0435 | | No log | 32.0 | 224 | 0.5426 | 0.845 | 0.2429 | 1.0993 | 0.845 | 0.8322 | 0.1631 | 0.0433 | | No log | 33.0 | 231 | 0.5424 | 0.85 | 0.2426 | 1.0989 | 0.85 | 0.8348 | 0.1615 | 0.0436 | | No log | 34.0 | 238 | 0.5406 | 0.84 | 0.2419 | 1.0979 | 0.8400 | 0.8251 | 0.1640 | 0.0440 | | No log | 35.0 | 245 | 0.5438 | 0.85 | 0.2436 | 1.0953 | 0.85 | 0.8348 | 0.1595 | 0.0438 | | No log | 36.0 | 252 | 0.5429 | 0.85 | 0.2429 | 1.0970 | 0.85 | 0.8348 | 0.1495 | 0.0433 | | No log | 37.0 | 259 | 0.5431 | 0.85 | 0.2427 | 1.0951 | 0.85 | 0.8348 | 0.1617 | 0.0435 | | No log | 38.0 | 266 | 0.5424 | 0.85 | 0.2426 | 1.0959 | 0.85 | 0.8348 | 0.1587 | 0.0434 | | No log | 39.0 | 273 | 0.5428 | 0.85 | 0.2432 | 1.0924 | 0.85 | 0.8348 | 0.1512 | 0.0433 | | No log | 40.0 | 280 | 0.5437 | 0.85 | 0.2438 | 1.0911 | 0.85 | 0.8348 | 0.1726 | 0.0438 | | No log | 41.0 | 287 | 0.5438 | 0.85 | 0.2434 | 1.0925 | 0.85 | 0.8348 | 0.1704 | 0.0433 | | No log | 42.0 | 294 | 0.5428 | 0.85 | 0.2432 | 1.0927 | 0.85 | 0.8348 | 0.1585 | 0.0436 | | No log | 43.0 | 301 | 0.5455 | 0.85 | 0.2443 | 1.0907 | 0.85 | 0.8348 | 0.1756 | 0.0437 | | No log | 44.0 | 308 | 0.5427 | 0.85 | 0.2433 | 1.0908 | 0.85 | 0.8348 | 0.1616 | 0.0433 | | No log | 45.0 | 315 | 0.5456 | 0.85 | 0.2446 | 1.0878 | 0.85 | 0.8348 | 0.1767 | 0.0437 | | No log | 46.0 | 322 | 0.5439 | 0.85 | 0.2438 | 1.0895 | 0.85 | 0.8348 | 0.1503 | 0.0435 | | No log | 47.0 | 329 | 0.5448 | 0.85 | 0.2443 | 1.0891 | 0.85 | 0.8348 | 0.1674 | 0.0439 | | No log | 48.0 | 336 | 0.5440 | 0.85 | 0.2437 | 1.0898 | 0.85 | 0.8348 | 0.1768 | 0.0437 | | No log | 49.0 | 343 | 0.5443 | 0.85 | 0.2441 | 1.0883 | 0.85 | 0.8348 | 0.1433 | 0.0432 | | No log | 50.0 | 350 | 0.5449 | 0.85 | 0.2444 | 1.0877 | 0.85 | 0.8348 | 0.1722 | 0.0436 | | No log | 51.0 | 357 | 0.5443 | 0.85 | 0.2442 | 1.0871 | 0.85 | 0.8348 | 0.1606 | 0.0434 | | No log | 52.0 | 364 | 0.5453 | 0.85 | 0.2444 | 1.0865 | 0.85 | 0.8348 | 0.1729 | 0.0436 | | No log | 53.0 | 371 | 0.5433 | 0.845 | 0.2438 | 1.0873 | 0.845 | 0.8287 | 0.1570 | 0.0434 | | No log | 54.0 | 378 | 0.5453 | 0.85 | 0.2447 | 1.0854 | 0.85 | 0.8348 | 0.1606 | 0.0435 | | No log | 55.0 | 385 | 0.5438 | 0.85 | 0.2439 | 1.0868 | 0.85 | 0.8348 | 0.1721 | 0.0434 | | No log | 56.0 | 392 | 0.5455 | 0.85 | 0.2447 | 1.0853 | 0.85 | 0.8348 | 0.1710 | 0.0437 | | No log | 57.0 | 399 | 0.5435 | 0.85 | 0.2439 | 1.0864 | 0.85 | 0.8348 | 0.1540 | 0.0434 | | No log | 58.0 | 406 | 0.5451 | 0.85 | 0.2447 | 1.0844 | 0.85 | 0.8348 | 0.1636 | 0.0436 | | No log | 59.0 | 413 | 0.5442 | 0.85 | 0.2441 | 1.0858 | 0.85 | 0.8348 | 0.1556 | 0.0435 | | No log | 60.0 | 420 | 0.5453 | 0.85 | 0.2447 | 1.0843 | 0.85 | 0.8348 | 0.1717 | 0.0437 | | No log | 61.0 | 427 | 0.5439 | 0.85 | 0.2442 | 1.0847 | 0.85 | 0.8348 | 0.1541 | 0.0432 | | No log | 62.0 | 434 | 0.5455 | 0.85 | 0.2449 | 1.0839 | 0.85 | 0.8348 | 0.1550 | 0.0435 | | No log | 63.0 | 441 | 0.5446 | 0.85 | 0.2445 | 1.0843 | 0.85 | 0.8348 | 0.1553 | 0.0435 | | No log | 64.0 | 448 | 0.5448 | 0.85 | 0.2446 | 1.0833 | 0.85 | 0.8348 | 0.1634 | 0.0435 | | No log | 65.0 | 455 | 0.5443 | 0.85 | 0.2443 | 1.0847 | 0.85 | 0.8348 | 0.1554 | 0.0435 | | No log | 66.0 | 462 | 0.5448 | 0.85 | 0.2447 | 1.0831 | 0.85 | 0.8348 | 0.1547 | 0.0436 | | No log | 67.0 | 469 | 0.5452 | 0.85 | 0.2448 | 1.0828 | 0.85 | 0.8348 | 0.1563 | 0.0436 | | No log | 68.0 | 476 | 0.5443 | 0.85 | 0.2444 | 1.0834 | 0.85 | 0.8348 | 0.1472 | 0.0434 | | No log | 69.0 | 483 | 0.5447 | 0.85 | 0.2445 | 1.0832 | 0.85 | 0.8348 | 0.1632 | 0.0434 | | No log | 70.0 | 490 | 0.5447 | 0.85 | 0.2446 | 1.0831 | 0.85 | 0.8348 | 0.1559 | 0.0435 | | No log | 71.0 | 497 | 0.5447 | 0.85 | 0.2446 | 1.0829 | 0.85 | 0.8348 | 0.1473 | 0.0435 | | 0.1823 | 72.0 | 504 | 0.5443 | 0.85 | 0.2444 | 1.0828 | 0.85 | 0.8348 | 0.1559 | 0.0434 | | 0.1823 | 73.0 | 511 | 0.5447 | 0.85 | 0.2447 | 1.0825 | 0.85 | 0.8348 | 0.1472 | 0.0434 | | 0.1823 | 74.0 | 518 | 0.5444 | 0.85 | 0.2444 | 1.0829 | 0.85 | 0.8348 | 0.1559 | 0.0436 | | 0.1823 | 75.0 | 525 | 0.5446 | 0.85 | 0.2445 | 1.0829 | 0.85 | 0.8348 | 0.1557 | 0.0435 | | 0.1823 | 76.0 | 532 | 0.5448 | 0.85 | 0.2445 | 1.0825 | 0.85 | 0.8348 | 0.1559 | 0.0435 | | 0.1823 | 77.0 | 539 | 0.5443 | 0.85 | 0.2444 | 1.0827 | 0.85 | 0.8348 | 0.1558 | 0.0435 | | 0.1823 | 78.0 | 546 | 0.5446 | 0.85 | 0.2446 | 1.0824 | 0.85 | 0.8348 | 0.1560 | 0.0436 | | 0.1823 | 79.0 | 553 | 0.5450 | 0.85 | 0.2448 | 1.0821 | 0.85 | 0.8348 | 0.1637 | 0.0436 | | 0.1823 | 80.0 | 560 | 0.5447 | 0.85 | 0.2446 | 1.0823 | 0.85 | 0.8348 | 0.1638 | 0.0436 | | 0.1823 | 81.0 | 567 | 0.5446 | 0.85 | 0.2446 | 1.0820 | 0.85 | 0.8348 | 0.1560 | 0.0435 | | 0.1823 | 82.0 | 574 | 0.5447 | 0.85 | 0.2446 | 1.0819 | 0.85 | 0.8348 | 0.1561 | 0.0435 | | 0.1823 | 83.0 | 581 | 0.5448 | 0.85 | 0.2446 | 1.0822 | 0.85 | 0.8348 | 0.1550 | 0.0436 | | 0.1823 | 84.0 | 588 | 0.5445 | 0.85 | 0.2446 | 1.0819 | 0.85 | 0.8348 | 0.1551 | 0.0435 | | 0.1823 | 85.0 | 595 | 0.5446 | 0.85 | 0.2446 | 1.0818 | 0.85 | 0.8348 | 0.1560 | 0.0436 | | 0.1823 | 86.0 | 602 | 0.5446 | 0.85 | 0.2446 | 1.0818 | 0.85 | 0.8348 | 0.1560 | 0.0435 | | 0.1823 | 87.0 | 609 | 0.5448 | 0.85 | 0.2447 | 1.0820 | 0.85 | 0.8348 | 0.1560 | 0.0435 | | 0.1823 | 88.0 | 616 | 0.5447 | 0.85 | 0.2446 | 1.0819 | 0.85 | 0.8348 | 0.1551 | 0.0435 | | 0.1823 | 89.0 | 623 | 0.5446 | 0.85 | 0.2446 | 1.0819 | 0.85 | 0.8348 | 0.1560 | 0.0435 | | 0.1823 | 90.0 | 630 | 0.5446 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1638 | 0.0436 | | 0.1823 | 91.0 | 637 | 0.5446 | 0.85 | 0.2445 | 1.0817 | 0.85 | 0.8348 | 0.1474 | 0.0435 | | 0.1823 | 92.0 | 644 | 0.5445 | 0.85 | 0.2445 | 1.0818 | 0.85 | 0.8348 | 0.1551 | 0.0436 | | 0.1823 | 93.0 | 651 | 0.5447 | 0.85 | 0.2446 | 1.0818 | 0.85 | 0.8348 | 0.1560 | 0.0436 | | 0.1823 | 94.0 | 658 | 0.5447 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1561 | 0.0436 | | 0.1823 | 95.0 | 665 | 0.5447 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1550 | 0.0435 | | 0.1823 | 96.0 | 672 | 0.5446 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1474 | 0.0436 | | 0.1823 | 97.0 | 679 | 0.5446 | 0.85 | 0.2446 | 1.0817 | 0.85 | 0.8348 | 0.1551 | 0.0436 | | 0.1823 | 98.0 | 686 | 0.5446 | 0.85 | 0.2446 | 1.0817 | 0.85 | 0.8348 | 0.1474 | 0.0436 | | 0.1823 | 99.0 | 693 | 0.5446 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1474 | 0.0436 | | 0.1823 | 100.0 | 700 | 0.5446 | 0.85 | 0.2446 | 1.0816 | 0.85 | 0.8348 | 0.1474 | 0.0436 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

vk21/ppo-SnowballTarget-unit5

vk21

2023-07-10T20:34:28Z

10

0

ml-agents

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

reinforcement-learning

2023-07-10T20:34:22Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: vk21/ppo-SnowballTarget-unit5 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

DarkAirforce/dqn-SpaceInvadersNoFrameskip-v4

DarkAirforce

2023-07-10T20:33:23Z

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-07T19:24:04Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 534.00 +/- 175.24 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DarkAirforce -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga DarkAirforce -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga DarkAirforce ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO

skrl

2023-07-10T20:15:49Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T19:47:18Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: -38.54 +/- 17.49 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FactoryTaskNutBoltPlace type: IsaacGymEnvs-FactoryTaskNutBoltPlace ---  # IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FactoryTaskNutBoltPlace - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FactoryTaskNutBoltPlace-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 120 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 30 # 120 * 128 / 512 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 1e-4 cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0.016 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2

ALM-AHME

2023-07-10T20:09:00Z

213

0

transformers

[ "transformers", "pytorch", "convnextv2", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-10T20:08:11Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2 results: [] ---  # convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-V2 This model is a fine-tuned version of [ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20](https://huggingface.co/ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20) 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 12 ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

ruggedmug/ppo-LunarLander-v2

ruggedmug

2023-07-10T20:06:30Z

0

stable-baselines3

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

reinforcement-learning

2023-07-07T20:09:57Z

--- 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: 280.76 +/- 15.33 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 ... ```

BigSalmon/InformalToFormalLincoln103Paraphrase

BigSalmon

2023-07-10T19:36:48Z

209

0

transformers

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

text-generation

2023-07-08T22:41:06Z

data: https://github.com/BigSalmon2/InformalToFormalDataset Text Generation Informal Formal ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln103Paraphrase") ``` ``` 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. ``` ``` 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. ``` ``` 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. ``` ``` ideas: in modern-day america, it is customary for the commander-in-chief to conduct regular press conferences related keywords: transparency, check and balance, sacrosanct, public accountability, adversarial, unscripted, direct access, open government, watchdog, healthy democracy, institutional integrity, right to know, direct line of communication, behind closed doors, updates, track progress, instill confidence, reassure, humanize, leadership style, day-to-day, forthcoming, demystify, ask hard questions *** ideas: i know this one guy who retired so young, attesting to how careful they were with money. related keywords: money management, resourceful, penny-pinching, live below their means, frugal, financial discipline, financial independence, conservative, long-term vision, discretionary spending, deferred gratification, preparedness, self-control, cushion ``` ``` less specific: actors and musicians should ( support democracy ). clarifies: actors and musicians should ( wield their celebrity to amplify pro-democracy messaging / marshal their considerable influence in the service of the democratic cause ). *** less specific: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( be careful ). clarifies: amid a contemporary culture that thrives on profligacy, the discipline necessary to retire early is a vanishing quality. rather than yielding to the lure of indulgence, the aspiring retiree must ( master their desires / exercise self-restraint / embrace frugality / restrain their appetite for splendor ). ``` ``` dull: clean emotional heft: spotless, immaculate, pristine *** dull: hot emotional heft: scorching, searing, blistering *** dull: happy emotional heft: euphoric ``` ``` text: {guide: vividly describe the premise of the show "seinfield"} -> set in the heart of bustling new york city, the sitcom "seinfield" follows the everyday {restrict: term that implies they get into trouble but in a comical way} -> misadventures of four neurotic friends. on any given episode, one can find them quarreling over their favorite diner's latest menu change, haggling over the division of household expenses, or contriving a scheme to spy on the apartment's newest resident. mundane as their exploits may be, they never fail to elicit a hearty laugh. {guide: mention how larry david is responsible} -> behind the show's witty, incisive dialogue lies the sharp pen of larry david, who co-created the show with jerry seinfeld. {guide: mention how larry david came up with the most recognizable lines} -> it is his genius that conjured such instantly {restrict: term that imply everybody knows them} -> recognizable quips as "no soup for you!" and "not that there's anything wrong with that!". {guide: mention how humanity should revel in having such good comedians these days} -> as this list of laugh-out-loud lines attests, the world is fortunate to ( count such a sharp comedic mind among its ranks / have such a talented humorist in its midst / have such comedic talent gracing its airwaves ). ``` ``` system: i'm not sure i agree with your assessment of the situation. user: replace "assessment" with a more casual term. chatbot: ( take / read / view ). system: i've been feeling really busy lately, trying to balance work and family responsibilities. user: replace "busy" with a more intense word that conveys it is too much. chatbot: overwhelmed. system: the candidate's stance on immigration is quite nebulous, and it's difficult to discern exactly what they would do if elected. user: replace "nebulous" with a word that conveys a lack of detail. chatbot: ( vague / ambiguous ). ``` ``` user: exorbitant in price, scholarly journals unconscionably define the breadth of one's knowledge awareness by the contents of their wallet. [replace “knowledge awareness” with a more natural expression] chatbot: intellectual horizons. user: can you do another alternative to “intellectual horizons” that has more relation to “scholarly journals”? chatbot: academic enlightenment. ``` ``` key: calculate. syn: estimate, consider, weigh, number, count, apportion, proportion, investigate, reckon, rate, compute. ant: guess, conjecture, hit, chance, risk, stake, miscalculate. ``` ``` description: more forceful version of curious that is less forceful than nosy answer: inquisitive description: more forceful version of hopeful that is less forceful than overconfident answer: optimistic ``` ``` key: inquisitive positive: curious, interested negative: nosy, prying *** key: witty positive: clever, humorous negative: sarcastic, caustic *** key: influential positive: impactful, powerful negative: overbearing, domineering ``` ``` defective: the blogger's { use of language imprecise } confused an already complicated issue. precise: the blogger's ( vague wording ) confused an already complicated issue. defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals. precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals. ``` ``` example: the new car uses gas. boring: uses stronger: guzzles example: he hates people that are rude. boring: hates stronger: loathes, abhors, despises, scorns, detests ``` ``` initial: The music at the party was [ loud; replace with a word that suggests a more uncomfortable noise level ] and overwhelming. modified: The music at the party was { ear-splitting } and overwhelming. initial: their house is [ small; replace with a positive spin ]. modified: their house is { cozy }. ``` ``` defective: they spent the weekend enjoying { time do what you want }. precise: they spent the weekend enjoying ( leisure activities). defective: the author rightly notes the inequities perpetuated by { employment based on who you know }. precise: the author rightly notes the inequities perpetuated by ( nepotism ). defective: the senator's speech was high on { words sounding dignified } but low on concrete proposals. precise: the senator's speech was high on ( lofty rhetoric ) but low on concrete proposals. ``` ``` persona: human resources manager buzzwords: pipeline, talent, retention, compensation, flexible, recruitment, personnel, resume, competitive, quality, onboard ```

NasimB/gpt2-cocnat-aochildes-mod-sub-length-10k

NasimB

2023-07-10T19:27:45Z

16

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T17:32:01Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-aochildes-mod-sub-length-10k results: [] ---  # gpt2-cocnat-aochildes-mod-sub-length-10k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3425 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6933 | 0.29 | 500 | 5.6341 | | 5.3469 | 0.59 | 1000 | 5.1996 | | 4.9864 | 0.88 | 1500 | 4.9580 | | 4.7189 | 1.18 | 2000 | 4.8083 | | 4.5609 | 1.47 | 2500 | 4.6850 | | 4.4523 | 1.77 | 3000 | 4.5821 | | 4.317 | 2.06 | 3500 | 4.5146 | | 4.1329 | 2.35 | 4000 | 4.4652 | | 4.1086 | 2.65 | 4500 | 4.4071 | | 4.0635 | 2.94 | 5000 | 4.3601 | | 3.8482 | 3.24 | 5500 | 4.3553 | | 3.8055 | 3.53 | 6000 | 4.3282 | | 3.7859 | 3.83 | 6500 | 4.2926 | | 3.6619 | 4.12 | 7000 | 4.2970 | | 3.5196 | 4.41 | 7500 | 4.2933 | | 3.5139 | 4.71 | 8000 | 4.2857 | | 3.4905 | 5.0 | 8500 | 4.2710 | | 3.3203 | 5.3 | 9000 | 4.2871 | | 3.322 | 5.59 | 9500 | 4.2867 | | 3.3172 | 5.89 | 10000 | 4.2863 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

grace-pro/afriberta-small-finetuned-hausa

grace-pro

2023-07-10T19:21:16Z

105

0

transformers

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

token-classification

2023-07-10T18:52:05Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: afriberta-small-finetuned-hausa results: [] ---  # afriberta-small-finetuned-hausa This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1444 - Precision: 0.6873 - Recall: 0.4713 - F1: 0.5592 - Accuracy: 0.9618 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1493 | 1.0 | 2624 | 0.1382 | 0.6423 | 0.3968 | 0.4905 | 0.9572 | | 0.1259 | 2.0 | 5248 | 0.1319 | 0.6734 | 0.4415 | 0.5333 | 0.9603 | | 0.106 | 3.0 | 7872 | 0.1385 | 0.6908 | 0.4502 | 0.5452 | 0.9611 | | 0.0949 | 4.0 | 10496 | 0.1377 | 0.6752 | 0.4759 | 0.5583 | 0.9613 | | 0.086 | 5.0 | 13120 | 0.1444 | 0.6873 | 0.4713 | 0.5592 | 0.9618 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jordyvl/vit-small_tobacco3482_kd_CEKD_t2.5_a0.5

jordyvl

2023-07-10T19:16:42Z

166

0

transformers

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

image-classification

2023-07-10T18:37:44Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t2.5_a0.5 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4300 - Accuracy: 0.83 - Brier Loss: 0.2807 - Nll: 1.0350 - F1 Micro: 0.83 - F1 Macro: 0.8295 - Ece: 0.2287 - Aurc: 0.0560 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.6525 | 0.225 | 0.8757 | 5.3231 | 0.225 | 0.1387 | 0.2689 | 0.6977 | | No log | 2.0 | 14 | 1.3106 | 0.405 | 0.7470 | 3.3487 | 0.405 | 0.2195 | 0.2936 | 0.4032 | | No log | 3.0 | 21 | 0.9127 | 0.585 | 0.5785 | 1.8686 | 0.585 | 0.5142 | 0.2974 | 0.2067 | | No log | 4.0 | 28 | 0.7280 | 0.715 | 0.4339 | 1.6780 | 0.715 | 0.6761 | 0.2672 | 0.1204 | | No log | 5.0 | 35 | 0.6523 | 0.775 | 0.3676 | 1.6537 | 0.775 | 0.7619 | 0.2554 | 0.0929 | | No log | 6.0 | 42 | 0.5888 | 0.785 | 0.3502 | 1.3926 | 0.785 | 0.7538 | 0.2277 | 0.0908 | | No log | 7.0 | 49 | 0.6113 | 0.805 | 0.3326 | 1.7118 | 0.805 | 0.7903 | 0.2428 | 0.0803 | | No log | 8.0 | 56 | 0.5404 | 0.785 | 0.3178 | 1.1557 | 0.785 | 0.7671 | 0.2183 | 0.0716 | | No log | 9.0 | 63 | 0.5380 | 0.82 | 0.3051 | 1.3231 | 0.82 | 0.8072 | 0.2168 | 0.0773 | | No log | 10.0 | 70 | 0.6035 | 0.775 | 0.3508 | 1.3888 | 0.775 | 0.7682 | 0.2191 | 0.0812 | | No log | 11.0 | 77 | 0.5473 | 0.795 | 0.3202 | 1.2622 | 0.795 | 0.7740 | 0.2303 | 0.0626 | | No log | 12.0 | 84 | 0.4860 | 0.825 | 0.2937 | 1.3575 | 0.825 | 0.8053 | 0.2392 | 0.0727 | | No log | 13.0 | 91 | 0.5046 | 0.81 | 0.3032 | 1.1857 | 0.81 | 0.8086 | 0.2248 | 0.0564 | | No log | 14.0 | 98 | 0.4745 | 0.825 | 0.2870 | 1.2338 | 0.825 | 0.8089 | 0.2441 | 0.0518 | | No log | 15.0 | 105 | 0.4764 | 0.81 | 0.2943 | 1.0325 | 0.81 | 0.8110 | 0.1935 | 0.0556 | | No log | 16.0 | 112 | 0.4918 | 0.81 | 0.3062 | 1.0551 | 0.81 | 0.8015 | 0.2198 | 0.0587 | | No log | 17.0 | 119 | 0.4757 | 0.815 | 0.2970 | 1.4203 | 0.815 | 0.7965 | 0.2263 | 0.0850 | | No log | 18.0 | 126 | 0.4586 | 0.825 | 0.2926 | 1.0361 | 0.825 | 0.8268 | 0.2279 | 0.0583 | | No log | 19.0 | 133 | 0.4503 | 0.835 | 0.2855 | 1.1476 | 0.835 | 0.8301 | 0.2392 | 0.0589 | | No log | 20.0 | 140 | 0.4780 | 0.805 | 0.3105 | 0.9928 | 0.805 | 0.7902 | 0.1988 | 0.0775 | | No log | 21.0 | 147 | 0.4965 | 0.8 | 0.3205 | 1.1887 | 0.8000 | 0.8029 | 0.2410 | 0.0702 | | No log | 22.0 | 154 | 0.4753 | 0.815 | 0.3016 | 0.9609 | 0.815 | 0.8169 | 0.2163 | 0.0580 | | No log | 23.0 | 161 | 0.4733 | 0.8 | 0.3074 | 1.2566 | 0.8000 | 0.8001 | 0.2162 | 0.0704 | | No log | 24.0 | 168 | 0.4472 | 0.815 | 0.2888 | 1.0352 | 0.815 | 0.8187 | 0.2317 | 0.0590 | | No log | 25.0 | 175 | 0.4434 | 0.815 | 0.2854 | 0.9874 | 0.815 | 0.8186 | 0.2149 | 0.0554 | | No log | 26.0 | 182 | 0.4316 | 0.82 | 0.2754 | 1.0477 | 0.82 | 0.8267 | 0.2195 | 0.0508 | | No log | 27.0 | 189 | 0.4276 | 0.83 | 0.2751 | 1.1016 | 0.83 | 0.8336 | 0.2050 | 0.0525 | | No log | 28.0 | 196 | 0.4329 | 0.82 | 0.2795 | 1.0537 | 0.82 | 0.8220 | 0.2158 | 0.0611 | | No log | 29.0 | 203 | 0.4327 | 0.82 | 0.2827 | 1.1766 | 0.82 | 0.8237 | 0.2024 | 0.0603 | | No log | 30.0 | 210 | 0.4317 | 0.82 | 0.2820 | 1.0331 | 0.82 | 0.8219 | 0.2083 | 0.0611 | | No log | 31.0 | 217 | 0.4316 | 0.82 | 0.2803 | 1.0974 | 0.82 | 0.8263 | 0.1984 | 0.0575 | | No log | 32.0 | 224 | 0.4340 | 0.82 | 0.2833 | 1.0384 | 0.82 | 0.8240 | 0.2202 | 0.0590 | | No log | 33.0 | 231 | 0.4333 | 0.81 | 0.2824 | 1.0355 | 0.81 | 0.8160 | 0.2103 | 0.0586 | | No log | 34.0 | 238 | 0.4309 | 0.83 | 0.2817 | 1.1015 | 0.83 | 0.8307 | 0.2107 | 0.0577 | | No log | 35.0 | 245 | 0.4321 | 0.82 | 0.2817 | 1.0359 | 0.82 | 0.8229 | 0.2147 | 0.0590 | | No log | 36.0 | 252 | 0.4304 | 0.825 | 0.2802 | 1.1016 | 0.825 | 0.8257 | 0.2137 | 0.0569 | | No log | 37.0 | 259 | 0.4303 | 0.825 | 0.2811 | 1.0990 | 0.825 | 0.8268 | 0.2149 | 0.0581 | | No log | 38.0 | 266 | 0.4314 | 0.825 | 0.2814 | 1.1003 | 0.825 | 0.8257 | 0.2163 | 0.0581 | | No log | 39.0 | 273 | 0.4302 | 0.82 | 0.2806 | 1.1007 | 0.82 | 0.8226 | 0.2102 | 0.0576 | | No log | 40.0 | 280 | 0.4307 | 0.825 | 0.2809 | 1.0376 | 0.825 | 0.8264 | 0.2049 | 0.0573 | | No log | 41.0 | 287 | 0.4303 | 0.82 | 0.2808 | 1.0434 | 0.82 | 0.8226 | 0.2096 | 0.0574 | | No log | 42.0 | 294 | 0.4310 | 0.825 | 0.2817 | 1.0376 | 0.825 | 0.8268 | 0.2140 | 0.0580 | | No log | 43.0 | 301 | 0.4310 | 0.825 | 0.2813 | 1.0391 | 0.825 | 0.8257 | 0.2147 | 0.0580 | | No log | 44.0 | 308 | 0.4301 | 0.825 | 0.2808 | 1.0389 | 0.825 | 0.8257 | 0.2064 | 0.0573 | | No log | 45.0 | 315 | 0.4305 | 0.83 | 0.2811 | 1.0419 | 0.83 | 0.8307 | 0.2300 | 0.0577 | | No log | 46.0 | 322 | 0.4303 | 0.82 | 0.2808 | 1.0423 | 0.82 | 0.8226 | 0.2197 | 0.0582 | | No log | 47.0 | 329 | 0.4304 | 0.825 | 0.2811 | 1.0405 | 0.825 | 0.8257 | 0.2240 | 0.0580 | | No log | 48.0 | 336 | 0.4300 | 0.82 | 0.2805 | 1.0407 | 0.82 | 0.8226 | 0.2105 | 0.0574 | | No log | 49.0 | 343 | 0.4307 | 0.825 | 0.2812 | 1.0381 | 0.825 | 0.8257 | 0.2252 | 0.0577 | | No log | 50.0 | 350 | 0.4304 | 0.82 | 0.2810 | 1.0422 | 0.82 | 0.8226 | 0.2353 | 0.0578 | | No log | 51.0 | 357 | 0.4310 | 0.825 | 0.2813 | 1.0382 | 0.825 | 0.8264 | 0.2153 | 0.0569 | | No log | 52.0 | 364 | 0.4309 | 0.82 | 0.2814 | 1.0380 | 0.82 | 0.8226 | 0.2282 | 0.0574 | | No log | 53.0 | 371 | 0.4307 | 0.825 | 0.2813 | 1.0357 | 0.825 | 0.8264 | 0.2250 | 0.0568 | | No log | 54.0 | 378 | 0.4305 | 0.82 | 0.2810 | 1.0366 | 0.82 | 0.8226 | 0.2284 | 0.0575 | | No log | 55.0 | 385 | 0.4304 | 0.825 | 0.2811 | 1.0351 | 0.825 | 0.8264 | 0.2241 | 0.0566 | | No log | 56.0 | 392 | 0.4308 | 0.825 | 0.2813 | 1.0369 | 0.825 | 0.8257 | 0.2414 | 0.0572 | | No log | 57.0 | 399 | 0.4305 | 0.825 | 0.2810 | 1.0356 | 0.825 | 0.8257 | 0.2322 | 0.0571 | | No log | 58.0 | 406 | 0.4302 | 0.82 | 0.2808 | 1.0359 | 0.82 | 0.8226 | 0.2368 | 0.0569 | | No log | 59.0 | 413 | 0.4302 | 0.82 | 0.2809 | 1.0346 | 0.82 | 0.8226 | 0.2271 | 0.0569 | | No log | 60.0 | 420 | 0.4303 | 0.82 | 0.2809 | 1.0357 | 0.82 | 0.8226 | 0.2272 | 0.0570 | | No log | 61.0 | 427 | 0.4304 | 0.825 | 0.2810 | 1.0360 | 0.825 | 0.8257 | 0.2325 | 0.0569 | | No log | 62.0 | 434 | 0.4303 | 0.825 | 0.2809 | 1.0360 | 0.825 | 0.8257 | 0.2321 | 0.0568 | | No log | 63.0 | 441 | 0.4303 | 0.83 | 0.2809 | 1.0356 | 0.83 | 0.8295 | 0.2300 | 0.0562 | | No log | 64.0 | 448 | 0.4304 | 0.825 | 0.2810 | 1.0347 | 0.825 | 0.8264 | 0.2242 | 0.0564 | | No log | 65.0 | 455 | 0.4301 | 0.83 | 0.2808 | 1.0361 | 0.83 | 0.8295 | 0.2384 | 0.0564 | | No log | 66.0 | 462 | 0.4303 | 0.83 | 0.2810 | 1.0359 | 0.83 | 0.8295 | 0.2293 | 0.0563 | | No log | 67.0 | 469 | 0.4302 | 0.83 | 0.2809 | 1.0360 | 0.83 | 0.8295 | 0.2386 | 0.0564 | | No log | 68.0 | 476 | 0.4304 | 0.83 | 0.2810 | 1.0360 | 0.83 | 0.8295 | 0.2384 | 0.0563 | | No log | 69.0 | 483 | 0.4305 | 0.83 | 0.2812 | 1.0355 | 0.83 | 0.8295 | 0.2295 | 0.0564 | | No log | 70.0 | 490 | 0.4302 | 0.825 | 0.2808 | 1.0354 | 0.825 | 0.8264 | 0.2239 | 0.0561 | | No log | 71.0 | 497 | 0.4305 | 0.83 | 0.2812 | 1.0352 | 0.83 | 0.8295 | 0.2296 | 0.0564 | | 0.1776 | 72.0 | 504 | 0.4303 | 0.83 | 0.2808 | 1.0356 | 0.83 | 0.8295 | 0.2287 | 0.0561 | | 0.1776 | 73.0 | 511 | 0.4301 | 0.825 | 0.2807 | 1.0351 | 0.825 | 0.8264 | 0.2348 | 0.0563 | | 0.1776 | 74.0 | 518 | 0.4304 | 0.83 | 0.2811 | 1.0353 | 0.83 | 0.8295 | 0.2195 | 0.0562 | | 0.1776 | 75.0 | 525 | 0.4301 | 0.825 | 0.2808 | 1.0355 | 0.825 | 0.8257 | 0.2320 | 0.0568 | | 0.1776 | 76.0 | 532 | 0.4302 | 0.83 | 0.2808 | 1.0348 | 0.83 | 0.8295 | 0.2289 | 0.0561 | | 0.1776 | 77.0 | 539 | 0.4301 | 0.83 | 0.2808 | 1.0355 | 0.83 | 0.8295 | 0.2300 | 0.0562 | | 0.1776 | 78.0 | 546 | 0.4301 | 0.83 | 0.2808 | 1.0354 | 0.83 | 0.8295 | 0.2394 | 0.0563 | | 0.1776 | 79.0 | 553 | 0.4302 | 0.83 | 0.2809 | 1.0346 | 0.83 | 0.8295 | 0.2287 | 0.0560 | | 0.1776 | 80.0 | 560 | 0.4302 | 0.83 | 0.2809 | 1.0353 | 0.83 | 0.8295 | 0.2299 | 0.0563 | | 0.1776 | 81.0 | 567 | 0.4302 | 0.83 | 0.2809 | 1.0350 | 0.83 | 0.8295 | 0.2299 | 0.0563 | | 0.1776 | 82.0 | 574 | 0.4302 | 0.83 | 0.2808 | 1.0354 | 0.83 | 0.8295 | 0.2298 | 0.0560 | | 0.1776 | 83.0 | 581 | 0.4302 | 0.83 | 0.2809 | 1.0350 | 0.83 | 0.8295 | 0.2299 | 0.0561 | | 0.1776 | 84.0 | 588 | 0.4299 | 0.83 | 0.2807 | 1.0352 | 0.83 | 0.8295 | 0.2287 | 0.0561 | | 0.1776 | 85.0 | 595 | 0.4301 | 0.83 | 0.2808 | 1.0349 | 0.83 | 0.8295 | 0.2296 | 0.0562 | | 0.1776 | 86.0 | 602 | 0.4301 | 0.83 | 0.2808 | 1.0351 | 0.83 | 0.8295 | 0.2287 | 0.0562 | | 0.1776 | 87.0 | 609 | 0.4300 | 0.83 | 0.2807 | 1.0351 | 0.83 | 0.8295 | 0.2297 | 0.0561 | | 0.1776 | 88.0 | 616 | 0.4300 | 0.83 | 0.2807 | 1.0349 | 0.83 | 0.8295 | 0.2287 | 0.0562 | | 0.1776 | 89.0 | 623 | 0.4300 | 0.83 | 0.2807 | 1.0353 | 0.83 | 0.8295 | 0.2296 | 0.0560 | | 0.1776 | 90.0 | 630 | 0.4300 | 0.83 | 0.2807 | 1.0349 | 0.83 | 0.8295 | 0.2297 | 0.0559 | | 0.1776 | 91.0 | 637 | 0.4300 | 0.83 | 0.2807 | 1.0352 | 0.83 | 0.8295 | 0.2296 | 0.0562 | | 0.1776 | 92.0 | 644 | 0.4300 | 0.83 | 0.2807 | 1.0351 | 0.83 | 0.8295 | 0.2287 | 0.0561 | | 0.1776 | 93.0 | 651 | 0.4300 | 0.83 | 0.2807 | 1.0351 | 0.83 | 0.8295 | 0.2297 | 0.0562 | | 0.1776 | 94.0 | 658 | 0.4300 | 0.83 | 0.2807 | 1.0349 | 0.83 | 0.8295 | 0.2297 | 0.0560 | | 0.1776 | 95.0 | 665 | 0.4300 | 0.83 | 0.2807 | 1.0350 | 0.83 | 0.8295 | 0.2297 | 0.0562 | | 0.1776 | 96.0 | 672 | 0.4300 | 0.83 | 0.2807 | 1.0349 | 0.83 | 0.8295 | 0.2296 | 0.0561 | | 0.1776 | 97.0 | 679 | 0.4300 | 0.83 | 0.2807 | 1.0350 | 0.83 | 0.8295 | 0.2296 | 0.0560 | | 0.1776 | 98.0 | 686 | 0.4300 | 0.83 | 0.2807 | 1.0350 | 0.83 | 0.8295 | 0.2296 | 0.0560 | | 0.1776 | 99.0 | 693 | 0.4300 | 0.83 | 0.2807 | 1.0350 | 0.83 | 0.8295 | 0.2287 | 0.0560 | | 0.1776 | 100.0 | 700 | 0.4300 | 0.83 | 0.2807 | 1.0350 | 0.83 | 0.8295 | 0.2287 | 0.0560 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

NasimB/gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k

NasimB

2023-07-10T19:10:16Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T16:50:38Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k results: [] ---  # gpt2-dp-all-mod-datasets-rarity-all-iorder-13k-2p6k This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.4226 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.7606 | 0.29 | 500 | 5.6940 | | 5.4347 | 0.59 | 1000 | 5.2560 | | 5.0945 | 0.88 | 1500 | 5.0226 | | 4.8232 | 1.18 | 2000 | 4.8777 | | 4.675 | 1.47 | 2500 | 4.7626 | | 4.5767 | 1.77 | 3000 | 4.6625 | | 4.4488 | 2.06 | 3500 | 4.5933 | | 4.2612 | 2.36 | 4000 | 4.5563 | | 4.245 | 2.65 | 4500 | 4.4882 | | 4.208 | 2.94 | 5000 | 4.4332 | | 3.9773 | 3.24 | 5500 | 4.4362 | | 3.9484 | 3.53 | 6000 | 4.4046 | | 3.9304 | 3.83 | 6500 | 4.3669 | | 3.7943 | 4.12 | 7000 | 4.3731 | | 3.6517 | 4.42 | 7500 | 4.3646 | | 3.646 | 4.71 | 8000 | 4.3456 | | 3.6381 | 5.01 | 8500 | 4.3333 | | 3.3812 | 5.3 | 9000 | 4.3586 | | 3.3875 | 5.59 | 9500 | 4.3536 | | 3.3847 | 5.89 | 10000 | 4.3483 | | 3.2816 | 6.18 | 10500 | 4.3600 | | 3.2295 | 6.48 | 11000 | 4.3636 | | 3.223 | 6.77 | 11500 | 4.3630 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

darthPanda/ppo-Huggy-v0

darthPanda

2023-07-10T18:57:02Z

14

0

ml-agents

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

reinforcement-learning

2023-07-10T18:55:55Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash 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. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: darthPanda/ppo-Huggy-v0 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

43m1m4n/jpbrinx

43m1m4n

2023-07-10T18:53:46Z

0

null

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

null

2023-06-04T20:40:20Z

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

MaitreHibou/Reinforce-Cartpole-v1

MaitreHibou

2023-07-10T18:49:32Z

0

null

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

reinforcement-learning

2023-07-10T18:49:23Z

--- 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: 500.00 +/- 0.00 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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

DavidSolan0/coverart

DavidSolan0

2023-07-10T18:34:53Z

9

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T18:30:01Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### coverart Dreambooth model trained by DavidSolan0 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

simonestradasch/COMPner-bert-base-spanish-wwm-cased

simonestradasch

2023-07-10T18:28:38Z

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "es", "dataset:simonestradasch/NERcomp", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-07-10T18:07:06Z

--- language: - es tags: - generated_from_trainer datasets: - simonestradasch/NERcomp model-index: - name: COMPner-bert-base-spanish-wwm-cased results: [] ---  # COMPner-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the simonestradasch/NERcomp dataset. It achieves the following results on the evaluation set: - Loss: 0.2793 - Body Part Precision: 0.6700 - Body Part Recall: 0.7186 - Body Part F1: 0.6934 - Body Part Number: 565 - Disease Precision: 0.6966 - Disease Recall: 0.7533 - Disease F1: 0.7238 - Disease Number: 1350 - Family Member Precision: 0.9 - Family Member Recall: 0.75 - Family Member F1: 0.8182 - Family Member Number: 24 - Medication Precision: 0.7143 - Medication Recall: 0.6190 - Medication F1: 0.6633 - Medication Number: 105 - Procedure Precision: 0.5233 - Procedure Recall: 0.5125 - Procedure F1: 0.5178 - Procedure Number: 439 - Overall Precision: 0.6640 - Overall Recall: 0.6971 - Overall F1: 0.6802 - Overall Accuracy: 0.9136 ## 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: 13 - 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 | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.4741 | 1.0 | 703 | 0.2932 | 0.6449 | 0.6301 | 0.6374 | 565 | 0.6984 | 0.7170 | 0.7076 | 1350 | 0.9412 | 0.6667 | 0.7805 | 24 | 0.8551 | 0.5619 | 0.6782 | 105 | 0.5113 | 0.3599 | 0.4225 | 439 | 0.6674 | 0.6271 | 0.6466 | 0.9091 | | 0.259 | 2.0 | 1406 | 0.2793 | 0.6700 | 0.7186 | 0.6934 | 565 | 0.6966 | 0.7533 | 0.7238 | 1350 | 0.9 | 0.75 | 0.8182 | 24 | 0.7143 | 0.6190 | 0.6633 | 105 | 0.5233 | 0.5125 | 0.5178 | 439 | 0.6640 | 0.6971 | 0.6802 | 0.9136 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

PraveenJesu/openai-whisper-medium-peft-lora-v2.2.5

PraveenJesu

2023-07-10T18:28:05Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-10T18:28:04Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

MaitreHibou/dqn-SpaceInvadersNoFrameskip-v4

MaitreHibou

2023-07-10T18:21:47Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T18:21:06Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 656.50 +/- 140.98 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MaitreHibou -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MaitreHibou -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MaitreHibou ``` ## Hyperparameters ```python OrderedDict([('batch_size', 64), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0002), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

skrl/IsaacGymEnvs-AnymalTerrain-PPO

skrl

2023-07-10T18:15:29Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-01-24T20:41:55Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 19.88 +/- 0.5 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-AnymalTerrain type: IsaacGymEnvs-AnymalTerrain ---  # IsaacGymEnvs-AnymalTerrain-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** AnymalTerrain - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-AnymalTerrain-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 24 # memory_size cfg["learning_epochs"] = 5 cfg["mini_batches"] = 6 # 24 * 4096 / 16384 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 3e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.001 cfg["value_loss_scale"] = 1.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = None cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

FerhatDk/wav2vec2-base-finetuned-ks

FerhatDk

2023-07-10T18:08:04Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2022-09-22T08:59:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks results: [] ---  # wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3550 - Accuracy: 0.8727 ## 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: 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: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 8 | 0.6840 | 0.6 | | 0.6867 | 2.0 | 16 | 0.6780 | 0.6364 | | 0.6742 | 3.0 | 24 | 0.6601 | 0.6182 | | 0.6446 | 4.0 | 32 | 0.6294 | 0.6364 | | 0.6299 | 5.0 | 40 | 0.6002 | 0.6727 | | 0.6299 | 6.0 | 48 | 0.5755 | 0.7091 | | 0.6021 | 7.0 | 56 | 0.5530 | 0.7273 | | 0.5678 | 8.0 | 64 | 0.5036 | 0.8182 | | 0.5512 | 9.0 | 72 | 0.4753 | 0.8545 | | 0.4784 | 10.0 | 80 | 0.4184 | 0.9273 | | 0.4784 | 11.0 | 88 | 0.4102 | 0.8909 | | 0.4515 | 12.0 | 96 | 0.4444 | 0.8182 | | 0.4878 | 13.0 | 104 | 0.3780 | 0.9091 | | 0.4418 | 14.0 | 112 | 0.4570 | 0.8 | | 0.4746 | 15.0 | 120 | 0.3870 | 0.8545 | | 0.4746 | 16.0 | 128 | 0.3932 | 0.8364 | | 0.4226 | 17.0 | 136 | 0.2779 | 0.9636 | | 0.4301 | 18.0 | 144 | 0.3125 | 0.9455 | | 0.3482 | 19.0 | 152 | 0.3212 | 0.9091 | | 0.3611 | 20.0 | 160 | 0.3925 | 0.8364 | | 0.3611 | 21.0 | 168 | 0.3389 | 0.8909 | | 0.3507 | 22.0 | 176 | 0.3099 | 0.8727 | | 0.3241 | 23.0 | 184 | 0.3120 | 0.8727 | | 0.2533 | 24.0 | 192 | 0.2313 | 0.9455 | | 0.2466 | 25.0 | 200 | 0.3550 | 0.8727 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Bellaaazzzzz/model_archive

Bellaaazzzzz

2023-07-10T18:00:43Z

2

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-07-10T17:41:57Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_archive These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png)

jordyvl/vit-small_tobacco3482_kd_CEKD_t1.5_a0.7

jordyvl

2023-07-10T17:57:06Z

166

0

transformers

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

image-classification

2023-07-10T17:18:33Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-small_tobacco3482_kd_CEKD_t1.5_a0.7 results: [] ---  # vit-small_tobacco3482_kd_CEKD_t1.5_a0.7 This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4797 - Accuracy: 0.835 - Brier Loss: 0.2522 - Nll: 0.8627 - F1 Micro: 0.835 - F1 Macro: 0.8222 - Ece: 0.1830 - Aurc: 0.0434 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 7 | 1.9341 | 0.215 | 0.8749 | 5.3238 | 0.2150 | 0.1264 | 0.2642 | 0.6914 | | No log | 2.0 | 14 | 1.5320 | 0.405 | 0.7410 | 3.5078 | 0.405 | 0.2276 | 0.2957 | 0.4015 | | No log | 3.0 | 21 | 1.0532 | 0.635 | 0.5629 | 2.0153 | 0.635 | 0.5844 | 0.3037 | 0.2006 | | No log | 4.0 | 28 | 0.7915 | 0.715 | 0.4093 | 1.6974 | 0.715 | 0.6762 | 0.2420 | 0.1131 | | No log | 5.0 | 35 | 0.8024 | 0.745 | 0.3869 | 1.7109 | 0.745 | 0.7548 | 0.2160 | 0.1006 | | No log | 6.0 | 42 | 0.7162 | 0.765 | 0.3351 | 1.8105 | 0.765 | 0.7599 | 0.2216 | 0.0874 | | No log | 7.0 | 49 | 0.6966 | 0.785 | 0.3304 | 1.5292 | 0.785 | 0.7682 | 0.2058 | 0.0979 | | No log | 8.0 | 56 | 0.6317 | 0.805 | 0.2995 | 1.3486 | 0.805 | 0.7887 | 0.2266 | 0.0721 | | No log | 9.0 | 63 | 0.6903 | 0.805 | 0.3304 | 1.5866 | 0.805 | 0.7971 | 0.2371 | 0.0995 | | No log | 10.0 | 70 | 0.6223 | 0.805 | 0.2940 | 1.3478 | 0.805 | 0.8114 | 0.2281 | 0.0697 | | No log | 11.0 | 77 | 0.6350 | 0.795 | 0.3145 | 1.3386 | 0.795 | 0.7730 | 0.2063 | 0.0962 | | No log | 12.0 | 84 | 0.5570 | 0.835 | 0.2666 | 1.2662 | 0.835 | 0.8181 | 0.1951 | 0.0553 | | No log | 13.0 | 91 | 0.5610 | 0.81 | 0.2858 | 1.2619 | 0.81 | 0.8002 | 0.1884 | 0.0626 | | No log | 14.0 | 98 | 0.5843 | 0.8 | 0.2961 | 1.0782 | 0.8000 | 0.8083 | 0.1993 | 0.0683 | | No log | 15.0 | 105 | 0.5918 | 0.78 | 0.2965 | 1.1207 | 0.78 | 0.7861 | 0.1895 | 0.0634 | | No log | 16.0 | 112 | 0.5541 | 0.84 | 0.2765 | 1.3189 | 0.8400 | 0.8455 | 0.1969 | 0.0597 | | No log | 17.0 | 119 | 0.5037 | 0.835 | 0.2568 | 0.9024 | 0.835 | 0.8248 | 0.2083 | 0.0499 | | No log | 18.0 | 126 | 0.5050 | 0.85 | 0.2563 | 1.0032 | 0.85 | 0.8441 | 0.2147 | 0.0580 | | No log | 19.0 | 133 | 0.5430 | 0.815 | 0.2779 | 1.1046 | 0.815 | 0.8044 | 0.1906 | 0.0562 | | No log | 20.0 | 140 | 0.5276 | 0.84 | 0.2743 | 0.9964 | 0.8400 | 0.8144 | 0.2104 | 0.0597 | | No log | 21.0 | 147 | 0.5155 | 0.835 | 0.2686 | 0.9556 | 0.835 | 0.8210 | 0.1962 | 0.0572 | | No log | 22.0 | 154 | 0.4937 | 0.835 | 0.2581 | 1.0079 | 0.835 | 0.8172 | 0.1975 | 0.0479 | | No log | 23.0 | 161 | 0.4931 | 0.845 | 0.2533 | 1.0021 | 0.845 | 0.8270 | 0.1884 | 0.0503 | | No log | 24.0 | 168 | 0.4869 | 0.83 | 0.2554 | 0.9660 | 0.83 | 0.8084 | 0.1945 | 0.0481 | | No log | 25.0 | 175 | 0.4843 | 0.845 | 0.2512 | 0.9979 | 0.845 | 0.8316 | 0.1746 | 0.0466 | | No log | 26.0 | 182 | 0.4866 | 0.835 | 0.2531 | 0.9006 | 0.835 | 0.8188 | 0.1833 | 0.0472 | | No log | 27.0 | 189 | 0.4882 | 0.825 | 0.2562 | 0.8929 | 0.825 | 0.8043 | 0.2023 | 0.0469 | | No log | 28.0 | 196 | 0.4814 | 0.82 | 0.2494 | 0.9122 | 0.82 | 0.8060 | 0.1773 | 0.0451 | | No log | 29.0 | 203 | 0.4749 | 0.835 | 0.2501 | 0.8770 | 0.835 | 0.8252 | 0.1688 | 0.0442 | | No log | 30.0 | 210 | 0.4761 | 0.84 | 0.2490 | 0.8848 | 0.8400 | 0.8250 | 0.2068 | 0.0443 | | No log | 31.0 | 217 | 0.4787 | 0.845 | 0.2508 | 0.8754 | 0.845 | 0.8309 | 0.1635 | 0.0438 | | No log | 32.0 | 224 | 0.4791 | 0.835 | 0.2521 | 0.8711 | 0.835 | 0.8224 | 0.1876 | 0.0446 | | No log | 33.0 | 231 | 0.4779 | 0.84 | 0.2509 | 0.8650 | 0.8400 | 0.8252 | 0.1813 | 0.0436 | | No log | 34.0 | 238 | 0.4774 | 0.84 | 0.2513 | 0.8662 | 0.8400 | 0.8252 | 0.1919 | 0.0441 | | No log | 35.0 | 245 | 0.4760 | 0.835 | 0.2502 | 0.8636 | 0.835 | 0.8224 | 0.1840 | 0.0434 | | No log | 36.0 | 252 | 0.4784 | 0.84 | 0.2509 | 0.8688 | 0.8400 | 0.8281 | 0.1691 | 0.0437 | | No log | 37.0 | 259 | 0.4771 | 0.835 | 0.2507 | 0.8670 | 0.835 | 0.8224 | 0.1936 | 0.0440 | | No log | 38.0 | 266 | 0.4764 | 0.835 | 0.2499 | 0.8614 | 0.835 | 0.8224 | 0.1830 | 0.0434 | | No log | 39.0 | 273 | 0.4769 | 0.835 | 0.2503 | 0.8651 | 0.835 | 0.8224 | 0.2001 | 0.0438 | | No log | 40.0 | 280 | 0.4777 | 0.84 | 0.2514 | 0.8608 | 0.8400 | 0.8281 | 0.1832 | 0.0435 | | No log | 41.0 | 287 | 0.4777 | 0.835 | 0.2504 | 0.8650 | 0.835 | 0.8224 | 0.1953 | 0.0437 | | No log | 42.0 | 294 | 0.4779 | 0.835 | 0.2511 | 0.8629 | 0.835 | 0.8224 | 0.1944 | 0.0440 | | No log | 43.0 | 301 | 0.4790 | 0.835 | 0.2519 | 0.8631 | 0.835 | 0.8222 | 0.1808 | 0.0439 | | No log | 44.0 | 308 | 0.4777 | 0.835 | 0.2509 | 0.8604 | 0.835 | 0.8222 | 0.1886 | 0.0435 | | No log | 45.0 | 315 | 0.4787 | 0.835 | 0.2517 | 0.8620 | 0.835 | 0.8222 | 0.1940 | 0.0437 | | No log | 46.0 | 322 | 0.4774 | 0.84 | 0.2509 | 0.8614 | 0.8400 | 0.8281 | 0.1779 | 0.0433 | | No log | 47.0 | 329 | 0.4785 | 0.835 | 0.2517 | 0.8609 | 0.835 | 0.8222 | 0.1811 | 0.0438 | | No log | 48.0 | 336 | 0.4792 | 0.835 | 0.2521 | 0.8611 | 0.835 | 0.8222 | 0.1849 | 0.0436 | | No log | 49.0 | 343 | 0.4771 | 0.84 | 0.2509 | 0.8623 | 0.8400 | 0.8281 | 0.1908 | 0.0430 | | No log | 50.0 | 350 | 0.4793 | 0.835 | 0.2520 | 0.8633 | 0.835 | 0.8222 | 0.1900 | 0.0435 | | No log | 51.0 | 357 | 0.4786 | 0.83 | 0.2517 | 0.8654 | 0.83 | 0.8159 | 0.1684 | 0.0437 | | No log | 52.0 | 364 | 0.4792 | 0.83 | 0.2521 | 0.8625 | 0.83 | 0.8166 | 0.1915 | 0.0430 | | No log | 53.0 | 371 | 0.4785 | 0.835 | 0.2513 | 0.8652 | 0.835 | 0.8222 | 0.1853 | 0.0434 | | No log | 54.0 | 378 | 0.4798 | 0.835 | 0.2523 | 0.8652 | 0.835 | 0.8222 | 0.1767 | 0.0437 | | No log | 55.0 | 385 | 0.4791 | 0.835 | 0.2519 | 0.8637 | 0.835 | 0.8222 | 0.1891 | 0.0435 | | No log | 56.0 | 392 | 0.4790 | 0.835 | 0.2519 | 0.8614 | 0.835 | 0.8222 | 0.1749 | 0.0429 | | No log | 57.0 | 399 | 0.4782 | 0.835 | 0.2513 | 0.8625 | 0.835 | 0.8222 | 0.1909 | 0.0433 | | No log | 58.0 | 406 | 0.4794 | 0.835 | 0.2521 | 0.8602 | 0.835 | 0.8222 | 0.1758 | 0.0435 | | No log | 59.0 | 413 | 0.4790 | 0.835 | 0.2517 | 0.8617 | 0.835 | 0.8222 | 0.1754 | 0.0432 | | No log | 60.0 | 420 | 0.4791 | 0.835 | 0.2520 | 0.8614 | 0.835 | 0.8222 | 0.1830 | 0.0430 | | No log | 61.0 | 427 | 0.4789 | 0.835 | 0.2518 | 0.8612 | 0.835 | 0.8222 | 0.1870 | 0.0432 | | No log | 62.0 | 434 | 0.4792 | 0.835 | 0.2520 | 0.8620 | 0.835 | 0.8222 | 0.1902 | 0.0433 | | No log | 63.0 | 441 | 0.4789 | 0.835 | 0.2518 | 0.8619 | 0.835 | 0.8222 | 0.1997 | 0.0431 | | No log | 64.0 | 448 | 0.4797 | 0.835 | 0.2523 | 0.8607 | 0.835 | 0.8222 | 0.1833 | 0.0434 | | No log | 65.0 | 455 | 0.4797 | 0.835 | 0.2522 | 0.8624 | 0.835 | 0.8222 | 0.1922 | 0.0434 | | No log | 66.0 | 462 | 0.4791 | 0.835 | 0.2519 | 0.8620 | 0.835 | 0.8222 | 0.1894 | 0.0430 | | No log | 67.0 | 469 | 0.4792 | 0.835 | 0.2520 | 0.8612 | 0.835 | 0.8222 | 0.1885 | 0.0433 | | No log | 68.0 | 476 | 0.4796 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1918 | 0.0433 | | No log | 69.0 | 483 | 0.4793 | 0.835 | 0.2521 | 0.8628 | 0.835 | 0.8222 | 0.1828 | 0.0433 | | No log | 70.0 | 490 | 0.4792 | 0.835 | 0.2519 | 0.8622 | 0.835 | 0.8222 | 0.1918 | 0.0432 | | No log | 71.0 | 497 | 0.4797 | 0.835 | 0.2523 | 0.8615 | 0.835 | 0.8222 | 0.1836 | 0.0434 | | 0.194 | 72.0 | 504 | 0.4797 | 0.835 | 0.2522 | 0.8618 | 0.835 | 0.8222 | 0.1842 | 0.0433 | | 0.194 | 73.0 | 511 | 0.4794 | 0.835 | 0.2521 | 0.8624 | 0.835 | 0.8222 | 0.1914 | 0.0432 | | 0.194 | 74.0 | 518 | 0.4794 | 0.835 | 0.2521 | 0.8617 | 0.835 | 0.8222 | 0.1915 | 0.0431 | | 0.194 | 75.0 | 525 | 0.4796 | 0.835 | 0.2522 | 0.8623 | 0.835 | 0.8222 | 0.1917 | 0.0434 | | 0.194 | 76.0 | 532 | 0.4795 | 0.835 | 0.2520 | 0.8622 | 0.835 | 0.8222 | 0.1985 | 0.0433 | | 0.194 | 77.0 | 539 | 0.4795 | 0.835 | 0.2520 | 0.8623 | 0.835 | 0.8222 | 0.1985 | 0.0432 | | 0.194 | 78.0 | 546 | 0.4795 | 0.835 | 0.2522 | 0.8621 | 0.835 | 0.8222 | 0.1981 | 0.0432 | | 0.194 | 79.0 | 553 | 0.4798 | 0.835 | 0.2522 | 0.8626 | 0.835 | 0.8222 | 0.1909 | 0.0433 | | 0.194 | 80.0 | 560 | 0.4796 | 0.835 | 0.2521 | 0.8630 | 0.835 | 0.8222 | 0.1984 | 0.0433 | | 0.194 | 81.0 | 567 | 0.4797 | 0.835 | 0.2522 | 0.8619 | 0.835 | 0.8222 | 0.1902 | 0.0434 | | 0.194 | 82.0 | 574 | 0.4797 | 0.835 | 0.2522 | 0.8631 | 0.835 | 0.8222 | 0.1913 | 0.0433 | | 0.194 | 83.0 | 581 | 0.4797 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1909 | 0.0433 | | 0.194 | 84.0 | 588 | 0.4797 | 0.835 | 0.2522 | 0.8623 | 0.835 | 0.8222 | 0.1909 | 0.0433 | | 0.194 | 85.0 | 595 | 0.4797 | 0.835 | 0.2522 | 0.8624 | 0.835 | 0.8222 | 0.1909 | 0.0434 | | 0.194 | 86.0 | 602 | 0.4796 | 0.835 | 0.2522 | 0.8623 | 0.835 | 0.8222 | 0.1830 | 0.0433 | | 0.194 | 87.0 | 609 | 0.4797 | 0.835 | 0.2522 | 0.8629 | 0.835 | 0.8222 | 0.1909 | 0.0434 | | 0.194 | 88.0 | 616 | 0.4797 | 0.835 | 0.2521 | 0.8634 | 0.835 | 0.8222 | 0.1830 | 0.0433 | | 0.194 | 89.0 | 623 | 0.4797 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1910 | 0.0434 | | 0.194 | 90.0 | 630 | 0.4798 | 0.835 | 0.2523 | 0.8627 | 0.835 | 0.8222 | 0.1909 | 0.0434 | | 0.194 | 91.0 | 637 | 0.4797 | 0.835 | 0.2522 | 0.8625 | 0.835 | 0.8222 | 0.1909 | 0.0434 | | 0.194 | 92.0 | 644 | 0.4797 | 0.835 | 0.2522 | 0.8630 | 0.835 | 0.8222 | 0.1830 | 0.0434 | | 0.194 | 93.0 | 651 | 0.4798 | 0.835 | 0.2522 | 0.8629 | 0.835 | 0.8222 | 0.1910 | 0.0434 | | 0.194 | 94.0 | 658 | 0.4797 | 0.835 | 0.2522 | 0.8628 | 0.835 | 0.8222 | 0.1910 | 0.0434 | | 0.194 | 95.0 | 665 | 0.4797 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1910 | 0.0434 | | 0.194 | 96.0 | 672 | 0.4798 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1834 | 0.0435 | | 0.194 | 97.0 | 679 | 0.4797 | 0.835 | 0.2522 | 0.8628 | 0.835 | 0.8222 | 0.1830 | 0.0434 | | 0.194 | 98.0 | 686 | 0.4797 | 0.835 | 0.2522 | 0.8628 | 0.835 | 0.8222 | 0.1830 | 0.0434 | | 0.194 | 99.0 | 693 | 0.4797 | 0.835 | 0.2522 | 0.8628 | 0.835 | 0.8222 | 0.1830 | 0.0434 | | 0.194 | 100.0 | 700 | 0.4797 | 0.835 | 0.2522 | 0.8627 | 0.835 | 0.8222 | 0.1830 | 0.0434 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

FerhatDk/wav2vec2-base_music_speech_both_classification

FerhatDk

2023-07-10T17:56:34Z

167

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-10T17:00:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base_music_speech_both_classification results: [] ---  # wav2vec2-base_music_speech_both_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Accuracy: 0.9847 ## 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: 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: 0.1 - lr_scheduler_warmup_steps: 500 - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9458 | 1.0 | 66 | 0.8468 | 0.7405 | | 0.3785 | 2.0 | 132 | 0.2951 | 0.9771 | | 0.1762 | 3.0 | 198 | 0.2639 | 0.9313 | | 0.134 | 4.0 | 264 | 0.1084 | 0.9771 | | 0.0782 | 5.0 | 330 | 0.0877 | 0.9771 | | 0.0568 | 6.0 | 396 | 0.0912 | 0.9771 | | 0.0122 | 7.0 | 462 | 0.4056 | 0.9198 | | 0.059 | 8.0 | 528 | 0.0586 | 0.9847 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

simonestradasch/fake-news-bert-base-spanish-wwm-cased

simonestradasch

2023-07-10T17:35:24Z

108

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-10T17:29:57Z

--- tags: - generated_from_trainer metrics: - f1 model-index: - name: fake-news-bert-base-spanish-wwm-cased results: [] ---  # fake-news-bert-base-spanish-wwm-cased This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4163 - F1: 0.8558 ## 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: 13 - 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 | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4947 | 1.0 | 140 | 0.4019 | 0.8137 | | 0.2068 | 2.0 | 280 | 0.4163 | 0.8558 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

grammarly/detexd-roberta-base

grammarly

2023-07-10T17:34:23Z

132

10

transformers

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

text-classification

2023-03-21T18:44:55Z

--- license: apache-2.0 language: - en pipeline_tag: text-classification --- # DeTexD-RoBERTa-base delicate text detection This is a baseline RoBERTa-base model for the delicate text detection task. * Paper: [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO) * [GitHub repository](https://github.com/grammarly/detexd) The labels meaning according to the paper: - LABEL_0 -> non-delicate (0) - LABEL_1 -> very low risk (1) - LABEL_2 -> low risk (2) - LABEL_3 -> medium risk (3) - LABEL_4 -> high risk (4) - LABEL_5 -> very high risk (5) ## Classification example code Here's a short usage example with the torch library in a binary classification task: ```python from transformers import pipeline classifier = pipeline("text-classification", model="grammarly/detexd-roberta-base") def predict_binary_score(text: str): # get multiclass probability scores scores = classifier(text, top_k=None) # convert to a single score by summing the probability scores # for the higher-index classes return sum(score['score'] for score in scores if score['label'] in ('LABEL_3', 'LABEL_4', 'LABEL_5')) def predict_delicate(text: str, threshold=0.72496545): return predict_binary_score(text) > threshold print(predict_delicate("Time flies like an arrow. Fruit flies like a banana.")) ``` Expected output: ``` False ``` ## Citation Information ``` @inproceedings{chernodub-etal-2023-detexd, title = "{D}e{T}ex{D}: A Benchmark Dataset for Delicate Text Detection", author = "Yavnyi, Serhii and Sliusarenko, Oleksii and Razzaghi, Jade and Mo, Yichen and Hovakimyan, Knar and Chernodub, Artem", booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.woah-1.2", pages = "14--28", abstract = "Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called {``}delicate text.{''} We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.", } ```

cagarraz/rl_course_vizdoom_health_gathering_supreme

cagarraz

2023-07-10T17:23:21Z

0

sample-factory

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

reinforcement-learning

2023-07-10T17:23:08Z

--- 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: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 3.94 +/- 0.20 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r cagarraz/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

komo-dono/harukatomatsu

komo-dono

2023-07-10T17:05:31Z

0

null

[ "region:us" ]

null

2023-07-10T17:03:49Z

--- license: openrail language: - ja tags: - music haruka tomatsu 600 epoch

opendiffusion/sentimentcheck

opendiffusion

2023-07-10T16:58:49Z

0

tf-keras

[ "tf-keras", "bert", "region:us" ]

null

2023-05-11T18:26:04Z

# Intro OpenDiffusion's SentimentCheck is an AI model built upon Tensorflow+Keras+Pickles. SentimentCheck harnesses the power of deep learning algorithms to accurately classify sentiment in text, making it a flexible tool for businesses, researchers, and developers. ## Usage --- language: - en - nl - de - fr - it - es license: mit --- # bert-base-multilingual-uncased-sentiment This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for direct use as a sentiment analysis model for product reviews in any of the six languages above, or for further finetuning on related sentiment analysis tasks. ## Training data Here is the number of product reviews we used for finetuning the model: | Language | Number of reviews | | -------- | ----------------- | | English | 150k | | Dutch | 80k | | German | 137k | | French | 140k | | Italian | 72k | | Spanish | 50k | ## Accuracy The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages: - Accuracy (exact) is the exact match on the number of stars. - Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer. | Language | Accuracy (exact) | Accuracy (off-by-1) | | -------- | ---------------------- | ------------------- | | English | 67% | 95% | Dutch | 57% | 93% | German | 61% | 94% | French | 59% | 94% | Italian | 59% | 95% | Spanish | 58% | 95%

Buth/fatuh

Buth

2023-07-10T16:50:46Z

0

adapter-transformers

[ "adapter-transformers", "en", "dataset:Open-Orca/OpenOrca", "license:openrail", "region:us" ]

null

2023-07-10T16:48:59Z

--- license: openrail datasets: - Open-Orca/OpenOrca language: - en metrics: - accuracy library_name: adapter-transformers ---

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

svalcin

2023-07-10T16:39:14Z

0

null

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

reinforcement-learning

2023-07-10T16:39:10Z

--- 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 playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="svalcin/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"]) ```

dashan1992/dsl2

dashan1992

2023-07-10T16:35:33Z

0

peft

[ "peft", "region:us" ]

null

2023-07-10T16:34:19Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

banden/ppo-LunarLander-v2

banden

2023-07-10T16:23:12Z

0

stable-baselines3

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

reinforcement-learning

2023-07-10T16:22:51Z

--- 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: 250.46 +/- 41.26 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 ... ```

uw-madison/mra-base-4096-8-d3

uw-madison

2023-07-10T16:12:42Z

495

0

transformers

[ "transformers", "pytorch", "mra", "fill-mask", "arxiv:2207.10284", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-06-23T06:36:15Z

# MRA MRA model for masked language modeling (MLM) for sequence length 512. ## About MRA The MRA model was proposed in [Multi Resolution Analysis (MRA) for Approximate Self-Attention](https://arxiv.org/abs/2207.10284) by Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung, and Vikas Singh. The abstract from the paper is the following: *Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention.* This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/mra-attention).

tyavika/Distilbert-QA-Pytorch-seed

tyavika

2023-07-10T16:10:13Z

104

0

transformers

[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2023-07-10T12:52:40Z

--- tags: - generated_from_trainer model-index: - name: Distilbert-QA-Pytorch-seed results: [] ---  # Distilbert-QA-Pytorch-seed This model is a fine-tuned version of [tyavika/Distilbert-QA-Pytorch-seed](https://huggingface.co/tyavika/Distilbert-QA-Pytorch-seed) on the None 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: 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: linear - num_epochs: 10 ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

mitra-mir/setfit_model_labelfaithful_epochs2

mitra-mir

2023-07-10T15:54:42Z

7

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-07-08T13:16:11Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(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={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 22 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 2, "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": 44, "warmup_steps": 5, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors

sianadouglas/ensembletest

sianadouglas

2023-07-10T15:48:14Z

0

null

[ "en", "license:other", "region:us" ]

null

2023-07-10T15:47:23Z

--- license: other language: - en ---

mgmeskill/Pixelcopter-PLE-v0

mgmeskill

2023-07-10T15:38:32Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T15:26:11Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 42.50 +/- 37.13 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Suryabhan/openai-whisper-large-v2-LORA-colab

Suryabhan

2023-07-10T15:32:46Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-10T15:32:41Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0.dev0

tyavika/LR1E4-BS16-Bert_CNN512LSTM256NoBid

tyavika

2023-07-10T15:31:42Z

77

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-09T20:06:29Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: LR1E4-BS16-Bert_CNN512LSTM256NoBid results: [] ---  # LR1E4-BS16-Bert_CNN512LSTM256NoBid 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: 1.6667 ## 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: 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.7267 | 1.0 | 3290 | 1.5092 | | 1.2394 | 2.0 | 6580 | 1.3933 | | 0.8348 | 3.0 | 9870 | 1.5591 | | 0.542 | 4.0 | 13160 | 1.6667 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

MnLgt/textual_inversion_muir_1_5

MnLgt

2023-07-10T15:31:36Z

1

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T14:16:45Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - jordandavis/textual_inversion_muir_1_5 These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.

agercas/speecht5_finetuned_voxpopuli_nl

agercas

2023-07-10T15:27:22Z

78

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:facebook/voxpopuli", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-10T09:21:57Z

--- license: mit tags: - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: speecht5_finetuned_voxpopuli_nl results: [] ---  # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4572 ## 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: 4 - eval_batch_size: 2 - 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 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5221 | 4.3 | 1000 | 0.4774 | | 0.505 | 8.61 | 2000 | 0.4648 | | 0.4929 | 12.91 | 3000 | 0.4583 | | 0.4901 | 17.21 | 4000 | 0.4572 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Trong-Nghia/xlnet-base-cased-detect-dep

Trong-Nghia

2023-07-10T15:17:37Z

90

0

transformers

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

text-classification

2023-06-30T13:14:00Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: xlnet-base-cased-detect-dep results: [] ---  # xlnet-base-cased-detect-dep This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5555 - Accuracy: 0.744 - F1: 0.8164 ## 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-06 - 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 376 | 0.5582 | 0.716 | 0.8065 | | 0.6188 | 2.0 | 752 | 0.5479 | 0.756 | 0.8232 | | 0.5835 | 3.0 | 1128 | 0.5306 | 0.758 | 0.8276 | | 0.5492 | 4.0 | 1504 | 0.5555 | 0.744 | 0.8164 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Birchlabs/llama-13b-stepwise-embeddings

Birchlabs

2023-07-10T15:17:11Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2023-07-10T13:55:53Z

--- license: apache-2.0 --- Fine-tuned input (`embed_tokens: Embedding`) and output (`lm_head: Linear`) embeddings layers, for use with [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Prior to finetuning: we grew the vocabulary of the tokenizer and embeddings layers. The new embeddings were average-initialized, and needed training, so we trained them. These are the weights from that training. Ordinarily a QLoRA finetune of an LLM would not finetune the `embed_tokens: Embedding` (you'd need to get a bit creative, because not only have the dimensions changed, but also I don't believe any way has been established to train _adapters_ over `Embedding`s). Nor apparently would it finetune `lm_head: Linear`. This is harder than it sounds (i.e. you can't handle it the same way you adapt the other Linear layers), because the dimensions have grown.

EleutherAI/pythia-1b-deduped

EleutherAI

2023-07-10T15:04:31Z

22,714

18

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "causal-lm", "pythia", "en", "dataset:EleutherAI/the_pile_deduplicated", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-02-14T00:07:42Z

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> **This is the current release.** Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-1B-deduped ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:[email protected]). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — | | 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-1B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-1B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-1B-deduped will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-1B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-1B-deduped may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-1B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data Pythia-1B-deduped was trained on the Pile **after the dataset has been globally deduplicated**.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

NICFRU/bart-base-paraphrasing-news

NICFRU

2023-07-10T15:02:02Z

106

0

transformers

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

text2text-generation

2023-07-10T14:46:34Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-paraphrasing results: [] ---  # bart-base-paraphrasing This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6617 - Rouge1: 57.7088 - Rouge2: 51.0096 - Rougel: 54.7514 - Rougelsum: 56.3943 - Gen Len: 20.0 ## 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: 4e-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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 0.2 | 10 | 0.5263 | 58.2676 | 51.5842 | 55.5057 | 57.1584 | 19.94 | | No log | 0.4 | 20 | 0.5050 | 56.1604 | 48.7383 | 54.0373 | 55.372 | 20.0 | | No log | 0.6 | 30 | 0.4674 | 58.0617 | 51.4993 | 56.0368 | 56.9665 | 20.0 | | No log | 0.8 | 40 | 0.4545 | 57.5375 | 51.0203 | 55.5247 | 56.5761 | 19.94 | | No log | 1.0 | 50 | 0.4373 | 57.7263 | 50.8021 | 55.0549 | 56.35 | 19.98 | | No log | 1.2 | 60 | 0.4313 | 57.87 | 50.9904 | 54.9727 | 56.5379 | 19.97 | | No log | 1.4 | 70 | 0.4855 | 56.5101 | 49.3124 | 54.1572 | 55.0671 | 20.0 | | No log | 1.6 | 80 | 0.4202 | 56.6535 | 50.0302 | 53.6891 | 55.1016 | 19.96 | | No log | 1.8 | 90 | 0.4544 | 57.315 | 50.6289 | 54.642 | 55.7326 | 19.95 | | 0.5858 | 2.0 | 100 | 0.4157 | 56.4569 | 48.8105 | 53.937 | 55.3515 | 20.0 | | 0.5858 | 2.2 | 110 | 0.4555 | 57.8424 | 51.5966 | 55.6655 | 56.6862 | 20.0 | | 0.5858 | 2.4 | 120 | 0.4196 | 58.2562 | 51.7596 | 55.5085 | 57.1823 | 19.97 | | 0.5858 | 2.6 | 130 | 0.4334 | 58.6906 | 51.6106 | 55.6631 | 57.5254 | 19.89 | | 0.5858 | 2.8 | 140 | 0.4710 | 56.5401 | 49.33 | 53.8792 | 55.0282 | 20.0 | | 0.5858 | 3.0 | 150 | 0.4357 | 58.2083 | 52.0049 | 55.9938 | 57.1928 | 20.0 | | 0.5858 | 3.2 | 160 | 0.4735 | 58.8112 | 52.2196 | 56.5004 | 57.7703 | 19.94 | | 0.5858 | 3.4 | 170 | 0.4428 | 57.6778 | 50.6377 | 54.8752 | 56.4778 | 20.0 | | 0.5858 | 3.6 | 180 | 0.4983 | 57.4124 | 50.4244 | 54.6163 | 56.0992 | 20.0 | | 0.5858 | 3.8 | 190 | 0.4620 | 58.0701 | 51.5021 | 55.7222 | 56.8737 | 20.0 | | 0.2865 | 4.0 | 200 | 0.4502 | 59.1191 | 52.7516 | 56.4389 | 57.7153 | 20.0 | | 0.2865 | 4.2 | 210 | 0.4805 | 58.9064 | 52.7148 | 56.1058 | 57.6709 | 20.0 | | 0.2865 | 4.4 | 220 | 0.4755 | 58.6883 | 52.1464 | 55.9164 | 57.3825 | 20.0 | | 0.2865 | 4.6 | 230 | 0.4524 | 58.9916 | 52.1101 | 56.4116 | 57.9468 | 19.9 | | 0.2865 | 4.8 | 240 | 0.4726 | 58.9953 | 52.8173 | 56.5846 | 58.0805 | 20.0 | | 0.2865 | 5.0 | 250 | 0.4841 | 58.1058 | 51.614 | 55.3374 | 56.7617 | 20.0 | | 0.2865 | 5.2 | 260 | 0.5047 | 58.2785 | 51.1874 | 55.5336 | 56.8795 | 20.0 | | 0.2865 | 5.4 | 270 | 0.4658 | 57.2753 | 49.6038 | 53.9588 | 55.6038 | 19.91 | | 0.2865 | 5.6 | 280 | 0.5261 | 58.1691 | 51.5254 | 55.2685 | 56.7787 | 20.0 | | 0.2865 | 5.8 | 290 | 0.4833 | 57.8088 | 51.2838 | 54.8739 | 56.4374 | 20.0 | | 0.1668 | 6.0 | 300 | 0.5067 | 58.2021 | 51.3629 | 55.3548 | 56.9093 | 19.99 | | 0.1668 | 6.2 | 310 | 0.5461 | 58.0327 | 51.4051 | 55.3426 | 56.7923 | 20.0 | | 0.1668 | 6.4 | 320 | 0.5463 | 58.1027 | 51.3706 | 55.1733 | 56.7923 | 19.9 | | 0.1668 | 6.6 | 330 | 0.5837 | 57.6284 | 50.8245 | 54.6253 | 56.2127 | 20.0 | | 0.1668 | 6.8 | 340 | 0.5221 | 58.0869 | 51.5448 | 55.4226 | 56.7532 | 20.0 | | 0.1668 | 7.0 | 350 | 0.5433 | 58.7676 | 52.0403 | 56.2634 | 57.6441 | 20.0 | | 0.1668 | 7.2 | 360 | 0.5498 | 57.9172 | 50.9727 | 55.1006 | 56.6018 | 20.0 | | 0.1668 | 7.4 | 370 | 0.5581 | 57.4669 | 50.698 | 54.6448 | 56.1325 | 20.0 | | 0.1668 | 7.6 | 380 | 0.5526 | 57.0821 | 50.298 | 54.1635 | 55.8059 | 20.0 | | 0.1668 | 7.8 | 390 | 0.5548 | 57.5422 | 50.2734 | 54.2446 | 56.1223 | 20.0 | | 0.1071 | 8.0 | 400 | 0.5620 | 57.4548 | 50.2657 | 54.5094 | 55.9422 | 20.0 | | 0.1071 | 8.2 | 410 | 0.5772 | 57.4144 | 50.2443 | 54.5173 | 55.9331 | 20.0 | | 0.1071 | 8.4 | 420 | 0.5857 | 57.2975 | 50.2116 | 54.5918 | 55.9931 | 20.0 | | 0.1071 | 8.6 | 430 | 0.5827 | 58.4767 | 51.4318 | 55.4792 | 57.1284 | 20.0 | | 0.1071 | 8.8 | 440 | 0.5728 | 58.4414 | 51.3523 | 55.2838 | 57.202 | 20.0 | | 0.1071 | 9.0 | 450 | 0.5919 | 58.0499 | 51.3783 | 55.0748 | 56.6939 | 20.0 | | 0.1071 | 9.2 | 460 | 0.5937 | 57.7604 | 50.845 | 54.8941 | 56.351 | 20.0 | | 0.1071 | 9.4 | 470 | 0.5970 | 57.3655 | 50.4126 | 54.4522 | 55.7815 | 20.0 | | 0.1071 | 9.6 | 480 | 0.5911 | 58.203 | 51.0367 | 55.3215 | 56.8485 | 20.0 | | 0.1071 | 9.8 | 490 | 0.6121 | 58.2898 | 51.2749 | 55.4292 | 57.0241 | 20.0 | | 0.0718 | 10.0 | 500 | 0.5903 | 58.2487 | 51.3838 | 55.4237 | 56.8863 | 20.0 | | 0.0718 | 10.2 | 510 | 0.5983 | 58.2681 | 51.0925 | 55.2887 | 56.9562 | 20.0 | | 0.0718 | 10.4 | 520 | 0.6308 | 57.9797 | 50.7386 | 54.995 | 56.5939 | 20.0 | | 0.0718 | 10.6 | 530 | 0.6307 | 57.6269 | 50.5515 | 54.446 | 56.1544 | 20.0 | | 0.0718 | 10.8 | 540 | 0.6173 | 57.9545 | 51.1005 | 54.9406 | 56.5732 | 20.0 | | 0.0718 | 11.0 | 550 | 0.6322 | 58.3718 | 51.4321 | 55.4241 | 57.1879 | 20.0 | | 0.0718 | 11.2 | 560 | 0.6027 | 58.6581 | 51.8607 | 55.6436 | 57.32 | 20.0 | | 0.0718 | 11.4 | 570 | 0.6140 | 58.6476 | 51.7822 | 55.5845 | 57.3018 | 20.0 | | 0.0718 | 11.6 | 580 | 0.6184 | 59.2454 | 52.4204 | 56.2174 | 57.9278 | 20.0 | | 0.0718 | 11.8 | 590 | 0.6281 | 59.2945 | 52.8165 | 56.547 | 58.0674 | 20.0 | | 0.0512 | 12.0 | 600 | 0.6128 | 58.2165 | 51.3689 | 55.37 | 56.8342 | 20.0 | | 0.0512 | 12.2 | 610 | 0.6482 | 57.9196 | 50.9793 | 55.0883 | 56.6986 | 20.0 | | 0.0512 | 12.4 | 620 | 0.6267 | 57.4782 | 50.1118 | 54.2802 | 55.8872 | 20.0 | | 0.0512 | 12.6 | 630 | 0.6198 | 57.457 | 50.4079 | 54.2449 | 55.8118 | 20.0 | | 0.0512 | 12.8 | 640 | 0.6500 | 57.6903 | 51.0627 | 55.0743 | 56.3025 | 20.0 | | 0.0512 | 13.0 | 650 | 0.6265 | 57.4394 | 50.9013 | 54.7936 | 56.1688 | 20.0 | | 0.0512 | 13.2 | 660 | 0.6817 | 58.4345 | 51.7087 | 55.291 | 57.0057 | 20.0 | | 0.0512 | 13.4 | 670 | 0.6322 | 57.869 | 50.9503 | 54.8937 | 56.5178 | 20.0 | | 0.0512 | 13.6 | 680 | 0.6424 | 57.8285 | 51.1014 | 55.0072 | 56.5022 | 20.0 | | 0.0512 | 13.8 | 690 | 0.6668 | 58.7067 | 51.9929 | 55.5044 | 57.1517 | 20.0 | | 0.0397 | 14.0 | 700 | 0.6537 | 58.8717 | 52.4036 | 55.6521 | 57.4855 | 20.0 | | 0.0397 | 14.2 | 710 | 0.6463 | 58.9623 | 52.4749 | 55.8145 | 57.8095 | 20.0 | | 0.0397 | 14.4 | 720 | 0.6630 | 58.8097 | 52.1997 | 55.8204 | 57.6325 | 20.0 | | 0.0397 | 14.6 | 730 | 0.6839 | 59.0479 | 52.6573 | 56.0439 | 57.7322 | 20.0 | | 0.0397 | 14.8 | 740 | 0.6541 | 59.2854 | 52.6109 | 56.1891 | 57.9446 | 20.0 | | 0.0397 | 15.0 | 750 | 0.6486 | 58.8419 | 52.2004 | 55.8071 | 57.49 | 20.0 | | 0.0397 | 15.2 | 760 | 0.6578 | 57.6161 | 50.7276 | 54.5514 | 56.2359 | 20.0 | | 0.0397 | 15.4 | 770 | 0.6673 | 57.5458 | 50.8286 | 54.4597 | 56.1513 | 20.0 | | 0.0397 | 15.6 | 780 | 0.6624 | 57.6634 | 51.0017 | 54.6769 | 56.3837 | 20.0 | | 0.0397 | 15.8 | 790 | 0.6469 | 57.9037 | 51.137 | 54.8939 | 56.6427 | 20.0 | | 0.0301 | 16.0 | 800 | 0.6373 | 57.8696 | 51.0899 | 54.8543 | 56.4596 | 20.0 | | 0.0301 | 16.2 | 810 | 0.6712 | 58.614 | 52.0052 | 55.6436 | 57.3211 | 20.0 | | 0.0301 | 16.4 | 820 | 0.6812 | 58.5214 | 51.8911 | 55.7447 | 57.2663 | 20.0 | | 0.0301 | 16.6 | 830 | 0.6716 | 58.5818 | 51.929 | 55.7993 | 57.4064 | 20.0 | | 0.0301 | 16.8 | 840 | 0.6590 | 57.745 | 51.0481 | 54.8545 | 56.4781 | 20.0 | | 0.0301 | 17.0 | 850 | 0.6695 | 57.6663 | 50.9646 | 54.7863 | 56.3687 | 20.0 | | 0.0301 | 17.2 | 860 | 0.6858 | 57.5552 | 51.0436 | 54.7092 | 56.3079 | 20.0 | | 0.0301 | 17.4 | 870 | 0.6840 | 57.9091 | 51.3823 | 54.8309 | 56.6186 | 20.0 | | 0.0301 | 17.6 | 880 | 0.6751 | 57.8223 | 51.1688 | 54.7562 | 56.5558 | 20.0 | | 0.0301 | 17.8 | 890 | 0.6589 | 57.9956 | 51.1425 | 54.9509 | 56.6868 | 20.0 | | 0.0482 | 18.0 | 900 | 0.6634 | 58.0392 | 51.3121 | 55.0726 | 56.7878 | 20.0 | | 0.0482 | 18.2 | 910 | 0.6907 | 58.2021 | 51.4548 | 55.1874 | 56.91 | 20.0 | | 0.0482 | 18.4 | 920 | 0.6977 | 58.1124 | 51.4254 | 55.062 | 56.8412 | 20.0 | | 0.0482 | 18.6 | 930 | 0.6832 | 58.0776 | 51.3168 | 55.0849 | 56.8226 | 20.0 | | 0.0482 | 18.8 | 940 | 0.6672 | 57.925 | 51.2475 | 54.9661 | 56.655 | 20.0 | | 0.0482 | 19.0 | 950 | 0.6582 | 57.9285 | 51.2483 | 54.9744 | 56.6609 | 20.0 | | 0.0482 | 19.2 | 960 | 0.6575 | 57.9285 | 51.2483 | 54.9744 | 56.6609 | 20.0 | | 0.0482 | 19.4 | 970 | 0.6619 | 57.8961 | 51.2097 | 54.9475 | 56.6344 | 20.0 | | 0.0482 | 19.6 | 980 | 0.6658 | 57.8961 | 51.2097 | 54.9475 | 56.6344 | 20.0 | | 0.0482 | 19.8 | 990 | 0.6635 | 57.7222 | 51.0096 | 54.8166 | 56.4623 | 20.0 | | 0.0201 | 20.0 | 1000 | 0.6617 | 57.7088 | 51.0096 | 54.7514 | 56.3943 | 20.0 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

alienware/layoutlmv3-finetuned-cord_100

alienware

2023-07-10T15:01:59Z

4

0

transformers

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

token-classification

2023-07-09T12:32:12Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - cord-layoutlmv3 metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-cord_100 results: - task: name: Token Classification type: token-classification dataset: name: cord-layoutlmv3 type: cord-layoutlmv3 config: cord split: test args: cord metrics: - name: Precision type: precision value: 0.9569093610698366 - name: Recall type: recall value: 0.9640718562874252 - name: F1 type: f1 value: 0.9604772557792692 - name: Accuracy type: accuracy value: 0.9681663837011885 ---  # layoutlmv3-finetuned-cord_100 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1720 - Precision: 0.9569 - Recall: 0.9641 - F1: 0.9605 - Accuracy: 0.9682 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.56 | 250 | 0.3320 | 0.9011 | 0.9207 | 0.9108 | 0.9253 | | 0.3502 | 3.12 | 500 | 0.2811 | 0.9281 | 0.9371 | 0.9326 | 0.9427 | | 0.3502 | 4.69 | 750 | 0.2429 | 0.9210 | 0.9341 | 0.9275 | 0.9435 | | 0.162 | 6.25 | 1000 | 0.2264 | 0.9385 | 0.9476 | 0.9430 | 0.9542 | | 0.162 | 7.81 | 1250 | 0.1996 | 0.9373 | 0.9513 | 0.9443 | 0.9601 | | 0.0971 | 9.38 | 1500 | 0.1686 | 0.9569 | 0.9633 | 0.9601 | 0.9690 | | 0.0971 | 10.94 | 1750 | 0.1814 | 0.9532 | 0.9603 | 0.9567 | 0.9652 | | 0.0704 | 12.5 | 2000 | 0.1915 | 0.9539 | 0.9611 | 0.9575 | 0.9656 | | 0.0704 | 14.06 | 2250 | 0.1833 | 0.9590 | 0.9633 | 0.9612 | 0.9677 | | 0.0513 | 15.62 | 2500 | 0.1720 | 0.9569 | 0.9641 | 0.9605 | 0.9682 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

SHENMU007/neunit_BASE_V10.19

SHENMU007

2023-07-10T15:01:47Z

77

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-07-07T08:50:51Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

NasimB/gpt2-cocnat-mod-datasets-txt-processing

NasimB

2023-07-10T15:01:23Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T12:29:02Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: gpt2-cocnat-mod-datasets-txt-processing results: [] ---  # gpt2-cocnat-mod-datasets-txt-processing This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.3377 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.6848 | 0.3 | 500 | 5.6500 | | 5.3379 | 0.59 | 1000 | 5.2204 | | 4.9909 | 0.89 | 1500 | 4.9703 | | 4.7146 | 1.19 | 2000 | 4.8200 | | 4.5695 | 1.49 | 2500 | 4.7076 | | 4.4685 | 1.78 | 3000 | 4.5985 | | 4.3237 | 2.08 | 3500 | 4.5311 | | 4.1614 | 2.38 | 4000 | 4.4731 | | 4.1267 | 2.68 | 4500 | 4.4151 | | 4.082 | 2.97 | 5000 | 4.3593 | | 3.8448 | 3.27 | 5500 | 4.3575 | | 3.8261 | 3.57 | 6000 | 4.3240 | | 3.8089 | 3.86 | 6500 | 4.2887 | | 3.6462 | 4.16 | 7000 | 4.2921 | | 3.5453 | 4.46 | 7500 | 4.2840 | | 3.529 | 4.76 | 8000 | 4.2688 | | 3.4926 | 5.05 | 8500 | 4.2683 | | 3.3463 | 5.35 | 9000 | 4.2715 | | 3.3453 | 5.65 | 9500 | 4.2702 | | 3.3408 | 5.95 | 10000 | 4.2694 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

rickareo/distilbert-base-uncased-finetuned-emotion

rickareo

2023-07-10T14:59:25Z

105

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

2023-07-10T14:44:04Z

--- 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: split metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9229910973969778 ---  # 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: 0.2155 - Accuracy: 0.923 - F1: 0.9230 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8271 | 1.0 | 250 | 0.3166 | 0.903 | 0.8989 | | 0.2469 | 2.0 | 500 | 0.2155 | 0.923 | 0.9230 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3

ericNguyen0132/DepRoBERTa-2ndStage

ericNguyen0132

2023-07-10T14:56:14Z

105

0

transformers

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

text-classification

2023-07-10T13:42:58Z

--- tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: DepRoBERTa-2ndStage results: [] ---  # DepRoBERTa-2ndStage This model is a fine-tuned version of [rafalposwiata/deproberta-large-v1](https://huggingface.co/rafalposwiata/deproberta-large-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6330 - Accuracy: 0.855 - F1: 0.9134 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 469 | 0.3572 | 0.8617 | 0.9224 | | 0.4953 | 2.0 | 938 | 0.3593 | 0.8783 | 0.9315 | | 0.3493 | 3.0 | 1407 | 0.4274 | 0.8483 | 0.9091 | | 0.313 | 4.0 | 1876 | 0.5488 | 0.8617 | 0.9187 | | 0.2622 | 5.0 | 2345 | 0.6330 | 0.855 | 0.9134 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

pcuenq/falcon-7b-instruct-transformers

pcuenq

2023-07-10T14:54:25Z

7

0

transformers

[ "transformers", "pytorch", "falcon", "text-generation", "en", "dataset:tiiuae/falcon-refinedweb", "arxiv:2205.14135", "arxiv:1911.02150", "arxiv:2005.14165", "arxiv:2104.09864", "arxiv:2306.01116", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-10T12:57:31Z

--- datasets: - tiiuae/falcon-refinedweb language: - en inference: true license: apache-2.0 duplicated_from: pcuenq/falcon-7b-instruct --- # ✨ Falcon-7B-Instruct **Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.** *Paper coming soon 😊.* 🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)! ## Why use Falcon-7B-Instruct? * **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).** * **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). * **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)). 💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). 🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother! ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` 💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!** For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon). You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct. # Model Card for Falcon-7B-Instruct ## Model Details ### Model Description - **Developed by:** [https://www.tii.ae](https://www.tii.ae); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0; - **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Source - **Paper:** *coming soon*. ## Uses ### Direct Use Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets. ### Out-of-Scope Use Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. ## Bias, Risks, and Limitations Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. ### Recommendations We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use. ## How to Get Started with the Model ```python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model = "tiiuae/falcon-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", ) sequences = pipeline( "Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:", max_length=200, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Training Details ### Training Data Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets. | **Data source** | **Fraction** | **Tokens** | **Description** | |--------------------|--------------|------------|-----------------------------------| | [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat | | [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct | | [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct | | [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl | The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer. ## Evaluation *Paper coming soon.* See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results. Note that this model variant is not optimized for NLP benchmarks. ## Technical Specifications For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b). ### Model Architecture and Objective Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences: * **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864)); * **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)); * **Decoder-block:** parallel attention/MLP with a single layer norm. | **Hyperparameter** | **Value** | **Comment** | |--------------------|-----------|----------------------------------------| | Layers | 32 | | | `d_model` | 4544 | Increased to compensate for multiquery | | `head_dim` | 64 | Reduced to optimise for FlashAttention | | Vocabulary | 65024 | | | Sequence length | 2048 | | ### Compute Infrastructure #### Hardware Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances. #### Software Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.) ## Citation *Paper coming soon* 😊. In the meanwhile, you can use the following information to cite: ``` @article{falcon40b, title={{Falcon-40B}: an open large language model with state-of-the-art performance}, author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme}, year={2023} } ``` To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116). ``` @article{refinedweb, title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only}, author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay}, journal={arXiv preprint arXiv:2306.01116}, eprint={2306.01116}, eprinttype = {arXiv}, url={https://arxiv.org/abs/2306.01116}, year={2023} } ``` ## License Falcon-7B-Instruct is made available under the Apache 2.0 license. ## Contact [email protected]

pmpc/de_pipeline

pmpc

2023-07-10T14:53:50Z

1

0

spacy

[ "spacy", "token-classification", "de", "model-index", "region:us" ]

token-classification

2023-07-10T10:51:54Z

--- tags: - spacy - token-classification language: - de model-index: - name: de_pipeline results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9573497322 - name: NER Recall type: recall value: 0.9567803331 - name: NER F Score type: f_score value: 0.9570649479 --- | Feature | Description | | --- | --- | | **Name** | `de_pipeline` | | **Version** | `0.0.0` | | **spaCy** | `>=3.5.3,<3.6.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | n/a | | **License** | n/a | | **Author** | [n/a]() | ### Label Scheme <details> <summary>View label scheme (19 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `AN`, `EUN`, `GRT`, `GS`, `INN`, `LD`, `LDS`, `LIT`, `MRK`, `ORG`, `PER`, `RR`, `RS`, `ST`, `STR`, `UN`, `VO`, `VS`, `VT` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_F` | 95.71 | | `ENTS_P` | 95.73 | | `ENTS_R` | 95.68 | | `TRANSFORMER_LOSS` | 11836.63 | | `NER_LOSS` | 8009.96 |

firecoral/ppo-LunarLander-v2

firecoral

2023-07-10T14:49:32Z

0

stable-baselines3

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

reinforcement-learning

2023-07-10T14:49:10Z

--- 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: 261.63 +/- 20.29 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 ... ```

lizhuang144/flan-t5-base-factual-sg

lizhuang144

2023-07-10T14:34:47Z

109

0

transformers

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

text2text-generation

2023-07-06T11:13:16Z

See details at 'https://github.com/zhuang-li/FACTUAL/tree/main'

marsh5/Reinforce-cartpole

marsh5

2023-07-10T14:31:44Z

0

null

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

reinforcement-learning

2023-07-10T14:31:34Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 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 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Birchlabs/llama-13b-stepwise-tokenizer

Birchlabs

2023-07-10T14:25:54Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2023-07-10T13:49:42Z

--- license: apache-2.0 --- forked from https://huggingface.co/huggyllama/llama-13b/tree/main This tokenizer supports [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter). Adds four new tokens for stepwise reasoning: ``` <|step_start|> <|step_end|> <|answer_start|> <|answer_end|> ``` See [`Birchlabs/llama-13b-stepwise-adapter`](https://huggingface.co/Birchlabs/llama-13b-stepwise-adapter) for details of how all the parts should be used together.

JennnDexter/textual_inversion

JennnDexter

2023-07-10T14:24:31Z

29

0

diffusers

[ "diffusers", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-07T11:57:47Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true --- # Textual inversion text2image fine-tuning - JennnDexter/textual_inversion These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. You can find some example images in the following.

sannne990/meinahentai

sannne990

2023-07-10T14:08:40Z

0

null

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

null

2023-07-10T13:56:40Z

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

iammartian0/speecht5_finetuned_voxpopuli_it

iammartian0

2023-07-10T14:03:39Z

81

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:voxpopuli/it", "license:mit", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-10T11:00:58Z

--- license: mit tags: - generated_from_trainer - text-to-speech datasets: - voxpopuli/it model-index: - name: speecht5_finetuned_voxpopuli_it results: [] ---  # speecht5_finetuned_voxpopuli_it This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli/it dataset. It achieves the following results on the evaluation set: - Loss: 0.4855 ## 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: 4 - eval_batch_size: 2 - 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 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.5467 | 10.58 | 1000 | 0.5003 | | 0.5182 | 21.16 | 2000 | 0.4882 | | 0.5046 | 31.75 | 3000 | 0.4857 | | 0.5013 | 42.33 | 4000 | 0.4855 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

dilip-reddy/ppo-LunarLander

dilip-reddy

2023-07-10T13:57:53Z

4

0

stable-baselines3

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

reinforcement-learning

2023-07-10T13:57:33Z

--- 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: 268.69 +/- 17.74 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 ... ```

cointegrated/rubert-base-lesha17-punctuation

cointegrated

2023-07-10T13:56:54Z

125

1

transformers

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

token-classification

2022-03-02T23:29:05Z

The model for https://github.com/Lesha17/Punctuation; all credits go to the owner of this repository.

JoaoReis/Neuronet

JoaoReis

2023-07-10T13:45:12Z

0

null

[ "region:us" ]

null

2023-07-10T13:29:55Z

import socket,warnings try: socket.setdefaulttimeout(1) socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect(('1.1.1.1', 53)) except socket.error as ex: raise Exception("STOP: No internet. Click '>|' in top right and set 'Internet' switch to on") import os iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '') if iskaggle: !pip install -Uqq fastai !pip install -Uqq duckduckgo_search from duckduckgo_search import ddg_images from fastcore.all import * def search_images(term, max_images=200): return L(ddg_images(term, max_results=max_images)).itemgot('image') urls = search_images(' star fox photos', max_images=1) urls[0] from fastdownload import download_url dest = 'starfox.jpg' download_url(urls[0], dest, show_progress=False) from fastai.vision.all import * im = Image.open(dest) im.to_thumb(256,256) download_url(search_images('eva 01', max_images=1)[0], 'forest.jpg', show_progress=False) Image.open('forest.jpg').to_thumb(256,256) searches = 'eva 01','star fox' path = Path('eva 01_or_not') from time import sleep for o in searches: dest = (path/o) dest.mkdir(exist_ok=True, parents=True) download_images(dest, urls=search_images(f'{o} photo')) sleep(10) # Pause between searches to avoid over-loading server download_images(dest, urls=search_images(f'{o} sun photo')) sleep(10) download_images(dest, urls=search_images(f'{o} shade photo')) sleep(10) resize_images(path/o, max_size=400, dest=path/o) failed = verify_images(get_image_files(path)) failed.map(Path.unlink) len(failed) dls = DataBlock( blocks=(ImageBlock, CategoryBlock), get_items=get_image_files, splitter=RandomSplitter(valid_pct=0.2, seed=42), get_y=parent_label, item_tfms=[Resize(192, method='squish')] ).dataloaders(path) dls.show_batch(max_n=6) learn = vision_learner(dls, resnet18, metrics=error_rate) learn.fine_tune(3) is_bird,_,probs = learn.predict(PILImage.create('bird.jpg')) print(f"This is a: {is_bird}.") print(f"Probability it's a bird: {probs[0]:.4f}")

PKU-Alignment/beaver-dam-7b

PKU-Alignment

2023-07-10T13:42:02Z

1,707

6

safe-rlhf

[ "safe-rlhf", "pytorch", "llama", "beaver", "safety", "ai-safety", "deepspeed", "rlhf", "alpaca", "en", "dataset:PKU-Alignment/BeaverTails", "arxiv:2302.13971", "region:us" ]

null

2023-07-10T02:57:51Z

--- datasets: - PKU-Alignment/BeaverTails language: - en tags: - beaver - safety - llama - ai-safety - deepspeed - rlhf - alpaca library_name: safe-rlhf --- # 🦫 BeaverDam Model Card ## Beaver-Dam-7B Boasting 7 billion parameters, Beaver-Dam-7B is a powerful QA-Moderation model derived from the Llama-7B base model and trained on the [PKU-Alignment/BeaverTails](https://huggingface.co/datasets/PKU-Alignment/BeaverTails) Classification Dataset. Beaver-Dam's key feature is its ability to analyze responses to prompts for toxicity across 14 different categories. - **Developed by:** [PKU-Alignment Team](https://github.com/PKU-Alignment) - **Model type:** QA moderation - **License:** Non-commercial license - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971) ## Model Sources - **Repository:** https://github.com/PKU-Alignment/beavertails - **Web:** https://sites.google.com/view/pku-beavertails - **Paper:** Coming soon ## Why Choose Beaver-Dam-7B? Traditional approaches to content moderation in Question-Answering (QA) tasks often gauge the toxicity of a QA pair by examining each utterance individually. This method, while effective to a degree, can inadvertently result in a significant number of user prompts being discarded. If the moderation system perceives them as too harmful, it may prevent the language model from generating appropriate responses, consequently interrupting the user experience and potentially hindering the evolution of a beneficial AI with human-like understanding. BeaverDam is a shift in the approach to content moderation for QA tasks - a concept we term "QA moderation": ![qa-moderation-teaser.png](qa-moderation-teaser.png) In this paradigm, a QA pair is classified as harmful or benign based on its degree of risk neutrality. Specifically, it assesses the extent to which potential risks in a potentially harmful question can be counteracted by a non-threatening response.

WALIDALI/bekiamzrev

WALIDALI

2023-07-10T13:39:54Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-10T13:33:42Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### bekiamzrev Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

skrl/IsaacGymEnvs-FrankaCabinet-PPO

skrl

2023-07-10T13:39:14Z

0

skrl

[ "skrl", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-01-24T20:43:14Z

--- library_name: skrl tags: - deep-reinforcement-learning - reinforcement-learning - skrl model-index: - name: PPO results: - metrics: - type: mean_reward value: 3368.97 +/- 117.64 name: Total reward (mean) task: type: reinforcement-learning name: reinforcement-learning dataset: name: IsaacGymEnvs-FrankaCabinet type: IsaacGymEnvs-FrankaCabinet ---  # IsaacGymEnvs-FrankaCabinet-PPO Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments. - **Task:** FrankaCabinet - **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html) # Usage (with skrl) Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts. * PyTorch ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FrankaCabinet-PPO", filename="agent.pt") agent.load(path) ``` * JAX ```python from skrl.utils.huggingface import download_model_from_huggingface # assuming that there is an agent named `agent` path = download_model_from_huggingface("skrl/IsaacGymEnvs-FrankaCabinet-PPO", filename="agent.pickle") agent.load(path) ``` # Hyperparameters Note: Undefined parameters keep their values by default. ```python # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters cfg = PPO_DEFAULT_CONFIG.copy() cfg["rollouts"] = 16 # memory_size cfg["learning_epochs"] = 8 cfg["mini_batches"] = 8 # 16 * 4096 / 8192 cfg["discount_factor"] = 0.99 cfg["lambda"] = 0.95 cfg["learning_rate"] = 5e-4 cfg["learning_rate_scheduler"] = KLAdaptiveRL cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008} cfg["random_timesteps"] = 0 cfg["learning_starts"] = 0 cfg["grad_norm_clip"] = 1.0 cfg["ratio_clip"] = 0.2 cfg["value_clip"] = 0.2 cfg["clip_predicted_values"] = True cfg["entropy_loss_scale"] = 0.0 cfg["value_loss_scale"] = 2.0 cfg["kl_threshold"] = 0 cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01 cfg["state_preprocessor"] = RunningStandardScaler cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device} cfg["value_preprocessor"] = RunningStandardScaler cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device} ```

Winmodel/Taxi-v3

Winmodel

2023-07-10T13:32:37Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-07-10T13:32:35Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Winmodel/Taxi-v3", 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"]) ```

JesseJr/distilbert-base-uncased-finetuned-cola

JesseJr

2023-07-10T13:32:08Z

61

0

transformers

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

text-classification

2023-07-10T13:27:38Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: JesseJr/distilbert-base-uncased-finetuned-cola results: [] ---  # JesseJr/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2071 - Validation Loss: 0.5352 - Train Matthews Correlation: 0.5089 - Epoch: 2 ## 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5299 | 0.4596 | 0.4739 | 0 | | 0.3386 | 0.4643 | 0.5152 | 1 | | 0.2071 | 0.5352 | 0.5089 | 2 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3

AladarMezga/detr-resnet-50_finetuned_cppe5

AladarMezga

2023-07-10T13:26:52Z

192

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:cppe-5", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-07-10T12:06:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cppe-5 model-index: - name: detr-resnet-50_finetuned_cppe5 results: [] ---  # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 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: 10 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3