davanstrien/model_cards_with_readmes · Datasets at Hugging Face

lfcc/bert-portuguese-squad

lfcc

bert

10

1

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,451

false

# bert-portuguese-squad This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.041 | 1.0 | 5578 | 1.1970 | | 0.8267 | 2.0 | 11156 | 1.2215 | | 0.586 | 3.0 | 16734 | 1.3191 | | 0.4251 | 4.0 | 22312 | 1.6129 | | 0.3045 | 5.0 | 27890 | 1.7907 | | 0.2432 | 6.0 | 33468 | 1.9715 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.2

63da614a4ceb33ef543be7e6c7cdb22d

Roy029/mpyt5_e5

Roy029

mt5

9

1

transformers

0

text2text-generation

true

false

openrail

null

0

[]

false

true

1,050

false

# Model Card for mpyt5_e5  事前に自然言語だけでなくPythonを学習したモデル # Training Details ## Training Data  Python Code (1.05GB) ## Training Procedure  - MLM - python vocab (https://huggingface.co/kkuramitsu/mt5-pytoken) ### Preprocessing mT5 + Python ### Speeds, Sizes, Times  - mT5-small(300M Paramators) - max_length = 128 # Model Version - *epoch5： This Model - *epoch10： https://huggingface.co/Roy029/mpyt5_e10 - *epoch15： https://huggingface.co/Roy029/mpyt5_e15 - *epoch20： https://huggingface.co/Roy029/mpyt5_e20

fa99d5c41632f29d721cb195415483f1

mikr/whisper-small-hu-cv11

mikr

whisper

17

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer']

true

1,537

false

# openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5649 - Wer: 30.6374 ## 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: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0182 | 7.01 | 1000 | 0.4546 | 31.4735 | | 0.0023 | 14.02 | 2000 | 0.5045 | 31.0910 | | 0.0008 | 22.01 | 3000 | 0.5318 | 30.2816 | | 0.0006 | 29.02 | 4000 | 0.5585 | 30.5989 | | 0.0004 | 37.01 | 5000 | 0.5649 | 30.6374 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

95ddc9a707c412d270a28c4d96c35ba7

pig4431/YELP_ALBERT_5E

pig4431

albert

10

8

transformers

0

text-classification

true

false

apache-2.0

null

['yelp_review_full']

null

0

['generated_from_trainer']

true

10,800

false

# YELP_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - Accuracy: 0.9733 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4967 | 0.03 | 50 | 0.1667 | 0.9467 | | 0.3268 | 0.06 | 100 | 0.2106 | 0.9133 | | 0.3413 | 0.1 | 150 | 0.2107 | 0.9667 | | 0.3172 | 0.13 | 200 | 0.1906 | 0.94 | | 0.2804 | 0.16 | 250 | 0.2588 | 0.9 | | 0.2604 | 0.19 | 300 | 0.2023 | 0.94 | | 0.2532 | 0.22 | 350 | 0.1263 | 0.9533 | | 0.2103 | 0.26 | 400 | 0.1233 | 0.96 | | 0.212 | 0.29 | 450 | 0.2019 | 0.9267 | | 0.2669 | 0.32 | 500 | 0.1110 | 0.9667 | | 0.2187 | 0.35 | 550 | 0.1542 | 0.96 | | 0.2203 | 0.38 | 600 | 0.0879 | 0.9733 | | 0.2699 | 0.42 | 650 | 0.0971 | 0.9667 | | 0.2107 | 0.45 | 700 | 0.0863 | 0.9667 | | 0.2443 | 0.48 | 750 | 0.0823 | 0.9733 | | 0.1987 | 0.51 | 800 | 0.1207 | 0.9733 | | 0.2326 | 0.54 | 850 | 0.1368 | 0.9667 | | 0.1787 | 0.58 | 900 | 0.1027 | 0.9667 | | 0.2159 | 0.61 | 950 | 0.2443 | 0.9333 | | 0.1316 | 0.64 | 1000 | 0.2035 | 0.9467 | | 0.2416 | 0.67 | 1050 | 0.0882 | 0.9733 | | 0.2008 | 0.7 | 1100 | 0.1709 | 0.9533 | | 0.2065 | 0.74 | 1150 | 0.1098 | 0.9667 | | 0.2391 | 0.77 | 1200 | 0.1055 | 0.9667 | | 0.1533 | 0.8 | 1250 | 0.1997 | 0.94 | | 0.2016 | 0.83 | 1300 | 0.0899 | 0.96 | | 0.2016 | 0.86 | 1350 | 0.0957 | 0.9733 | | 0.2316 | 0.9 | 1400 | 0.0784 | 0.98 | | 0.1839 | 0.93 | 1450 | 0.0784 | 0.9733 | | 0.2121 | 0.96 | 1500 | 0.1150 | 0.9733 | | 0.1307 | 0.99 | 1550 | 0.0969 | 0.9733 | | 0.1271 | 1.02 | 1600 | 0.2326 | 0.9467 | | 0.1736 | 1.06 | 1650 | 0.0979 | 0.9667 | | 0.1357 | 1.09 | 1700 | 0.0862 | 0.98 | | 0.1871 | 1.12 | 1750 | 0.1419 | 0.9667 | | 0.1411 | 1.15 | 1800 | 0.1301 | 0.96 | | 0.1317 | 1.18 | 1850 | 0.1602 | 0.9533 | | 0.1432 | 1.22 | 1900 | 0.1885 | 0.9533 | | 0.1793 | 1.25 | 1950 | 0.0776 | 0.9667 | | 0.1322 | 1.28 | 2000 | 0.0822 | 0.9733 | | 0.1416 | 1.31 | 2050 | 0.0920 | 0.9733 | | 0.1524 | 1.34 | 2100 | 0.0673 | 0.98 | | 0.1338 | 1.38 | 2150 | 0.0602 | 0.98 | | 0.152 | 1.41 | 2200 | 0.0916 | 0.98 | | 0.1192 | 1.44 | 2250 | 0.0559 | 0.98 | | 0.1471 | 1.47 | 2300 | 0.1096 | 0.9667 | | 0.1267 | 1.5 | 2350 | 0.0695 | 0.9733 | | 0.1776 | 1.54 | 2400 | 0.1363 | 0.96 | | 0.1495 | 1.57 | 2450 | 0.0818 | 0.98 | | 0.1158 | 1.6 | 2500 | 0.1282 | 0.9667 | | 0.1772 | 1.63 | 2550 | 0.0682 | 0.9733 | | 0.1187 | 1.66 | 2600 | 0.1032 | 0.9733 | | 0.136 | 1.7 | 2650 | 0.1071 | 0.9667 | | 0.1829 | 1.73 | 2700 | 0.0753 | 0.9667 | | 0.1147 | 1.76 | 2750 | 0.1071 | 0.9733 | | 0.1174 | 1.79 | 2800 | 0.1441 | 0.9667 | | 0.0707 | 1.82 | 2850 | 0.1362 | 0.9667 | | 0.1372 | 1.86 | 2900 | 0.1861 | 0.9533 | | 0.2108 | 1.89 | 2950 | 0.0770 | 0.9733 | | 0.2014 | 1.92 | 3000 | 0.1114 | 0.9667 | | 0.1373 | 1.95 | 3050 | 0.1244 | 0.9667 | | 0.1242 | 1.98 | 3100 | 0.1220 | 0.96 | | 0.1267 | 2.02 | 3150 | 0.1139 | 0.9733 | | 0.1021 | 2.05 | 3200 | 0.2013 | 0.9533 | | 0.1091 | 2.08 | 3250 | 0.1027 | 0.9733 | | 0.0648 | 2.11 | 3300 | 0.1464 | 0.9733 | | 0.1207 | 2.14 | 3350 | 0.1255 | 0.9733 | | 0.0833 | 2.18 | 3400 | 0.0708 | 0.98 | | 0.0796 | 2.21 | 3450 | 0.1608 | 0.96 | | 0.0624 | 2.24 | 3500 | 0.0827 | 0.98 | | 0.0518 | 2.27 | 3550 | 0.0602 | 0.98 | | 0.1242 | 2.3 | 3600 | 0.0752 | 0.9733 | | 0.0422 | 2.34 | 3650 | 0.1000 | 0.9733 | | 0.0748 | 2.37 | 3700 | 0.1171 | 0.9667 | | 0.0839 | 2.4 | 3750 | 0.1341 | 0.9667 | | 0.1033 | 2.43 | 3800 | 0.0744 | 0.98 | | 0.0567 | 2.46 | 3850 | 0.0869 | 0.98 | | 0.0756 | 2.5 | 3900 | 0.0745 | 0.98 | | 0.0768 | 2.53 | 3950 | 0.0895 | 0.9733 | | 0.0878 | 2.56 | 4000 | 0.0703 | 0.98 | | 0.1023 | 2.59 | 4050 | 0.0806 | 0.98 | | 0.0807 | 2.62 | 4100 | 0.0338 | 0.9867 | | 0.0868 | 2.66 | 4150 | 0.0892 | 0.9667 | | 0.0648 | 2.69 | 4200 | 0.1637 | 0.9533 | | 0.0535 | 2.72 | 4250 | 0.1622 | 0.9667 | | 0.0675 | 2.75 | 4300 | 0.1354 | 0.9733 | | 0.1121 | 2.78 | 4350 | 0.1440 | 0.9533 | | 0.0714 | 2.82 | 4400 | 0.1022 | 0.9467 | | 0.0786 | 2.85 | 4450 | 0.1110 | 0.9733 | | 0.0822 | 2.88 | 4500 | 0.1218 | 0.9733 | | 0.1075 | 2.91 | 4550 | 0.1041 | 0.9733 | | 0.0783 | 2.94 | 4600 | 0.0992 | 0.9733 | | 0.1059 | 2.98 | 4650 | 0.1187 | 0.9733 | | 0.067 | 3.01 | 4700 | 0.0931 | 0.9733 | | 0.0425 | 3.04 | 4750 | 0.1252 | 0.9733 | | 0.0539 | 3.07 | 4800 | 0.1152 | 0.9733 | | 0.0419 | 3.1 | 4850 | 0.1534 | 0.9667 | | 0.0462 | 3.13 | 4900 | 0.1398 | 0.9733 | | 0.0435 | 3.17 | 4950 | 0.1168 | 0.98 | | 0.0144 | 3.2 | 5000 | 0.1489 | 0.9667 | | 0.0367 | 3.23 | 5050 | 0.1293 | 0.9733 | | 0.0336 | 3.26 | 5100 | 0.1353 | 0.9733 | | 0.0246 | 3.29 | 5150 | 0.0958 | 0.98 | | 0.0181 | 3.33 | 5200 | 0.1294 | 0.9733 | | 0.0357 | 3.36 | 5250 | 0.1209 | 0.9733 | | 0.0683 | 3.39 | 5300 | 0.1748 | 0.96 | | 0.0353 | 3.42 | 5350 | 0.2159 | 0.9533 | | 0.0415 | 3.45 | 5400 | 0.1723 | 0.96 | | 0.0336 | 3.49 | 5450 | 0.1031 | 0.98 | | 0.0475 | 3.52 | 5500 | 0.0959 | 0.98 | | 0.0393 | 3.55 | 5550 | 0.2163 | 0.96 | | 0.0337 | 3.58 | 5600 | 0.1097 | 0.9733 | | 0.0415 | 3.61 | 5650 | 0.1365 | 0.98 | | 0.035 | 3.65 | 5700 | 0.1175 | 0.98 | | 0.0448 | 3.68 | 5750 | 0.1543 | 0.9667 | | 0.0445 | 3.71 | 5800 | 0.2005 | 0.96 | | 0.0211 | 3.74 | 5850 | 0.1179 | 0.98 | | 0.0198 | 3.77 | 5900 | 0.1298 | 0.9733 | | 0.026 | 3.81 | 5950 | 0.2167 | 0.9667 | | 0.0412 | 3.84 | 6000 | 0.1224 | 0.98 | | 0.0446 | 3.87 | 6050 | 0.0798 | 0.98 | | 0.0174 | 3.9 | 6100 | 0.0577 | 0.9933 | | 0.0535 | 3.93 | 6150 | 0.1482 | 0.9667 | | 0.0495 | 3.97 | 6200 | 0.0862 | 0.98 | | 0.0267 | 4.0 | 6250 | 0.1190 | 0.98 | | 0.0087 | 4.03 | 6300 | 0.0747 | 0.98 | | 0.0102 | 4.06 | 6350 | 0.0753 | 0.9867 | | 0.0178 | 4.09 | 6400 | 0.1812 | 0.9667 | | 0.0088 | 4.13 | 6450 | 0.0817 | 0.98 | | 0.0144 | 4.16 | 6500 | 0.0805 | 0.98 | | 0.014 | 4.19 | 6550 | 0.0862 | 0.9867 | | 0.0002 | 4.22 | 6600 | 0.0894 | 0.98 | | 0.0112 | 4.25 | 6650 | 0.1004 | 0.9733 | | 0.0054 | 4.29 | 6700 | 0.0832 | 0.9867 | | 0.0001 | 4.32 | 6750 | 0.0812 | 0.9867 | | 0.0202 | 4.35 | 6800 | 0.1828 | 0.9667 | | 0.009 | 4.38 | 6850 | 0.1114 | 0.98 | | 0.0001 | 4.41 | 6900 | 0.1295 | 0.98 | | 0.0077 | 4.45 | 6950 | 0.1610 | 0.9733 | | 0.0082 | 4.48 | 7000 | 0.1787 | 0.9667 | | 0.0198 | 4.51 | 7050 | 0.1485 | 0.9733 | | 0.0017 | 4.54 | 7100 | 0.1774 | 0.9733 | | 0.0115 | 4.57 | 7150 | 0.1567 | 0.9733 | | 0.0001 | 4.61 | 7200 | 0.1534 | 0.9733 | | 0.0247 | 4.64 | 7250 | 0.2020 | 0.9667 | | 0.0059 | 4.67 | 7300 | 0.1918 | 0.9667 | | 0.0052 | 4.7 | 7350 | 0.1315 | 0.98 | | 0.0076 | 4.73 | 7400 | 0.1289 | 0.98 | | 0.0218 | 4.77 | 7450 | 0.1610 | 0.9733 | | 0.0077 | 4.8 | 7500 | 0.1355 | 0.98 | | 0.0096 | 4.83 | 7550 | 0.1378 | 0.9733 | | 0.008 | 4.86 | 7600 | 0.1568 | 0.9733 | | 0.0103 | 4.89 | 7650 | 0.1388 | 0.9733 | | 0.0009 | 4.93 | 7700 | 0.1221 | 0.98 | | 0.0287 | 4.96 | 7750 | 0.1448 | 0.9733 | | 0.01 | 4.99 | 7800 | 0.1394 | 0.9733 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

b243c9bf28a38905d47a885fe0b96852

mlegls/usv3_usdc_predictor_0

mlegls

gpt2

9

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,030

false

# usv3_usdc_predictor_0 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0 - Datasets 2.4.0 - Tokenizers 0.12.1

72b993018f7fb35f91816e3af5691874

c-x-he/my_awesome_wnut_model

c-x-he

distilbert

15

0

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,835

false

# c-x-he/my_awesome_wnut_model 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: 0.1304 - Validation Loss: 0.2744 - Train Precision: 0.5429 - Train Recall: 0.4007 - Train F1: 0.4611 - Train Accuracy: 0.9441 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3493 | 0.3035 | 0.4447 | 0.2309 | 0.3039 | 0.9347 | 0 | | 0.1647 | 0.2772 | 0.5284 | 0.3565 | 0.4257 | 0.9415 | 1 | | 0.1304 | 0.2744 | 0.5429 | 0.4007 | 0.4611 | 0.9441 | 2 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.10.0 - Datasets 2.9.0 - Tokenizers 0.13.2

9ff17fbdd1d3e8b9b70dea1f57983794

dannytkn/bert-finetuned-squad

dannytkn

bert

10

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

948

false

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.8.2 - Datasets 1.18.3 - Tokenizers 0.10.3

11d1a74a6b86d94bdae62591288fce53

d4niel92/distilbert-base-uncased-finetuned-emotion

d4niel92

distilbert

12

0

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,343

false

# 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: 0.2259 - Accuracy: 0.924 - F1: 0.9238 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8417 | 1.0 | 250 | 0.3291 | 0.9005 | 0.8962 | | 0.2551 | 2.0 | 500 | 0.2259 | 0.924 | 0.9238 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

406ba899c219444e946545424bc77a29

liaad/srl-en_xlmr-base

liaad

xlm-roberta

7

3

transformers

1

feature-extraction

true

false

apache-2.0

['multilingual', 'pt', 'en']

['PropBank.Br', 'CoNLL-2012']

null

0

['xlm-roberta-base', 'semantic role labeling', 'finetuned']

false

true

4,083

false

# XLM-R base fine-tuned on English semantic role labeling ## Model description This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_xlmr-base") model = AutoModel.from_pretrained("liaad/srl-en_xlmr-base") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). #### Limitations and bias - This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow. - The models were trained only for 5 epochs. - The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data. ## Training procedure The models were trained on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data. They were tested on the PropBank.Br data set as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results | Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 | | `srl-pt_mbert-base` | 72.76 | 66.89 | | `srl-en_xlmr-base` | 66.59 | 65.24 | | `srl-en_xlmr-large` | 67.60 | 64.94 | | `srl-en_mbert-base` | 63.07 | 58.56 | | `srl-enpt_xlmr-base` | 76.50 | 73.74 | | `srl-enpt_xlmr-large` | **78.22** | 74.55 | | `srl-enpt_mbert-base` | 74.88 | 69.19 | | `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | | `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | | `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

09cd56349a6470246a7af6296de84312

thatdramebaazguy/movie-roberta-base

thatdramebaazguy

roberta

10

7

transformers

1

fill-mask

true

cc-by-4.0

['English']

['imdb', 'cornell_movie_dialogue', 'polarity_movie_data', '25mlens_movie_data']

null

1

0

['roberta', 'roberta-base', 'masked-language-modeling', 'masked-lm']

false

true

1,319

false

# roberta-base for MLM Objective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling. This is the Movie Roberta to be used in Movie Domain applications. ``` model_name = "thatdramebaazguy/movie-roberta-base" pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="Fill-Mask") ``` ## Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Fill-Mask **Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Eval data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Infrastructure**: 4x Tesla v100 **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/train_movie_roberta.sh) ## Hyperparameters ``` Num examples = 4767233 Num Epochs = 2 Instantaneous batch size per device = 20 Total train batch size (w. parallel, distributed & accumulation) = 80 Gradient Accumulation steps = 1 Total optimization steps = 119182 eval_loss = 1.6153 eval_samples = 20573 perplexity = 5.0296 learning_rate=5e-05 n_gpu = 4 ``` ## Performance perplexity = 5.0296 Some of my work: - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) ---

a6a9b3834886565cd570e09f067b23ed

sayakpaul/distilbert-base-uncased-finetuned-emotion-lr-3e-05-wd-001

sayakpaul

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,394

false

# distilbert-base-uncased-finetuned-emotion-lr-3e-05-wd-001 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: 0.2415 - Accuracy: 0.919 - F1: 0.9191 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.9356 | 1.0 | 125 | 0.3832 | 0.8895 | 0.8855 | | 0.2866 | 2.0 | 250 | 0.2415 | 0.919 | 0.9191 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.10.0 - Datasets 2.6.1 - Tokenizers 0.13.1

d935a2bf1a0b863de14705fde085a237

ryusangwon/distilbert-base-uncased-finetuned-emotion

ryusangwon

distilbert

12

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,341

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an emtion dataset. It achieves the following results on the evaluation set: - Loss: 0.2254 - Accuracy: 0.925 - F1: 0.9249 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3271 | 0.903 | 0.8983 | | No log | 2.0 | 500 | 0.2254 | 0.925 | 0.9249 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

cdc9cb535b0f3068352302b7de96e867

Rolv-Arild/xls-r-300m-npsc-4

Rolv-Arild

wav2vec2

27

11

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer']

true

5,564

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Wer: 0.1697 ## 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: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.4527 | 0.28 | 250 | 4.0144 | 1.0 | | 3.1828 | 0.56 | 500 | 3.1369 | 1.0 | | 2.9927 | 0.85 | 750 | 3.0183 | 1.0 | | 2.9591 | 1.13 | 1000 | 2.9991 | 1.0 | | 2.8989 | 1.41 | 1250 | 2.9000 | 1.0000 | | 2.4286 | 1.69 | 1500 | 1.7688 | 0.9550 | | 1.6765 | 1.98 | 1750 | 0.6842 | 0.4855 | | 1.4521 | 2.26 | 2000 | 0.5096 | 0.3736 | | 1.3589 | 2.54 | 2250 | 0.4479 | 0.3335 | | 1.3136 | 2.82 | 2500 | 0.4056 | 0.3123 | | 1.2856 | 3.11 | 2750 | 0.3870 | 0.2987 | | 1.2283 | 3.39 | 3000 | 0.3646 | 0.2828 | | 1.2053 | 3.67 | 3250 | 0.3499 | 0.2748 | | 1.2087 | 3.95 | 3500 | 0.3345 | 0.2603 | | 1.2002 | 4.24 | 3750 | 0.3320 | 0.2523 | | 1.1383 | 4.52 | 4000 | 0.3117 | 0.2439 | | 1.1364 | 4.8 | 4250 | 0.3198 | 0.2383 | | 1.158 | 5.08 | 4500 | 0.3071 | 0.2342 | | 1.108 | 5.37 | 4750 | 0.3011 | 0.2314 | | 1.1025 | 5.65 | 5000 | 0.2875 | 0.2289 | | 1.0697 | 5.93 | 5250 | 0.2926 | 0.2256 | | 1.0904 | 6.21 | 5500 | 0.2695 | 0.2245 | | 1.0802 | 6.5 | 5750 | 0.2602 | 0.2189 | | 1.0882 | 6.78 | 6000 | 0.2603 | 0.2168 | | 1.0881 | 7.06 | 6250 | 0.2540 | 0.2293 | | 1.0378 | 7.34 | 6500 | 0.2614 | 0.2193 | | 1.0397 | 7.63 | 6750 | 0.2707 | 0.2104 | | 1.0296 | 7.91 | 7000 | 0.2483 | 0.2119 | | 1.0249 | 8.19 | 7250 | 0.2483 | 0.2047 | | 1.013 | 8.47 | 7500 | 0.2487 | 0.2042 | | 1.0064 | 8.76 | 7750 | 0.2456 | 0.2016 | | 1.0668 | 9.04 | 8000 | 0.2397 | 0.1995 | | 1.0129 | 9.32 | 8250 | 0.2374 | 0.1994 | | 1.0164 | 9.6 | 8500 | 0.2206 | 0.1992 | | 0.975 | 9.89 | 8750 | 0.2247 | 0.1973 | | 0.9849 | 10.17 | 9000 | 0.2325 | 0.1953 | | 0.9826 | 10.45 | 9250 | 0.2301 | 0.1934 | | 0.9835 | 10.73 | 9500 | 0.2192 | 0.1942 | | 0.9676 | 11.02 | 9750 | 0.2266 | 0.1913 | | 0.9627 | 11.3 | 10000 | 0.2193 | 0.1921 | | 0.976 | 11.58 | 10250 | 0.2309 | 0.1882 | | 0.969 | 11.86 | 10500 | 0.2268 | 0.1886 | | 0.9611 | 12.15 | 10750 | 0.2322 | 0.1863 | | 0.9397 | 12.43 | 11000 | 0.2197 | 0.1844 | | 0.9601 | 12.71 | 11250 | 0.2211 | 0.1871 | | 0.9718 | 12.99 | 11500 | 0.2079 | 0.1898 | | 0.9347 | 13.28 | 11750 | 0.2054 | 0.1843 | | 0.9377 | 13.56 | 12000 | 0.2031 | 0.1842 | | 0.934 | 13.84 | 12250 | 0.2059 | 0.1806 | | 0.9295 | 14.12 | 12500 | 0.2122 | 0.1861 | | 0.935 | 14.41 | 12750 | 0.2072 | 0.1787 | | 0.9021 | 14.69 | 13000 | 0.2105 | 0.1781 | | 0.9193 | 14.97 | 13250 | 0.2035 | 0.1786 | | 0.9214 | 15.25 | 13500 | 0.2035 | 0.1766 | | 0.9048 | 15.54 | 13750 | 0.1964 | 0.1758 | | 0.9006 | 15.82 | 14000 | 0.1984 | 0.1757 | | 0.9027 | 16.1 | 14250 | 0.2022 | 0.1743 | | 0.9083 | 16.38 | 14500 | 0.1969 | 0.1744 | | 0.9761 | 16.67 | 14750 | 0.1963 | 0.1728 | | 0.9311 | 16.95 | 15000 | 0.1960 | 0.1737 | | 0.886 | 17.23 | 15250 | 0.1929 | 0.1726 | | 0.8969 | 17.51 | 15500 | 0.1928 | 0.1734 | | 0.9084 | 17.8 | 15750 | 0.1937 | 0.1713 | | 0.8795 | 18.08 | 16000 | 0.1978 | 0.1709 | | 0.8883 | 18.36 | 16250 | 0.1956 | 0.1703 | | 0.8901 | 18.64 | 16500 | 0.1933 | 0.1705 | | 0.8922 | 18.93 | 16750 | 0.1962 | 0.1711 | | 0.8765 | 19.21 | 17000 | 0.1962 | 0.1711 | | 0.8992 | 19.49 | 17250 | 0.1965 | 0.1703 | | 0.8778 | 19.77 | 17500 | 0.1957 | 0.1699 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.0+cu113 - Datasets 1.18.1 - Tokenizers 0.11.0

1279e61ae88f9811203aadaa615e3148

Maheedhar/TF-Fine_tuned_T5-base

Maheedhar

t5

4

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,317

false

# TF-Fine_tuned_T5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2063 - Validation Loss: 0.1893 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6995 | 0.2622 | 0 | | 0.2845 | 0.2256 | 1 | | 0.2471 | 0.2079 | 2 | | 0.2216 | 0.1974 | 3 | | 0.2063 | 0.1893 | 4 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

58151a3cfeb502911ca1399a2fd0b668

sd-dreambooth-library/langel

sd-dreambooth-library

null

24

2

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,096

false

### Langel on Stable Diffusion via Dreambooth #### model by Kasuzu This your the Stable Diffusion model fine-tuned the Langel concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **Langel** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/1.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/5.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/0.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/4.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/langel/resolve/main/concept_images/2.jpeg)

ad04136f9dbc7319b6c5a0749dc70c28

elopezlopez/Bio_ClinicalBERT_fold_10_ternary_v1

elopezlopez

bert

13

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,669

false

# Bio_ClinicalBERT_fold_10_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0706 - F1: 0.7748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.6097 | 0.7290 | | 0.555 | 2.0 | 580 | 0.6106 | 0.7649 | | 0.555 | 3.0 | 870 | 0.6608 | 0.7847 | | 0.2449 | 4.0 | 1160 | 0.8894 | 0.7809 | | 0.2449 | 5.0 | 1450 | 1.1049 | 0.7760 | | 0.1055 | 6.0 | 1740 | 1.2951 | 0.7884 | | 0.0338 | 7.0 | 2030 | 1.4809 | 0.7760 | | 0.0338 | 8.0 | 2320 | 1.4751 | 0.7698 | | 0.0225 | 9.0 | 2610 | 1.6648 | 0.7809 | | 0.0225 | 10.0 | 2900 | 1.7174 | 0.7772 | | 0.006 | 11.0 | 3190 | 1.7872 | 0.7735 | | 0.006 | 12.0 | 3480 | 1.7803 | 0.7748 | | 0.0161 | 13.0 | 3770 | 1.9302 | 0.7735 | | 0.0005 | 14.0 | 4060 | 1.9853 | 0.7748 | | 0.0005 | 15.0 | 4350 | 2.0043 | 0.7735 | | 0.0062 | 16.0 | 4640 | 1.9969 | 0.7760 | | 0.0062 | 17.0 | 4930 | 2.0173 | 0.7760 | | 0.0068 | 18.0 | 5220 | 1.9891 | 0.7785 | | 0.0034 | 19.0 | 5510 | 1.9951 | 0.7797 | | 0.0034 | 20.0 | 5800 | 2.0283 | 0.7748 | | 0.0049 | 21.0 | 6090 | 1.9985 | 0.7834 | | 0.0049 | 22.0 | 6380 | 2.0131 | 0.7760 | | 0.0011 | 23.0 | 6670 | 2.0526 | 0.7748 | | 0.0011 | 24.0 | 6960 | 2.0662 | 0.7748 | | 0.001 | 25.0 | 7250 | 2.0706 | 0.7748 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

83c3644f6ec958ead1df010f7c12825f

ali2066/finetuned_token_2e-05_16_02_2022-01_55_54

ali2066

distilbert

40

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,787

false

# finetuned_token_2e-05_16_02_2022-01_55_54 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1722 - Precision: 0.3378 - Recall: 0.3615 - F1: 0.3492 - Accuracy: 0.9448 ## 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: 32 - eval_batch_size: 32 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3781 | 0.1512 | 0.2671 | 0.1931 | 0.8216 | | No log | 2.0 | 76 | 0.3020 | 0.1748 | 0.2938 | 0.2192 | 0.8551 | | No log | 3.0 | 114 | 0.2723 | 0.1938 | 0.3339 | 0.2452 | 0.8663 | | No log | 4.0 | 152 | 0.2574 | 0.2119 | 0.3506 | 0.2642 | 0.8727 | | No log | 5.0 | 190 | 0.2521 | 0.2121 | 0.3623 | 0.2676 | 0.8756 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

b1a371d73545e6f02af6342457b5f9c5

meghazisofiane/opus-mt-en-ar-finetuned-en-to-ar

meghazisofiane

marian

15

1

transformers

0

text2text-generation

true

false

apache-2.0

null

['un_multi']

null

0

['generated_from_trainer']

true

2,378

false

# opus-mt-en-ar-finetuned-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8133 - Bleu: 64.6767 - Gen Len: 17.595 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 50 | 0.7710 | 64.3416 | 17.4 | | No log | 2.0 | 100 | 0.7569 | 63.9546 | 17.465 | | No log | 3.0 | 150 | 0.7570 | 64.7484 | 17.385 | | No log | 4.0 | 200 | 0.7579 | 65.4073 | 17.305 | | No log | 5.0 | 250 | 0.7624 | 64.8939 | 17.325 | | No log | 6.0 | 300 | 0.7696 | 65.1257 | 17.45 | | No log | 7.0 | 350 | 0.7747 | 65.527 | 17.395 | | No log | 8.0 | 400 | 0.7791 | 65.1357 | 17.52 | | No log | 9.0 | 450 | 0.7900 | 65.3812 | 17.415 | | 0.3982 | 10.0 | 500 | 0.7925 | 65.7346 | 17.39 | | 0.3982 | 11.0 | 550 | 0.7951 | 65.1267 | 17.62 | | 0.3982 | 12.0 | 600 | 0.8040 | 64.6874 | 17.495 | | 0.3982 | 13.0 | 650 | 0.8069 | 64.7788 | 17.52 | | 0.3982 | 14.0 | 700 | 0.8105 | 64.6701 | 17.585 | | 0.3982 | 15.0 | 750 | 0.8120 | 64.7111 | 17.58 | | 0.3982 | 16.0 | 800 | 0.8133 | 64.6767 | 17.595 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

de90439c69747b76a5a26c2b1eb47f82

modhp/wav2vec2-model1-torgo

modhp

wav2vec2

80

9

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

975

false

# wav2vec2-model1-torgo This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 1.18.3 - Tokenizers 0.11.6

b9f09de676ef9b06cc22fb1fb54ccabc

kcarnold/inquisitive2

kcarnold

bart

15

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

984

false

# inquisitive2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1760 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7.0 ### Training results ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0 - Datasets 2.3.0 - Tokenizers 0.12.1

612efebbfc49ae9c7b7f0aaa3d542e50

huyue012/wav2vec2-base-cynthia-timit

huyue012

wav2vec2

14

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,988

false

# wav2vec2-base-cynthia-timit 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: 0.4888 - Wer: 0.3315 ## 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: 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: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.7674 | 1.0 | 500 | 2.8994 | 1.0 | | 1.3538 | 2.01 | 1000 | 0.5623 | 0.5630 | | 0.5416 | 3.01 | 1500 | 0.4595 | 0.4765 | | 0.3563 | 4.02 | 2000 | 0.4435 | 0.4328 | | 0.2869 | 5.02 | 2500 | 0.4035 | 0.4145 | | 0.2536 | 6.02 | 3000 | 0.4090 | 0.3945 | | 0.2072 | 7.03 | 3500 | 0.4188 | 0.3809 | | 0.1825 | 8.03 | 4000 | 0.4139 | 0.3865 | | 0.1754 | 9.04 | 4500 | 0.4320 | 0.3763 | | 0.1477 | 10.04 | 5000 | 0.4668 | 0.3699 | | 0.1418 | 11.04 | 5500 | 0.4439 | 0.3683 | | 0.1207 | 12.05 | 6000 | 0.4419 | 0.3678 | | 0.115 | 13.05 | 6500 | 0.4606 | 0.3786 | | 0.1022 | 14.06 | 7000 | 0.4403 | 0.3610 | | 0.1019 | 15.06 | 7500 | 0.4966 | 0.3609 | | 0.0898 | 16.06 | 8000 | 0.4675 | 0.3586 | | 0.0824 | 17.07 | 8500 | 0.4844 | 0.3583 | | 0.0737 | 18.07 | 9000 | 0.4801 | 0.3534 | | 0.076 | 19.08 | 9500 | 0.4945 | 0.3529 | | 0.0627 | 20.08 | 10000 | 0.4700 | 0.3417 | | 0.0723 | 21.08 | 10500 | 0.4630 | 0.3449 | | 0.0597 | 22.09 | 11000 | 0.5164 | 0.3456 | | 0.0566 | 23.09 | 11500 | 0.4957 | 0.3401 | | 0.0453 | 24.1 | 12000 | 0.5032 | 0.3419 | | 0.0492 | 25.1 | 12500 | 0.5391 | 0.3387 | | 0.0524 | 26.1 | 13000 | 0.5057 | 0.3348 | | 0.0381 | 27.11 | 13500 | 0.5098 | 0.3331 | | 0.0402 | 28.11 | 14000 | 0.5087 | 0.3353 | | 0.0358 | 29.12 | 14500 | 0.4888 | 0.3315 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

06eaa1e0926254149775a2b31d0f9a17

Evelyn18/distilbert-base-uncased-becasv2-4

Evelyn18

distilbert

13

7

transformers

0

question-answering

true

false

apache-2.0

null

['becasv2']

null

0

['generated_from_trainer']

true

1,530

false

# distilbert-base-uncased-becasv2-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.4637 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 5.3677 | | No log | 2.0 | 12 | 4.6741 | | No log | 3.0 | 18 | 4.2978 | | No log | 4.0 | 24 | 3.9963 | | No log | 5.0 | 30 | 3.7544 | | No log | 6.0 | 36 | 3.5810 | | No log | 7.0 | 42 | 3.4932 | | No log | 8.0 | 48 | 3.4637 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

12a2cbab990d315eda7981702d48fea1

merve/20newsgroups

merve

null

4

0

sklearn

0

text-classification

false

mit

null

0

['sklearn', 'skops', 'text-classification']

false

true

11,561

false

# Model description This is a multinomial naive Bayes model trained on 20 new groups dataset. Count vectorizer and TFIDF vectorizer are used on top of the model. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |---------------------|----------------------------------------------------------------------------------------| | memory | | | steps | [('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())] | | verbose | False | | vect | CountVectorizer() | | tfidf | TfidfTransformer() | | clf | MultinomialNB() | | vect__analyzer | word | | vect__binary | False | | vect__decode_error | strict | | vect__dtype | <class 'numpy.int64'> | | vect__encoding | utf-8 | | vect__input | content | | vect__lowercase | True | | vect__max_df | 1.0 | | vect__max_features | | | vect__min_df | 1 | | vect__ngram_range | (1, 1) | | vect__preprocessor | | | vect__stop_words | | | vect__strip_accents | | | vect__token_pattern | (?u)\b\w\w+\b | | vect__tokenizer | | | vect__vocabulary | | | tfidf__norm | l2 | | tfidf__smooth_idf | True | | tfidf__sublinear_tf | False | | tfidf__use_idf | True | | clf__alpha | 1.0 | | clf__class_prior | | | clf__fit_prior | True | </details> ### Model Plot The model plot is below. <style>#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 {color: black;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 pre{padding: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable {background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-item {z-index: 1;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-parallel-item:only-child::after {width: 0;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" type="checkbox" ><label for="9caae382-ba9c-4e50-b4e0-017fa1bca4b4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf', MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" type="checkbox" ><label for="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" class="sk-toggleable__label sk-toggleable__label-arrow">CountVectorizer</label><div class="sk-toggleable__content"><pre>CountVectorizer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69b80eb1-41d4-421a-9875-a9e95faa6d45" type="checkbox" ><label for="69b80eb1-41d4-421a-9875-a9e95faa6d45" class="sk-toggleable__label sk-toggleable__label-arrow">TfidfTransformer</label><div class="sk-toggleable__content"><pre>TfidfTransformer()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" type="checkbox" ><label for="63c8c7e2-7443-4092-a86b-32b1cbef1a1b" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|---------| # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import pickle with open(pkl_filename, 'rb') as file: clf = pickle.load(file) ``` </details> # Model Card Authors This model card is written by following authors: merve # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` bibtex @inproceedings{...,year={2020}} ```

ba60652a5320f7f68e4c6987f6de03e5

victorbahlangene/deberta-v3-small-fine-Disaster-Tweets-Part2

victorbahlangene

deberta-v2

11

5

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,563

false

# deberta-v3-small-fine-Disaster-Tweets-Part2 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4849 - Accuracy: 0.8275 - F1: 0.8278 ## 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: 8e-05 - train_batch_size: 32 - 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_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 203 | 0.4670 | 0.8511 | 0.8503 | | No log | 2.0 | 406 | 0.4381 | 0.8459 | 0.8455 | | 0.4016 | 3.0 | 609 | 0.4096 | 0.8424 | 0.8413 | | 0.4016 | 4.0 | 812 | 0.4849 | 0.8275 | 0.8278 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

2d3b91c95cd2fc5825084459cee2cd48

eglesaks/xlm-roberta-base-finetuned-est

eglesaks

xlm-roberta

11

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,257

false

# xlm-roberta-base-finetuned-est This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6781 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 4.2576 | | No log | 2.0 | 104 | 3.8075 | | No log | 3.0 | 156 | 3.6781 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1

2bf8eb37e1c376bb70d00dadefa81d93

evamaxfield/soft-search

evamaxfield

distilbert

10

7

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,756

false

# soft-search This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5558 - F1: 0.5960 - Accuracy: 0.7109 - Precision: 0.5769 - Recall: 0.6164 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| | 0.5939 | 1.0 | 71 | 0.5989 | 0.0533 | 0.6635 | 1.0 | 0.0274 | | 0.5903 | 2.0 | 142 | 0.5558 | 0.5960 | 0.7109 | 0.5769 | 0.6164 | | 0.4613 | 3.0 | 213 | 0.6670 | 0.5641 | 0.6777 | 0.5301 | 0.6027 | | 0.4454 | 4.0 | 284 | 0.7647 | 0.5541 | 0.6872 | 0.5467 | 0.5616 | | 0.2931 | 5.0 | 355 | 0.8726 | 0.5139 | 0.6682 | 0.5211 | 0.5068 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

9464c06b0f75eef2e479ea1afbf8d676

google/mobilenet_v2_0.35_96

google

mobilenet_v2

5

834

transformers

0

image-classification

true

false

other

null

['imagenet-1k']

null

0

['vision', 'image-classification']

false

true

2,898

false

# MobileNet V2 MobileNet V2 model pre-trained on ImageNet-1k at resolution 96x96. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet). Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_0.35\_96**, where **0.35** is the depth multiplier and **96** is the resolution of the input images the model was trained on. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_0.35_96") model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v2_0.35_96") inputs = preprocessor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch. ### BibTeX entry and citation info ```bibtex @inproceedings{mobilenetv22018, title={MobileNetV2: Inverted Residuals and Linear Bottlenecks}, author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen}, booktitle={CVPR}, year={2018} } ```

9817eb3c500bc367ed6aad5ea23d8c8e

JovialValley/model_broadclass_onSet2.1

JovialValley

wav2vec2

12

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

13,091

false

# model_broadclass_onSet2.1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1459 - 0 Precision: 0.9630 - 0 Recall: 1.0 - 0 F1-score: 0.9811 - 0 Support: 26 - 1 Precision: 1.0 - 1 Recall: 0.9231 - 1 F1-score: 0.9600 - 1 Support: 39 - 2 Precision: 1.0 - 2 Recall: 1.0 - 2 F1-score: 1.0 - 2 Support: 19 - 3 Precision: 0.8667 - 3 Recall: 1.0 - 3 F1-score: 0.9286 - 3 Support: 13 - Accuracy: 0.9691 - Macro avg Precision: 0.9574 - Macro avg Recall: 0.9808 - Macro avg F1-score: 0.9674 - Macro avg Support: 97 - Weighted avg Precision: 0.9722 - Weighted avg Recall: 0.9691 - Weighted avg F1-score: 0.9693 - Weighted avg Support: 97 - Wer: 0.1293 - Mtrix: [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 80 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:---------------------------------------------------------------------------------------:| | 2.3399 | 4.16 | 100 | 2.1769 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 2.3152 | 8.33 | 200 | 2.1458 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.9859 | 12.49 | 300 | 1.9172 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.7126 | 16.65 | 400 | 1.6954 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.6833 | 20.82 | 500 | 1.7553 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.5318 | 24.98 | 600 | 1.5921 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.5868 | 29.16 | 700 | 1.5517 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.5577 | 33.33 | 800 | 1.5089 | 0.2680 | 1.0 | 0.4228 | 26 | 0.0 | 0.0 | 0.0 | 39 | 0.0 | 0.0 | 0.0 | 19 | 0.0 | 0.0 | 0.0 | 13 | 0.2680 | 0.0670 | 0.25 | 0.1057 | 97 | 0.0718 | 0.2680 | 0.1133 | 97 | 0.9869 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 39, 0, 0, 0], [2, 19, 0, 0, 0], [3, 13, 0, 0, 0]] | | 1.2201 | 37.49 | 900 | 1.1567 | 0.4643 | 1.0 | 0.6341 | 26 | 1.0 | 0.4872 | 0.6552 | 39 | 1.0 | 0.5263 | 0.6897 | 19 | 1.0 | 0.9231 | 0.9600 | 13 | 0.6907 | 0.8661 | 0.7341 | 0.7347 | 97 | 0.8564 | 0.6907 | 0.6971 | 97 | 0.9485 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 20, 19, 0, 0], [2, 9, 0, 10, 0], [3, 1, 0, 0, 12]] | | 0.9692 | 41.65 | 1000 | 1.0489 | 0.5102 | 0.9615 | 0.6667 | 26 | 0.9615 | 0.6410 | 0.7692 | 39 | 0.9167 | 0.5789 | 0.7097 | 19 | 1.0 | 0.7692 | 0.8696 | 13 | 0.7320 | 0.8471 | 0.7377 | 0.7538 | 97 | 0.8369 | 0.7320 | 0.7435 | 97 | 0.9374 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 13, 25, 1, 0], [2, 8, 0, 11, 0], [3, 3, 0, 0, 10]] | | 0.9214 | 45.82 | 1100 | 0.9620 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 0.9048 | 1.0 | 0.9500 | 19 | 1.0 | 1.0 | 1.0 | 13 | 0.9588 | 0.9598 | 0.9712 | 0.9647 | 97 | 0.9602 | 0.9588 | 0.9587 | 97 | 0.9328 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 2, 0], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.9305 | 49.98 | 1200 | 0.9736 | 0.8125 | 1.0 | 0.8966 | 26 | 1.0 | 0.8205 | 0.9014 | 39 | 0.9048 | 1.0 | 0.9500 | 19 | 1.0 | 0.9231 | 0.9600 | 13 | 0.9175 | 0.9293 | 0.9359 | 0.9270 | 97 | 0.9311 | 0.9175 | 0.9175 | 97 | 0.9253 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 5, 32, 2, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 12]] | | 0.8982 | 54.16 | 1300 | 0.9586 | 0.7812 | 0.9615 | 0.8621 | 26 | 0.9688 | 0.7949 | 0.8732 | 39 | 0.9 | 0.9474 | 0.9231 | 19 | 1.0 | 1.0 | 1.0 | 13 | 0.8969 | 0.9125 | 0.9259 | 0.9146 | 97 | 0.9092 | 0.8969 | 0.8970 | 97 | 0.9283 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 6, 31, 2, 0], [2, 1, 0, 18, 0], [3, 0, 0, 0, 13]] | | 0.8382 | 58.33 | 1400 | 0.8864 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9722 | 0.8974 | 0.9333 | 39 | 0.95 | 1.0 | 0.9744 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9485 | 0.9376 | 0.9647 | 0.9495 | 97 | 0.9509 | 0.9485 | 0.9483 | 97 | 0.8904 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 35, 1, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.7314 | 62.49 | 1500 | 0.7880 | 0.96 | 0.9231 | 0.9412 | 26 | 0.9474 | 0.9231 | 0.9351 | 39 | 0.95 | 1.0 | 0.9744 | 19 | 0.9286 | 1.0 | 0.9630 | 13 | 0.9485 | 0.9465 | 0.9615 | 0.9534 | 97 | 0.9488 | 0.9485 | 0.9481 | 97 | 0.8020 | [[0, 1, 2, 3], [0, 24, 2, 0, 0], [1, 1, 36, 1, 1], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.448 | 66.65 | 1600 | 0.3458 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.2561 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.1921 | 70.82 | 1700 | 0.1970 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.1581 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.1499 | 74.98 | 1800 | 0.1463 | 0.9615 | 0.9615 | 0.9615 | 26 | 0.9730 | 0.9231 | 0.9474 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9588 | 0.9503 | 0.9712 | 0.9594 | 97 | 0.9610 | 0.9588 | 0.9590 | 97 | 0.1384 | [[0, 1, 2, 3], [0, 25, 1, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | | 0.1099 | 79.16 | 1900 | 0.1459 | 0.9630 | 1.0 | 0.9811 | 26 | 1.0 | 0.9231 | 0.9600 | 39 | 1.0 | 1.0 | 1.0 | 19 | 0.8667 | 1.0 | 0.9286 | 13 | 0.9691 | 0.9574 | 0.9808 | 0.9674 | 97 | 0.9722 | 0.9691 | 0.9693 | 97 | 0.1293 | [[0, 1, 2, 3], [0, 26, 0, 0, 0], [1, 1, 36, 0, 2], [2, 0, 0, 19, 0], [3, 0, 0, 0, 13]] | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

677cc09ee693bc1826a103d9a2e618e6

zates/distilbert-base-uncased-finetuned-squad-seed-420-finetuned-squad-seed-420

zates

distilbert

9

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad_v2']

null

0

['generated_from_trainer']

true

1,027

false

# distilbert-base-uncased-finetuned-squad-seed-420-finetuned-squad-seed-420 This model is a fine-tuned version of [zates/distilbert-base-uncased-finetuned-squad-seed-420](https://huggingface.co/zates/distilbert-base-uncased-finetuned-squad-seed-420) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

b8bf39f193992fb1611cf58614adab2f

theojolliffe/distilbart-cnn-arxiv-pubmed-v3-e8

theojolliffe

bart

13

2

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,225

false

# distilbart-cnn-arxiv-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8329 - Rouge1: 53.3047 - Rouge2: 34.6219 - Rougel: 37.6148 - Rougelsum: 50.8973 - Gen Len: 141.8704 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1211 | 50.4753 | 30.5417 | 33.192 | 48.1321 | 141.8704 | | 1.3657 | 2.0 | 796 | 0.9944 | 52.2197 | 33.6109 | 35.9448 | 50.0028 | 141.6111 | | 0.887 | 3.0 | 1194 | 0.9149 | 52.796 | 33.7683 | 36.4941 | 50.4514 | 141.5926 | | 0.6548 | 4.0 | 1592 | 0.8725 | 52.5353 | 33.4019 | 36.4573 | 50.2506 | 142.0 | | 0.6548 | 5.0 | 1990 | 0.8540 | 53.2987 | 34.6476 | 38.314 | 51.163 | 141.4815 | | 0.504 | 6.0 | 2388 | 0.8395 | 52.7218 | 34.6524 | 37.9921 | 50.5185 | 141.5556 | | 0.4006 | 7.0 | 2786 | 0.8342 | 53.2251 | 35.2702 | 38.3763 | 51.1958 | 141.6667 | | 0.3314 | 8.0 | 3184 | 0.8329 | 53.3047 | 34.6219 | 37.6148 | 50.8973 | 141.8704 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

5a9bc8eeb743abfea938e1cf2f918bbe

kyryl0s/gpt2-uk-zno-edition

kyryl0s

gpt2

6

4

transformers

1

text-generation

true

false

afl-3.0

['uk']

null

0

[]

false

true

950

false

## GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays Generated texts are not very cohesive yet but I'm working on it. <br /> The Hosted inference API outputs (on the right) are too short for some reason. Trying to fix it. <br /> Use the code from the example below. The model takes "ZNOTITLE: your essay title" inputs. ### Example of usage: ```python from transformers import AlbertTokenizer, GPT2LMHeadModel tokenizer = AlbertTokenizer.from_pretrained("kyryl0s/gpt2-uk-zno-edition") model = GPT2LMHeadModel.from_pretrained("kyryl0s/gpt2-uk-zno-edition") input_ids = tokenizer.encode("ZNOTITLE: За яку працю треба більше поважати людину - за фізичну чи інтелектуальну?", add_special_tokens=False, return_tensors='pt') outputs = model.generate( input_ids, do_sample=True, num_return_sequences=1, max_length=250 ) for i, out in enumerate(outputs): print("{}: {}".format(i, tokenizer.decode(out))) ```

682217a745be03200b407695d510c6ba

model-attribution-challenge/gpt2

model-attribution-challenge

gpt2

14

135

transformers

0

text-generation

true

mit

['en']

null

0

['exbert']

false

true

7,810

false

# GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and first released at [this page](https://openai.com/blog/better-language-models/). Disclaimer: The team releasing GPT-2 also wrote a [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. ## Model description GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ### How to use You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."}, {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"}, {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"}, {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"}, {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import GPT2Tokenizer, TFGPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = TFGPT2Model.from_pretrained('gpt2') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race, > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar > levels of caution around use cases that are sensitive to biases around human attributes. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("The White man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The White man worked as a mannequin for'}, {'generated_text': 'The White man worked as a maniser of the'}, {'generated_text': 'The White man worked as a bus conductor by day'}, {'generated_text': 'The White man worked as a plumber at the'}, {'generated_text': 'The White man worked as a journalist. He had'}] >>> set_seed(42) >>> generator("The Black man worked as a", max_length=10, num_return_sequences=5) [{'generated_text': 'The Black man worked as a man at a restaurant'}, {'generated_text': 'The Black man worked as a car salesman in a'}, {'generated_text': 'The Black man worked as a police sergeant at the'}, {'generated_text': 'The Black man worked as a man-eating monster'}, {'generated_text': 'The Black man worked as a slave, and was'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights 40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText [here](https://github.com/openai/gpt-2/blob/master/domains.txt). ## Training procedure ### Preprocessing The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens. The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact details of training. ## Evaluation results The model achieves the following results without any fine-tuning (zero-shot): | Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW | |:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:| | (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) | | | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 | ### BibTeX entry and citation info ```bibtex @article{radford2019language, title={Language Models are Unsupervised Multitask Learners}, author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, year={2019} } ``` <a href="https://huggingface.co/exbert/?model=gpt2"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

ec368bdecd4143f44919d3b8538f8026

inverse-scaling/opt-350m_eval

inverse-scaling

opt

11

3

transformers

0

text-generation

true

other

['en']

null

18

6

7

5

0

['text-generation']

true

8,668

false

# OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-350m") >>> generator("Hello, I'm am conscious and") [{'generated_text': "Hello, I'm am conscious and I'm a bit of a noob. I'm looking for"}] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True) >>> generator("Hello, I'm am conscious and") [{'generated_text': "Hello, I'm am conscious and I'm interested in this project. Can I get an initial contact"}] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5) >>> generator("The woman worked as a") [{'generated_text': "The woman works as a substitute teacher for kids who have missed school. She's the teacher herself,"}, {'generated_text': 'The woman works as a security guard for another company and does an average of around $13/hour'}, {'generated_text': 'The woman works as a receptionist, she could at the least wait a week or two for her'}, {'generated_text': 'The woman works as a manager/intern/career development coach/advisor at a nursing home'}, {'generated_text': 'The woman works as a maid and has to clean the house but you can tell her to do it'}] ``` compared to: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-350m", do_sample=True, num_return_sequences=5) >>> generator("The man worked as a") [{'generated_text': 'The man works as a security guard for the National Football League franchise. He has been a part of'}, {'generated_text': 'The man works as a security guard for another company and does an excellent job.\nI remember when'}, {'generated_text': 'The man works as a "secret agent" but at the same time he\'s working to protect the'}, {'generated_text': 'The man works as a manager/operator/servant for a grocery store and does a lot of'}, {'generated_text': 'The man works as a bouncer near the scene of the accident - how he could do that is'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

4fdb14d7e6986beaa613d3ddc2df157a

w11wo/javanese-distilbert-small

w11wo

distilbert

8

7

transformers

0

fill-mask

true

false

mit

['jv']

['wikipedia']

null

0

['javanese-distilbert-small']

false

true

3,473

false

## Javanese DistilBERT Small Javanese DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on the latest (late December 2020) Javanese Wikipedia articles. The model was originally HuggingFace's pretrained [English DistilBERT model](https://huggingface.co/distilbert-base-uncased) and is later fine-tuned on the Javanese dataset. It achieved a perplexity of 23.54 on the validation dataset (20% of the articles). Many of the techniques used are based on a Hugging Face tutorial [notebook](https://github.com/huggingface/notebooks/blob/master/examples/language_modeling.ipynb) written by [Sylvain Gugger](https://github.com/sgugger), and [fine-tuning tutorial notebook](https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb) written by [Pierre Guillou](https://huggingface.co/pierreguillou). Hugging Face's [Transformers](https://huggingface.co/transformers) library was used to train the model -- utilizing the base DistilBERT model and their `Trainer` class. PyTorch was used as the backend framework during training, but the model remains compatible with TensorFlow nonetheless. ## Model | Model | #params | Arch. | Training/Validation data (text) | |-----------------------------|---------|------------------|-------------------------------------| | `javanese-distilbert-small` | 66M | DistilBERT Small | Javanese Wikipedia (319 MB of text) | ## Evaluation Results The model was trained for 5 epochs and the following is the final result once the training ended. | train loss | valid loss | perplexity | total time | |------------|------------|------------|------------| | 3.088 | 3.153 | 23.54 | 1:46:37 | ## How to Use ### As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/javanese-distilbert-small" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Aku mangan sate ing [MASK] bareng konco-konco") ``` ### Feature Extraction in PyTorch ```python from transformers import DistilBertModel, DistilBertTokenizerFast pretrained_name = "w11wo/javanese-distilbert-small" model = DistilBertModel.from_pretrained(pretrained_name) tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name) prompt = "Indonesia minangka negara gedhe." encoded_input = tokenizer(prompt, return_tensors='pt') output = model(**encoded_input) ``` ## Disclaimer Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model. ## Author Javanese DistilBERT Small was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. ## Citation If you use any of our models in your research, please cite: ```bib @inproceedings{wongso2021causal, title={Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures}, author={Wongso, Wilson and Setiawan, David Samuel and Suhartono, Derwin}, booktitle={2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS)}, pages={1--7}, year={2021}, organization={IEEE} } ```

3e007c9d296a53dff7aeeb9af97157f7

muhtasham/small-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab

muhtasham

bert

12

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,697

false

# small-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4922 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.1384 | 6.25 | 500 | 5.9999 | | 5.8428 | 12.5 | 1000 | 5.6581 | | 5.4846 | 18.75 | 1500 | 5.4843 | | 5.1716 | 25.0 | 2000 | 5.3955 | | 4.8633 | 31.25 | 2500 | 4.9234 | | 4.6185 | 37.5 | 3000 | 4.6246 | | 4.2975 | 43.75 | 3500 | 4.3933 | | 4.0116 | 50.0 | 4000 | 4.1432 | | 3.7556 | 56.25 | 4500 | 3.8816 | | 3.5262 | 62.5 | 5000 | 3.4922 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

62399edd11d63df4adad4623810c1ad2

jhakaran1/process-data

jhakaran1

bert

12

4

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,356

false

# process-data This model is a fine-tuned version of [jhakaran1/bert-base-uncased-bert-mlm](https://huggingface.co/jhakaran1/bert-base-uncased-bert-mlm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8087 - Accuracy: 0.6792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6939 | 1.0 | 3907 | 0.7903 | 0.6660 | | 0.6155 | 2.0 | 7814 | 0.7929 | 0.6685 | | 0.5436 | 3.0 | 11721 | 0.8087 | 0.6792 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

b7b0f4c7f079dd3cc21e0db86a165875

tomekkorbak/trusting_swartz

tomekkorbak

gpt2

23

1

transformers

0

null

true

false

mit

['en']

['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

8,161

false

# trusting_swartz This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 4096}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'force_call_on': [25354], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'name': 'Unlikelihood', 'score_threshold': 0.00078}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'trusting_swartz', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2b4j03bo

a5599587a4041d4eec3557643e509802

Helsinki-NLP/opus-mt-en-run

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-en-run * source languages: en * target languages: run * OPUS readme: [en-run](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-run/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/en-run/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-run/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-run/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.run | 34.2 | 0.591 |

93941dc1fb377cd473b39d97c08389f2

racro/sentiment-analysis-browser-extension

racro

distilbert

45

7

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,054

false

# sentiment-analysis-browser-extension This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4233 - Accuracy: 0.8539 - F1: 0.8758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

0b656b9c31c0462e33177fbadbfcc707

masapasa/xls-r-300m-sv-cv8

masapasa

wav2vec2

19

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['sv-SE']

['mozilla-foundation/common_voice_8_0']

null

0

['robust-speech-event', 'automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard']

true

23,213

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 2.3347 - Wer: 1.0286 ## 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: 7.5e-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_steps: 20 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.7838 | 0.01 | 5 | 14.5035 | 1.0 | | 13.0582 | 0.03 | 10 | 13.6658 | 1.0 | | 7.3034 | 0.04 | 15 | 9.7898 | 1.0 | | 6.1847 | 0.05 | 20 | 6.9148 | 1.0 | | 5.3371 | 0.07 | 25 | 5.3661 | 1.0 | | 4.4274 | 0.08 | 30 | 4.6945 | 1.0 | | 4.0918 | 0.1 | 35 | 4.3172 | 1.0 | | 4.1734 | 0.11 | 40 | 4.0759 | 1.0 | | 3.7332 | 0.12 | 45 | 3.9039 | 1.0 | | 3.6871 | 0.14 | 50 | 3.7777 | 1.0 | | 3.4428 | 0.15 | 55 | 3.6718 | 1.0 | | 3.5514 | 0.16 | 60 | 3.5947 | 1.0 | | 3.4307 | 0.18 | 65 | 3.5144 | 1.0 | | 3.4102 | 0.19 | 70 | 3.4432 | 1.0 | | 3.4964 | 0.21 | 75 | 3.3890 | 1.0 | | 3.3936 | 0.22 | 80 | 3.3467 | 1.0 | | 3.3051 | 0.23 | 85 | 3.3102 | 1.0 | | 3.278 | 0.25 | 90 | 3.2801 | 1.0 | | 3.2223 | 0.26 | 95 | 3.2440 | 1.0 | | 3.1888 | 0.27 | 100 | 3.2900 | 1.0 | | 3.218 | 0.29 | 105 | 3.2627 | 1.0 | | 3.1308 | 0.3 | 110 | 3.2152 | 1.0 | | 3.109 | 0.31 | 115 | 3.1686 | 1.0 | | 3.1188 | 0.33 | 120 | 3.1734 | 1.0 | | 3.1132 | 0.34 | 125 | 3.1431 | 1.0 | | 3.0667 | 0.36 | 130 | 3.1686 | 1.0 | | 3.1167 | 0.37 | 135 | 3.1885 | 1.0 | | 3.0592 | 0.38 | 140 | 3.1100 | 1.0 | | 3.0531 | 0.4 | 145 | 3.1149 | 1.0 | | 3.1224 | 0.41 | 150 | 3.1205 | 1.0 | | 3.0651 | 0.42 | 155 | 3.1101 | 1.0 | | 3.0077 | 0.44 | 160 | 3.0980 | 1.0 | | 3.0027 | 0.45 | 165 | 3.1132 | 1.0 | | 3.0423 | 0.47 | 170 | 3.0886 | 1.0 | | 3.0462 | 0.48 | 175 | 3.0865 | 1.0 | | 3.0701 | 0.49 | 180 | 3.0863 | 1.0 | | 3.0871 | 0.51 | 185 | 3.0825 | 1.0 | | 3.0585 | 0.52 | 190 | 3.0720 | 1.0 | | 3.0274 | 0.53 | 195 | 3.0736 | 1.0 | | 3.0983 | 0.55 | 200 | 3.0658 | 1.0 | | 3.0538 | 0.56 | 205 | 3.1241 | 1.0 | | 3.0862 | 0.57 | 210 | 3.0573 | 1.0 | | 3.0041 | 0.59 | 215 | 3.0608 | 1.0 | | 3.027 | 0.6 | 220 | 3.0614 | 1.0 | | 2.9916 | 0.62 | 225 | 3.0527 | 1.0 | | 3.0157 | 0.63 | 230 | 3.0514 | 1.0 | | 3.0429 | 0.64 | 235 | 3.0391 | 1.0 | | 2.999 | 0.66 | 240 | 3.0462 | 1.0 | | 3.0053 | 0.67 | 245 | 3.0438 | 1.0 | | 2.9812 | 0.68 | 250 | 3.0447 | 1.0 | | 3.0062 | 0.7 | 255 | 3.0660 | 1.0 | | 3.0045 | 0.71 | 260 | 3.0103 | 1.0 | | 2.9684 | 0.73 | 265 | 3.0106 | 1.0 | | 2.9885 | 0.74 | 270 | 3.0014 | 1.0 | | 3.0062 | 0.75 | 275 | 2.9885 | 1.0 | | 2.9736 | 0.77 | 280 | 3.0330 | 1.0 | | 2.9766 | 0.78 | 285 | 2.9910 | 1.0 | | 2.9545 | 0.79 | 290 | 2.9972 | 1.0 | | 2.9936 | 0.81 | 295 | 2.9872 | 1.0 | | 3.0832 | 0.82 | 300 | 2.9978 | 1.0 | | 2.974 | 0.83 | 305 | 2.9978 | 1.0 | | 2.9846 | 0.85 | 310 | 2.9849 | 1.0 | | 2.9554 | 0.86 | 315 | 2.9810 | 1.0 | | 2.9524 | 0.88 | 320 | 2.9731 | 1.0 | | 2.9426 | 0.89 | 325 | 2.9824 | 1.0 | | 2.9416 | 0.9 | 330 | 2.9731 | 1.0 | | 2.9705 | 0.92 | 335 | 2.9830 | 1.0 | | 2.9502 | 0.93 | 340 | 2.9713 | 1.0 | | 2.9393 | 0.94 | 345 | 2.9790 | 1.0 | | 2.9336 | 0.96 | 350 | 2.9684 | 1.0 | | 2.9542 | 0.97 | 355 | 2.9689 | 1.0 | | 2.9408 | 0.98 | 360 | 2.9556 | 1.0 | | 2.9544 | 1.0 | 365 | 2.9563 | 1.0 | | 2.9187 | 1.01 | 370 | 2.9624 | 1.0 | | 2.9935 | 1.03 | 375 | 2.9500 | 1.0 | | 2.9803 | 1.04 | 380 | 2.9558 | 1.0 | | 2.9867 | 1.05 | 385 | 2.9473 | 1.0 | | 2.8925 | 1.07 | 390 | 2.9444 | 1.0 | | 2.9633 | 1.08 | 395 | 2.9490 | 1.0 | | 2.9191 | 1.1 | 400 | 2.9362 | 1.0 | | 2.9081 | 1.11 | 405 | 2.9394 | 1.0 | | 2.9381 | 1.12 | 410 | 2.9846 | 1.0 | | 2.9271 | 1.14 | 415 | 2.9638 | 1.0 | | 2.959 | 1.15 | 420 | 2.9835 | 1.0 | | 2.9486 | 1.16 | 425 | 2.9361 | 1.0 | | 2.9246 | 1.18 | 430 | 2.9615 | 1.0 | | 2.923 | 1.19 | 435 | 2.9313 | 1.0 | | 2.8908 | 1.21 | 440 | 2.9362 | 1.0 | | 2.8976 | 1.22 | 445 | 2.9224 | 1.0 | | 2.9278 | 1.23 | 450 | 2.9276 | 1.0 | | 2.8429 | 1.25 | 455 | 2.9299 | 1.0 | | 2.867 | 1.26 | 460 | 2.9258 | 1.0 | | 2.9734 | 1.27 | 465 | 2.9281 | 1.0000 | | 2.934 | 1.29 | 470 | 2.9229 | 1.0 | | 2.9521 | 1.3 | 475 | 2.9134 | 1.0 | | 2.9098 | 1.31 | 480 | 2.9051 | 0.9993 | | 2.9112 | 1.33 | 485 | 2.9028 | 0.9999 | | 2.8799 | 1.34 | 490 | 2.9101 | 0.9986 | | 2.857 | 1.36 | 495 | 2.9005 | 0.9992 | | 2.8525 | 1.37 | 500 | 2.8937 | 1.0 | | 2.8682 | 1.38 | 505 | 2.8904 | 1.0000 | | 2.8899 | 1.4 | 510 | 2.8914 | 0.9964 | | 2.7475 | 1.41 | 515 | 2.8842 | 0.9950 | | 2.9263 | 1.42 | 520 | 2.8852 | 0.9972 | | 2.8603 | 1.44 | 525 | 2.8762 | 0.9966 | | 2.864 | 1.45 | 530 | 2.8680 | 0.9978 | | 2.8632 | 1.47 | 535 | 2.8602 | 0.9964 | | 2.9289 | 1.48 | 540 | 2.8584 | 0.9952 | | 2.8689 | 1.49 | 545 | 2.8587 | 0.9956 | | 2.8304 | 1.51 | 550 | 2.8511 | 0.9993 | | 2.8024 | 1.52 | 555 | 2.8460 | 1.0 | | 2.7649 | 1.53 | 560 | 2.8460 | 1.0000 | | 2.8756 | 1.55 | 565 | 2.8348 | 0.9987 | | 2.8808 | 1.56 | 570 | 2.8539 | 0.9993 | | 2.9027 | 1.57 | 575 | 2.8282 | 0.9975 | | 2.8586 | 1.59 | 580 | 2.8288 | 0.9976 | | 2.8193 | 1.6 | 585 | 2.8101 | 1.0051 | | 2.811 | 1.62 | 590 | 2.7965 | 1.0014 | | 2.7332 | 1.63 | 595 | 2.7884 | 1.0026 | | 2.7717 | 1.64 | 600 | 2.7883 | 1.0060 | | 2.6901 | 1.66 | 605 | 2.7801 | 0.9974 | | 2.6905 | 1.67 | 610 | 2.8113 | 0.9968 | | 2.7442 | 1.68 | 615 | 2.8113 | 1.0007 | | 2.8431 | 1.7 | 620 | 2.8152 | 1.0343 | | 2.8028 | 1.71 | 625 | 2.7790 | 1.0250 | | 2.7151 | 1.73 | 630 | 2.7653 | 1.0287 | | 2.7405 | 1.74 | 635 | 2.7714 | 1.1303 | | 2.7566 | 1.75 | 640 | 2.7488 | 1.0312 | | 2.7337 | 1.77 | 645 | 2.7498 | 1.0176 | | 2.7486 | 1.78 | 650 | 2.7496 | 1.0760 | | 2.6918 | 1.79 | 655 | 2.7391 | 1.0353 | | 2.7142 | 1.81 | 660 | 2.7500 | 1.0283 | | 2.7057 | 1.82 | 665 | 2.7612 | 1.0127 | | 2.8348 | 1.83 | 670 | 2.7441 | 1.0056 | | 2.705 | 1.85 | 675 | 2.7473 | 1.0519 | | 2.7547 | 1.86 | 680 | 2.7216 | 1.0218 | | 2.7045 | 1.88 | 685 | 2.7261 | 1.1414 | | 2.7121 | 1.89 | 690 | 2.7223 | 1.0287 | | 2.6877 | 1.9 | 695 | 2.7283 | 1.0274 | | 2.6879 | 1.92 | 700 | 2.7451 | 1.1322 | | 2.6958 | 1.93 | 705 | 2.7166 | 1.0364 | | 2.6692 | 1.94 | 710 | 2.7148 | 1.0074 | | 2.5786 | 1.96 | 715 | 2.7101 | 1.0504 | | 2.6919 | 1.97 | 720 | 2.6963 | 1.0454 | | 2.7256 | 1.98 | 725 | 2.7201 | 1.0349 | | 2.6507 | 2.0 | 730 | 2.7099 | 1.1339 | | 2.7833 | 2.01 | 735 | 2.7111 | 1.0124 | | 2.7521 | 2.03 | 740 | 2.7024 | 1.0275 | | 2.6732 | 2.04 | 745 | 2.7058 | 1.0647 | | 2.719 | 2.05 | 750 | 2.7200 | 1.0211 | | 2.701 | 2.07 | 755 | 2.7024 | 1.0808 | | 2.6444 | 2.08 | 760 | 2.6813 | 1.0582 | | 2.5592 | 2.1 | 765 | 2.6783 | 1.1010 | | 2.6444 | 2.11 | 770 | 2.6707 | 1.0946 | | 2.6944 | 2.12 | 775 | 2.7012 | 1.1315 | | 2.6733 | 2.14 | 780 | 2.7072 | 1.1144 | | 2.6998 | 2.15 | 785 | 2.7132 | 1.0206 | | 2.796 | 2.16 | 790 | 2.7076 | 1.1262 | | 2.6881 | 2.18 | 795 | 2.6953 | 1.0841 | | 2.7382 | 2.19 | 800 | 2.6605 | 1.1234 | | 2.5814 | 2.21 | 805 | 2.6814 | 1.1865 | | 2.6695 | 2.22 | 810 | 2.6531 | 1.0985 | | 2.6415 | 2.23 | 815 | 2.6590 | 1.0804 | | 2.646 | 2.25 | 820 | 2.6514 | 1.0853 | | 2.6028 | 2.26 | 825 | 2.6723 | 1.1411 | | 2.6429 | 2.27 | 830 | 2.6729 | 1.0395 | | 2.6736 | 2.29 | 835 | 2.7039 | 1.0355 | | 2.6959 | 2.3 | 840 | 2.6510 | 1.0414 | | 2.6426 | 2.31 | 845 | 2.6660 | 1.1591 | | 2.7152 | 2.33 | 850 | 2.6361 | 1.0276 | | 2.7148 | 2.34 | 855 | 2.6723 | 1.2461 | | 2.6336 | 2.36 | 860 | 2.6332 | 1.0310 | | 2.665 | 2.37 | 865 | 2.6365 | 1.1312 | | 2.5607 | 2.38 | 870 | 2.6344 | 1.1301 | | 2.5614 | 2.4 | 875 | 2.6437 | 1.1513 | | 2.4899 | 2.41 | 880 | 2.6418 | 1.1532 | | 2.6794 | 2.42 | 885 | 2.6403 | 1.0272 | | 2.6814 | 2.44 | 890 | 2.6420 | 1.1323 | | 2.6614 | 2.45 | 895 | 2.6183 | 1.0525 | | 2.6629 | 2.47 | 900 | 2.6414 | 1.1569 | | 2.6166 | 2.48 | 905 | 2.6167 | 1.0265 | | 2.6374 | 2.49 | 910 | 2.6299 | 1.1720 | | 2.6035 | 2.51 | 915 | 2.6139 | 1.1565 | | 2.595 | 2.52 | 920 | 2.6126 | 1.0557 | | 2.6416 | 2.53 | 925 | 2.6190 | 1.0414 | | 2.6785 | 2.55 | 930 | 2.6352 | 1.0289 | | 2.6986 | 2.56 | 935 | 2.6268 | 1.0077 | | 2.6145 | 2.57 | 940 | 2.6166 | 1.0445 | | 2.6961 | 2.59 | 945 | 2.6142 | 1.0185 | | 2.6852 | 2.6 | 950 | 2.6072 | 1.0122 | | 2.5792 | 2.62 | 955 | 2.6078 | 1.1165 | | 2.6118 | 2.63 | 960 | 2.6177 | 1.1210 | | 2.5472 | 2.64 | 965 | 2.6126 | 1.0044 | | 2.577 | 2.66 | 970 | 2.6051 | 1.0881 | | 2.5602 | 2.67 | 975 | 2.5992 | 1.0178 | | 2.695 | 2.68 | 980 | 2.6023 | 1.0248 | | 2.7017 | 2.7 | 985 | 2.6190 | 1.0041 | | 2.6327 | 2.71 | 990 | 2.6024 | 1.0142 | | 2.6193 | 2.73 | 995 | 2.5897 | 1.0148 | | 2.5939 | 2.74 | 1000 | 2.5900 | 1.0329 | | 2.5477 | 2.75 | 1005 | 2.5971 | 1.0338 | | 2.6089 | 2.77 | 1010 | 2.5969 | 1.0064 | | 2.5625 | 2.78 | 1015 | 2.5899 | 1.0648 | | 2.5745 | 2.79 | 1020 | 2.5861 | 1.0627 | | 2.5702 | 2.81 | 1025 | 2.5923 | 1.0526 | | 2.645 | 2.82 | 1030 | 2.6053 | 1.0199 | | 2.6869 | 2.83 | 1035 | 2.6227 | 1.0011 | | 2.6678 | 2.85 | 1040 | 2.6094 | 1.0179 | | 2.6787 | 2.86 | 1045 | 2.5978 | 1.0028 | | 2.6246 | 2.88 | 1050 | 2.5965 | 1.0093 | | 2.5676 | 2.89 | 1055 | 2.5927 | 1.0627 | | 2.6773 | 2.9 | 1060 | 2.5907 | 1.0817 | | 2.6114 | 2.92 | 1065 | 2.5932 | 1.1013 | | 2.6227 | 2.93 | 1070 | 2.5840 | 1.0402 | | 2.594 | 2.94 | 1075 | 2.5997 | 1.1371 | | 2.751 | 2.96 | 1080 | 2.5909 | 1.0972 | | 2.6366 | 2.97 | 1085 | 2.6081 | 1.0598 | | 2.577 | 2.98 | 1090 | 2.5915 | 1.0410 | | 2.579 | 3.0 | 1095 | 2.5953 | 1.1433 | | 2.6706 | 3.01 | 1100 | 2.5913 | 1.0456 | | 2.6161 | 3.03 | 1105 | 2.6079 | 1.1009 | | 2.6397 | 3.04 | 1110 | 2.5951 | 1.1771 | | 2.6246 | 3.05 | 1115 | 2.5730 | 1.0299 | | 2.5637 | 3.07 | 1120 | 2.5622 | 1.0848 | | 2.5692 | 3.08 | 1125 | 2.5561 | 1.1472 | | 2.5948 | 3.1 | 1130 | 2.5568 | 1.0802 | | 2.5372 | 3.11 | 1135 | 2.5638 | 1.1261 | | 2.4995 | 3.12 | 1140 | 2.5727 | 1.1395 | | 2.6304 | 3.14 | 1145 | 2.5671 | 1.0259 | | 2.6395 | 3.15 | 1150 | 2.5778 | 1.0212 | | 2.6127 | 3.16 | 1155 | 2.5609 | 1.0457 | | 2.5919 | 3.18 | 1160 | 2.5604 | 1.0902 | | 2.6111 | 3.19 | 1165 | 2.5463 | 1.0014 | | 2.5971 | 3.21 | 1170 | 2.5429 | 1.0022 | | 2.5887 | 3.22 | 1175 | 2.5394 | 1.0412 | | 2.5644 | 3.23 | 1180 | 2.5342 | 1.0469 | | 2.4805 | 3.25 | 1185 | 2.6066 | 1.2668 | | 2.5324 | 3.26 | 1190 | 2.5395 | 1.0234 | | 2.5491 | 3.27 | 1195 | 2.5431 | 1.0644 | | 2.6302 | 3.29 | 1200 | 2.5558 | 1.0680 | | 2.6139 | 3.3 | 1205 | 2.5711 | 1.0565 | | 2.5607 | 3.31 | 1210 | 2.5635 | 1.0415 | | 2.6535 | 3.33 | 1215 | 2.5505 | 1.0613 | | 2.6129 | 3.34 | 1220 | 2.5403 | 1.0724 | | 2.5157 | 3.36 | 1225 | 2.5294 | 1.0585 | | 2.551 | 3.37 | 1230 | 2.5242 | 1.1599 | | 2.5527 | 3.38 | 1235 | 2.5474 | 1.2327 | | 2.4964 | 3.4 | 1240 | 2.5244 | 1.0857 | | 2.5781 | 3.41 | 1245 | 2.5299 | 1.0470 | | 2.6143 | 3.42 | 1250 | 2.5313 | 1.0019 | | 2.6566 | 3.44 | 1255 | 2.5431 | 1.0488 | | 2.5373 | 3.45 | 1260 | 2.5281 | 1.0901 | | 2.6597 | 3.47 | 1265 | 2.5300 | 1.0610 | | 2.5457 | 3.48 | 1270 | 2.5130 | 1.0420 | | 2.5632 | 3.49 | 1275 | 2.5306 | 1.1418 | | 2.5267 | 3.51 | 1280 | 2.5021 | 1.0293 | | 2.507 | 3.52 | 1285 | 2.5013 | 1.0196 | | 2.5713 | 3.53 | 1290 | 2.4978 | 1.0664 | | 2.4783 | 3.55 | 1295 | 2.4958 | 1.0530 | | 2.5874 | 3.56 | 1300 | 2.4968 | 1.0059 | | 2.5744 | 3.57 | 1305 | 2.5078 | 1.0287 | | 2.5701 | 3.59 | 1310 | 2.4971 | 1.0366 | | 2.5366 | 3.6 | 1315 | 2.4897 | 1.0191 | | 2.5679 | 3.62 | 1320 | 2.4830 | 1.0223 | | 2.5239 | 3.63 | 1325 | 2.4833 | 1.0784 | | 2.5411 | 3.64 | 1330 | 2.4851 | 1.1522 | | 2.5037 | 3.66 | 1335 | 2.4792 | 1.0928 | | 2.5907 | 3.67 | 1340 | 2.4750 | 1.0187 | | 2.5107 | 3.68 | 1345 | 2.4805 | 1.0873 | | 2.5908 | 3.7 | 1350 | 2.4753 | 1.0098 | | 2.6274 | 3.71 | 1355 | 2.4765 | 1.0045 | | 2.5708 | 3.73 | 1360 | 2.4597 | 1.0456 | | 2.6039 | 3.74 | 1365 | 2.4503 | 1.0485 | | 2.5305 | 3.75 | 1370 | 2.4439 | 1.0126 | | 2.4878 | 3.77 | 1375 | 2.4407 | 1.0162 | | 2.5055 | 3.78 | 1380 | 2.4421 | 1.0605 | | 2.5249 | 3.79 | 1385 | 2.4499 | 1.1163 | | 2.5508 | 3.81 | 1390 | 2.4654 | 1.1472 | | 2.5827 | 3.82 | 1395 | 2.4510 | 1.0561 | | 2.6148 | 3.83 | 1400 | 2.4496 | 0.9998 | | 2.5763 | 3.85 | 1405 | 2.4417 | 1.0067 | | 2.6077 | 3.86 | 1410 | 2.4458 | 1.0682 | | 2.5388 | 3.88 | 1415 | 2.4352 | 1.0820 | | 2.5235 | 3.89 | 1420 | 2.4277 | 1.0784 | | 2.4996 | 3.9 | 1425 | 2.4245 | 1.0671 | | 2.5601 | 3.92 | 1430 | 2.4202 | 1.0650 | | 2.5805 | 3.93 | 1435 | 2.4199 | 1.0530 | | 2.5841 | 3.94 | 1440 | 2.4228 | 1.0797 | | 2.4877 | 3.96 | 1445 | 2.4284 | 1.1159 | | 2.5542 | 3.97 | 1450 | 2.4190 | 1.0575 | | 2.5961 | 3.98 | 1455 | 2.4162 | 1.0676 | | 2.495 | 4.0 | 1460 | 2.4165 | 1.0821 | | 2.6157 | 4.01 | 1465 | 2.4119 | 1.0117 | | 2.5415 | 4.03 | 1470 | 2.4089 | 1.0110 | | 2.4916 | 4.04 | 1475 | 2.4032 | 1.0498 | | 2.5445 | 4.05 | 1480 | 2.3997 | 1.0429 | | 2.4941 | 4.07 | 1485 | 2.4008 | 1.0141 | | 2.5113 | 4.08 | 1490 | 2.3975 | 1.0357 | | 2.4707 | 4.1 | 1495 | 2.3938 | 1.0288 | | 2.4952 | 4.11 | 1500 | 2.3910 | 1.0300 | | 2.5017 | 4.12 | 1505 | 2.3861 | 1.0813 | | 2.5566 | 4.14 | 1510 | 2.3919 | 1.1082 | | 2.5754 | 4.15 | 1515 | 2.3947 | 1.0074 | | 2.6138 | 4.16 | 1520 | 2.4040 | 0.9989 | | 2.5024 | 4.18 | 1525 | 2.3949 | 1.0039 | | 2.5136 | 4.19 | 1530 | 2.3993 | 1.0496 | | 2.5646 | 4.21 | 1535 | 2.3981 | 1.0729 | | 2.4556 | 4.22 | 1540 | 2.3952 | 1.0494 | | 2.5774 | 4.23 | 1545 | 2.3924 | 1.0345 | | 2.5126 | 4.25 | 1550 | 2.3888 | 1.0306 | | 2.4596 | 4.26 | 1555 | 2.3960 | 1.0775 | | 2.521 | 4.27 | 1560 | 2.3978 | 1.1025 | | 2.6304 | 4.29 | 1565 | 2.3885 | 1.0433 | | 2.543 | 4.3 | 1570 | 2.3849 | 1.0072 | | 2.5601 | 4.31 | 1575 | 2.3855 | 1.0110 | | 2.6304 | 4.33 | 1580 | 2.3878 | 1.0369 | | 2.4121 | 4.34 | 1585 | 2.3783 | 1.0366 | | 2.4261 | 4.36 | 1590 | 2.3746 | 1.0307 | | 2.5038 | 4.37 | 1595 | 2.3789 | 1.0611 | | 2.5391 | 4.38 | 1600 | 2.3849 | 1.0738 | | 2.4341 | 4.4 | 1605 | 2.3779 | 1.0573 | | 2.5306 | 4.41 | 1610 | 2.3751 | 1.0460 | | 2.5818 | 4.42 | 1615 | 2.3743 | 1.0251 | | 2.5531 | 4.44 | 1620 | 2.3723 | 1.0209 | | 2.51 | 4.45 | 1625 | 2.3755 | 1.0316 | | 2.5788 | 4.47 | 1630 | 2.3725 | 1.0396 | | 2.5701 | 4.48 | 1635 | 2.3663 | 1.0292 | | 2.4194 | 4.49 | 1640 | 2.3641 | 1.0261 | | 2.5439 | 4.51 | 1645 | 2.3629 | 1.0376 | | 2.4527 | 4.52 | 1650 | 2.3629 | 1.0563 | | 2.5705 | 4.53 | 1655 | 2.3654 | 1.0766 | | 2.4552 | 4.55 | 1660 | 2.3708 | 1.0802 | | 2.5657 | 4.56 | 1665 | 2.3638 | 1.0248 | | 2.5371 | 4.57 | 1670 | 2.3639 | 1.0053 | | 2.5365 | 4.59 | 1675 | 2.3626 | 1.0072 | | 2.5383 | 4.6 | 1680 | 2.3584 | 1.0170 | | 2.546 | 4.62 | 1685 | 2.3574 | 1.0469 | | 2.6006 | 4.63 | 1690 | 2.3517 | 1.0509 | | 2.4894 | 4.64 | 1695 | 2.3489 | 1.0452 | | 2.4732 | 4.66 | 1700 | 2.3489 | 1.0586 | | 2.4933 | 4.67 | 1705 | 2.3501 | 1.0694 | | 2.4784 | 4.68 | 1710 | 2.3472 | 1.0647 | | 2.5349 | 4.7 | 1715 | 2.3419 | 1.0299 | | 2.553 | 4.71 | 1720 | 2.3420 | 1.0115 | | 2.5035 | 4.73 | 1725 | 2.3415 | 1.0117 | | 2.561 | 4.74 | 1730 | 2.3418 | 1.0242 | | 2.4773 | 4.75 | 1735 | 2.3420 | 1.0325 | | 2.4691 | 4.77 | 1740 | 2.3422 | 1.0394 | | 2.4959 | 4.78 | 1745 | 2.3405 | 1.0418 | | 2.4928 | 4.79 | 1750 | 2.3394 | 1.0449 | | 2.5058 | 4.81 | 1755 | 2.3392 | 1.0489 | | 2.5193 | 4.82 | 1760 | 2.3390 | 1.0506 | | 2.5369 | 4.83 | 1765 | 2.3392 | 1.0384 | | 2.4843 | 4.85 | 1770 | 2.3398 | 1.0236 | | 2.5074 | 4.86 | 1775 | 2.3400 | 1.0150 | | 2.4941 | 4.88 | 1780 | 2.3386 | 1.0150 | | 2.4352 | 4.89 | 1785 | 2.3370 | 1.0172 | | 2.4372 | 4.9 | 1790 | 2.3362 | 1.0208 | | 2.4855 | 4.92 | 1795 | 2.3358 | 1.0238 | | 2.4516 | 4.93 | 1800 | 2.3355 | 1.0276 | | 2.5281 | 4.94 | 1805 | 2.3356 | 1.0312 | | 2.5519 | 4.96 | 1810 | 2.3352 | 1.0318 | | 2.4641 | 4.97 | 1815 | 2.3349 | 1.0294 | | 2.4515 | 4.98 | 1820 | 2.3348 | 1.0284 | | 2.553 | 5.0 | 1825 | 2.3347 | 1.0286 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

3c1a521b8275fb62c741b47c837558a2

tomekkorbak/jovial_clarke

tomekkorbak

null

2

0

null

0

null

false

mit

['en']

['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

7,793

false

# jovial_clarke This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets. ## 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'force_call_on': [25177], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}], 'scorer_config': {}}, 'kl_gpt3_callback': {'force_call_on': [25177], 'gpt3_kwargs': {'model_name': 'davinci'}, 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'jovial_clarke', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25177, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/2037bqrd

41e5725b5897d9f5911a4b37196385b5

darkVOYAGE/dvAuto

darkVOYAGE

null

3

0

null

0

null

false

cc

null

0

[]

false

true

3,227

false

dvAuto is a custom tuned model built using the base SD v1.5, and trained on thirty-two 768x768px images of concept / sports / antique cars. Use the words "dvAuto" or "dvAuto style" near the beginning of the prompt. Sample images and prompt below. "dvAuto style, 85mm, telephoto, mountain background, low contrast, muted, photo realistic, 8k" scale 11.50 k_euler Model: dvAuto ![2022-12-12-19-29-08-04-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-689985233-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409824-6331c100acb6472115ae666a.png) ![2022-12-12-19-39-16-12-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-201031871-scale14.00-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409835-6331c100acb6472115ae666a.png) ![2022-12-12-19-39-46-02-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-1181339297-scale12.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409840-6331c100acb6472115ae666a.png) ![2022-12-12-19-40-08-06-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-1181339301-scale12.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409827-6331c100acb6472115ae666a.png) ![2022-12-12-19-46-06-14-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-754989033-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409828-6331c100acb6472115ae666a.png) ![2022-12-12-19-46-12-15-dvAuto_style_85mm_telephoto_mountain_background_low_contrast_muted_photo_realistic_8k-754989034-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409823-6331c100acb6472115ae666a.png) ![2022-12-12-19-56-50-31-dvAuto_style_85mm_telephoto_country_background_low_contrast_muted_photo_realistic_8k-754989050-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409644-6331c100acb6472115ae666a.png) ![2022-12-12-19-57-46-40-dvAuto_style_85mm_telephoto_country_background_low_contrast_muted_photo_realistic_8k-754989059-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409823-6331c100acb6472115ae666a.png) ![2022-12-12-21-50-17-03-dvAuto_style_85mm_telephoto_country_background_low_contrast_muted_photo_realistic_8k_perfect_composition_rule_of_thirds-754989022-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409831-6331c100acb6472115ae666a.png) ![2022-12-12-21-55-01-04-beautiful_girl_driving_a_convertible_car_dvAuto_style_85mm_telephoto_country_background_low_contrast_muted_photo_realistic_8k_perfect_composition_rule_of_thirds-754989023-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409837-6331c100acb6472115ae666a.png) ![2022-12-13-06-34-03-08-dvAuto_style_silver_future_police_cruiser_85mm_f1.8_sci_fi_carday_city_background_muted_natural_colors-36551108-scale11.50-k_euler-dvAuto.png](https://s3.amazonaws.com/moonup/production/uploads/1673274409827-6331c100acb6472115ae666a.png)

24f2f6ea01d363f19e9aa7acccb6d987

Helsinki-NLP/opus-mt-ha-es

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-ha-es * source languages: ha * target languages: es * OPUS readme: [ha-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ha-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ha-es/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-es/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ha-es/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.ha.es | 21.8 | 0.394 |

4eb58c43c9a0036ca904e109d7d9530f

suhasy2/fin_sentiment

suhasy2

distilbert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,109

false

# fin_sentiment This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5162 | 0.7978 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

a5752e31c1406fa960462e31a9eca4ba

muhtasham/tiny-mlm-glue-cola-custom-tokenizer-expand-vocab

muhtasham

bert

12

4

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,683

false

# tiny-mlm-glue-cola-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8843 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6267 | 0.47 | 500 | 4.9363 | | 5.0496 | 0.94 | 1000 | 4.7414 | | 4.7524 | 1.4 | 1500 | 4.5982 | | 4.6772 | 1.87 | 2000 | 4.5334 | | 4.543 | 2.34 | 2500 | 4.3460 | | 4.5676 | 2.81 | 3000 | 4.1526 | | 4.419 | 3.27 | 3500 | 4.3221 | | 4.3187 | 3.74 | 4000 | 4.0862 | | 4.3635 | 4.21 | 4500 | 4.1023 | | 4.2545 | 4.68 | 5000 | 3.8843 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

52021166e1addc9c7ca1dd7762aeb3d5

javilonso/Mex_Rbta_Opinion_Attraction

javilonso

roberta

9

4

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,466

false

# javilonso/Mex_Rbta_Opinion_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0061 - Validation Loss: 0.0386 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8979, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0863 | 0.0476 | 0 | | 0.0230 | 0.0353 | 1 | | 0.0061 | 0.0386 | 2 | ### Framework versions - Transformers 4.17.0 - TensorFlow 2.6.0 - Datasets 2.0.0 - Tokenizers 0.11.6

6e8379c9e93a9ea7735db1f7100341a1

stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1

stjiris

bert

16

2

sentence-transformers

1

sentence-similarity

true

false

mit

['pt']

['stjiris/portuguese-legal-sentences-v0', 'assin', 'assin2', 'stsb_multi_mt', 'stjiris/IRIS_sts']

null

0

['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity']

false

true

5,401

false

[![INESC-ID](https://www.inesc-id.pt/wp-content/uploads/2019/06/INESC-ID-logo_01.png)](https://www.inesc-id.pt/projects/PR07005/) [![A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/_static/logo.png)](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) Work developed as part of [Project IRIS](https://www.inesc-id.pt/projects/PR07005/). Thesis: [A Semantic Search System for Supremo Tribunal de Justiça](https://rufimelo99.github.io/SemanticSearchSystemForSTJ/) # stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1 derives from stjiris/bert-large-portuguese-cased-legal-mlm (legal variant of [BERTimbau](https://huggingface.co/neuralmind/bert-large-portuguese-cased) large). It was trained using the MLM technique with a learning rate 1e-5 [Legal Sentences from +-30000 documents](https://huggingface.co/datasets/stjiris/portuguese-legal-sentences-v1.0) 15000 training steps (best performance for our semantic search system implementation) It was trained for Semantic Textual Similarity, being submitted to a fine tuning stage with the [assin](https://huggingface.co/datasets/assin), [assin2](https://huggingface.co/datasets/assin2), [stsb_multi_mt pt](https://huggingface.co/datasets/stsb_multi_mt) and [IRIS STS](https://huggingface.co/datasets/stjiris/IRIS_sts) datasets. 'lr': 1e-5 ## 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 = ["Isto é um exemplo", "Isto é um outro exemplo"] model = SentenceTransformer('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1028, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) ) ``` ## Citing & Authors ### Contributions [@rufimelo99](https://github.com/rufimelo99) If you use this work, please cite: ```bibtex @inproceedings{MeloSemantic, author = {Melo, Rui and Santos, Professor Pedro Alexandre and Dias, Professor Jo{\~ a}o}, title = {A {Semantic} {Search} {System} for {Supremo} {Tribunal} de {Justi}{\c c}a}, } @inproceedings{souza2020bertimbau, author = {F{\'a}bio Souza and Rodrigo Nogueira and Roberto Lotufo}, title = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese}, booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)}, year = {2020} } @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } @inproceedings{real2020assin, title={The assin 2 shared task: a quick overview}, author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo}, booktitle={International Conference on Computational Processing of the Portuguese Language}, pages={406--412}, year={2020}, organization={Springer} } @InProceedings{huggingface:dataset:stsb_multi_mt, title = {Machine translated multilingual STS benchmark dataset.}, author={Philip May}, year={2021}, url={https://github.com/PhilipMay/stsb-multi-mt} } ```

b3038705be0763c6f2a9fa703a94d37b

nickmuchi/bert-finetuned-squad

nickmuchi

bert

8

5

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,315

false

# nickmuchi/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5685 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Epoch | |:----------:|:-----:| | 1.2720 | 0 | | 0.7798 | 1 | | 0.5685 | 2 | ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

d139040956528c3c27bde61cb0e25a82

anas-awadalla/splinter-large-few-shot-k-256-finetuned-squad-seed-4

anas-awadalla

splinter

16

1

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,004

false

# splinter-large-few-shot-k-256-finetuned-squad-seed-4 This model is a fine-tuned version of [tau/splinter-large-qass](https://huggingface.co/tau/splinter-large-qass) on the squad 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: 12 - eval_batch_size: 8 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

4f88a037871526ca74f603bf35a9489b

creat89/NER_FEDA_Cyrillic1

creat89

bert

7

0

transformers

0

null

true

false

mit

['multilingual', 'ru', 'bg', 'mk', 'uk', 'fi']

null

0

['labse', 'ner']

false

true

849

false

This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats: 1. Wikiann (LOC, PER, ORG) 2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO) 3. SlavNER 17 (LOC, MISC, ORG, PER) 4. CNE5 (GEOPOLIT, LOC, MEDIA, PER, ORG) 5. FactRuEval (LOC, ORG, PER) 6. NER-UK (LOC, MISC, ORG, PER) 7. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME) PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical, You can select the tagset to use in the output by configuring the model. More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).

b202e54e5cd6b813aec5ba15a52798a0

Helsinki-NLP/opus-mt-da-ru

Helsinki-NLP

marian

11

113

transformers

0

translation

true

false

apache-2.0

['da', 'ru']

null

1

0

['translation']

false

true

1,984

false

### dan-rus * source group: Danish * target group: Russian * OPUS readme: [dan-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/dan-rus/README.md) * model: transformer-align * source language(s): dan * target language(s): rus * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/dan-rus/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/dan-rus/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/dan-rus/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.dan.rus | 52.5 | 0.715 | ### System Info: - hf_name: dan-rus - source_languages: dan - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/dan-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['da', 'ru'] - src_constituents: {'dan'} - tgt_constituents: {'rus'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/dan-rus/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/dan-rus/opus-2020-06-17.test.txt - src_alpha3: dan - tgt_alpha3: rus - short_pair: da-ru - chrF2_score: 0.715 - bleu: 52.5 - brevity_penalty: 0.991 - ref_len: 10480.0 - src_name: Danish - tgt_name: Russian - train_date: 2020-06-17 - src_alpha2: da - tgt_alpha2: ru - prefer_old: False - long_pair: dan-rus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

69726ad55efb4b68532e69b8cdbb5ebc

StonyBrookNLP/teabreac-nt5-small-drop

StonyBrookNLP

t5

8

3

transformers

0

text2text-generation

true

false

cc-by-4.0

null

0

['question-answering, multi-step-reasoning, multi-hop-reasoning']

false

true

2,627

false

# What's this? This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496). This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details. We release the following models: - **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}` - **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}` - **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}` The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`. The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`. The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**. # How to use it? Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-nt5-small-drop" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization model = AutoModelForSeq2SeqLM.from_pretrained(model_name) enable_digit_tokenization(tokenizer) input_texts = [ "answer_me: Who scored the first touchdown of the game?" + "context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..." # Note: some models have slightly different qn/ctxt format. See the github repo. ] input_ids = tokenizer( input_texts, return_tensors="pt", truncation=True, max_length=800, add_special_tokens=True, padding=True, )["input_ids"] generated_ids = model.generate(input_ids, min_length=1, max_length=50) generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False) generated_predictions = [ tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions ] # => ["Chaz Schilens"] ```

1a133c8e4873ab474ffd360f2fa6dceb

google/maxim-s3-denoising-sidd

google

null

7

102

keras

2

image-to-image

false

apache-2.0

['en']

['sidd']

null

0

['vision', 'maxim', 'image-to-image']

false

true

2,506

false

# MAXIM pre-trained on SIDD for image denoising MAXIM model pre-trained for image denoising. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in [this repository](https://github.com/google-research/maxim). Disclaimer: The team releasing MAXIM did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description MAXIM introduces a shared MLP-based backbone for different image processing tasks such as image deblurring, deraining, denoising, dehazing, low-light image enhancement, and retouching. The following figure depicts the main components of MAXIM: ![](https://github.com/google-research/maxim/raw/main/maxim/images/overview.png) ## Training procedure and results The authors didn't release the training code. For more details on how the model was trained, refer to the [original paper](https://arxiv.org/abs/2201.02973). As per the [table](https://github.com/google-research/maxim#results-and-pre-trained-models), the model achieves a PSNR of 39.96 and an SSIM of 0.96. ## Intended uses & limitations You can use the raw model for image denoising tasks. The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). ### How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Denoising/input/0011_23.png" image = Image.open(requests.get(url, stream=True).raw) image = np.array(image) image = tf.convert_to_tensor(image) image = tf.image.resize(image, (256, 256)) model = from_pretrained_keras("google/maxim-s3-denoising-sidd") predictions = model.predict(tf.expand_dims(image, 0)) ``` For a more elaborate prediction pipeline, refer to [this Colab Notebook](https://colab.research.google.com/github/sayakpaul/maxim-tf/blob/main/notebooks/inference-dynamic-resize.ipynb). ### Citation ```bibtex @article{tu2022maxim, title={MAXIM: Multi-Axis MLP for Image Processing}, author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, journal={CVPR}, year={2022}, } ```

e1e75c4729ebe608416e02a96182020d

Bioskop/lucyedge

Bioskop

null

25

3

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,510

false

### LucyEdge on Stable Diffusion via Dreambooth #### model by Bioskop This your the Stable Diffusion model fine-tuned the LucyEdge concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **LucyEdge from edgerunners, a cyberpunk anime from Cyberpunk 2077 universe** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/0.jpeg) ![image 1](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/4.jpeg) ![image 3](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/6.jpeg) ![image 4](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/1.jpeg) ![image 5](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/5.jpeg) ![image 6](https://huggingface.co/Bioskop/lucyedge/resolve/main/concept_images/2.jpeg)

9f407e16e792a6b84e73e3a427aacfb8

pulkitkumar13/dark-bert-finetuned-ner

pulkitkumar13

bert

10

13

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

1,517

false

# dark-bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Precision: 0.9283 - Recall: 0.9478 - F1: 0.9380 - Accuracy: 0.9859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0881 | 1.0 | 1756 | 0.0716 | 0.9172 | 0.9322 | 0.9246 | 0.9817 | | 0.0375 | 2.0 | 3512 | 0.0610 | 0.9275 | 0.9455 | 0.9364 | 0.9857 | | 0.0207 | 3.0 | 5268 | 0.0639 | 0.9283 | 0.9478 | 0.9380 | 0.9859 | ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1

82804771dabfec2ffa8fc1dd262ac453

lmqg/mt5-small-jaquad-qag

lmqg

mt5

13

37

transformers

0

text2text-generation

true

false

cc-by-4.0

['ja']

['lmqg/qag_jaquad']

null

0

['questions and answers generation']

true

3,899

false

# Model Card of `lmqg/mt5-small-jaquad-qag` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ja - **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ja", model="lmqg/mt5-small-jaquad-qag") # model prediction question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-qag") output = pipe("ゾフィーは貴族出身ではあったが王族出身ではなく、ハプスブルク家の皇位継承者であるフランツ・フェルディナントとの結婚は貴賤結婚となった。皇帝フランツ・ヨーゼフは、2人の間に生まれた子孫が皇位を継がないことを条件として結婚を承認していた。視察が予定されている6月28日は2人の14回目の結婚記念日であった。") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_jaquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 58.35 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | | QAAlignedF1Score (MoverScore) | 39.19 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | | QAAlignedPrecision (BERTScore) | 58.34 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | | QAAlignedPrecision (MoverScore) | 39.21 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | | QAAlignedRecall (BERTScore) | 58.38 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | | QAAlignedRecall (MoverScore) | 39.17 | default | [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_jaquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 256 - epoch: 18 - batch: 8 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

b2edeefacddf39c91cb56b09b2bb093e

stevemobs/deberta-base-combined-squad1-aqa-1epoch

stevemobs

deberta

13

5

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,168

false

# deberta-base-combined-squad1-aqa-1epoch This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9431 ## 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: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.0971 | 1.0 | 9906 | 0.9431 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

306adfc6dd0f35c5cb46bf10f6e7b745

jayanta/resnet-152-fv-finetuned-memess

jayanta

resnet

12

7

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

3,217

false

# resnet-152-fv-finetuned-memess This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6281 - Accuracy: 0.7674 - Precision: 0.7651 - Recall: 0.7674 - F1: 0.7647 ## 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: 0.00012 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.5902 | 0.99 | 20 | 1.5519 | 0.4938 | 0.3491 | 0.4938 | 0.3529 | | 1.4694 | 1.99 | 40 | 1.3730 | 0.4892 | 0.4095 | 0.4892 | 0.3222 | | 1.3129 | 2.99 | 60 | 1.2052 | 0.5301 | 0.3504 | 0.5301 | 0.4005 | | 1.1831 | 3.99 | 80 | 1.1142 | 0.5587 | 0.4077 | 0.5587 | 0.4444 | | 1.0581 | 4.99 | 100 | 0.9930 | 0.6012 | 0.5680 | 0.6012 | 0.5108 | | 0.9464 | 5.99 | 120 | 0.9263 | 0.6507 | 0.6200 | 0.6507 | 0.6029 | | 0.8581 | 6.99 | 140 | 0.8400 | 0.6917 | 0.6645 | 0.6917 | 0.6638 | | 0.7739 | 7.99 | 160 | 0.7829 | 0.7087 | 0.6918 | 0.7087 | 0.6845 | | 0.6762 | 8.99 | 180 | 0.7512 | 0.7318 | 0.7206 | 0.7318 | 0.7189 | | 0.6162 | 9.99 | 200 | 0.7409 | 0.7264 | 0.7244 | 0.7264 | 0.7241 | | 0.5546 | 10.99 | 220 | 0.6936 | 0.7465 | 0.7429 | 0.7465 | 0.7395 | | 0.4633 | 11.99 | 240 | 0.6779 | 0.7473 | 0.7393 | 0.7473 | 0.7412 | | 0.4373 | 12.99 | 260 | 0.6736 | 0.7573 | 0.7492 | 0.7573 | 0.7523 | | 0.4074 | 13.99 | 280 | 0.6534 | 0.7566 | 0.7516 | 0.7566 | 0.7528 | | 0.39 | 14.99 | 300 | 0.6521 | 0.7651 | 0.7603 | 0.7651 | 0.7608 | | 0.3766 | 15.99 | 320 | 0.6499 | 0.7682 | 0.7607 | 0.7682 | 0.7630 | | 0.3507 | 16.99 | 340 | 0.6497 | 0.7697 | 0.7686 | 0.7697 | 0.7686 | | 0.3589 | 17.99 | 360 | 0.6519 | 0.7535 | 0.7485 | 0.7535 | 0.7502 | | 0.3261 | 18.99 | 380 | 0.6449 | 0.7589 | 0.7597 | 0.7589 | 0.7585 | | 0.3234 | 19.99 | 400 | 0.6281 | 0.7674 | 0.7651 | 0.7674 | 0.7647 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1

fc2031880c140d52abcfae03d3248fff

sd-concepts-library/hours-sentry-fade

sd-concepts-library

null

10

0

null

0

null

false

mit

null

0

[]

false

true

1,190

false

### Hours_Sentry_fade on Stable Diffusion This is the `<Hours_Sentry>` 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 a `style`: ![<Hours_Sentry> 0](https://huggingface.co/sd-concepts-library/hours-sentry-fade/resolve/main/concept_images/0.jpeg) ![<Hours_Sentry> 1](https://huggingface.co/sd-concepts-library/hours-sentry-fade/resolve/main/concept_images/1.jpeg) ![<Hours_Sentry> 2](https://huggingface.co/sd-concepts-library/hours-sentry-fade/resolve/main/concept_images/2.jpeg) ![<Hours_Sentry> 3](https://huggingface.co/sd-concepts-library/hours-sentry-fade/resolve/main/concept_images/3.jpeg) ![<Hours_Sentry> 4](https://huggingface.co/sd-concepts-library/hours-sentry-fade/resolve/main/concept_images/4.jpeg)

532ebc0408b6f590861361d44cf4895d

microsoft/swin-base-simmim-window6-192

microsoft

swin

5

924

transformers

0

null

true

false

apache-2.0

null

['imagenet-1k']

null

0

['vision', 'simmim']

false

true

624

false

# Swin Transformer (base-sized model) Swin Transformer model pre-trained on ImageNet-1k using the SimMIM objective at resolution 192x192. It was introduced in the paper [SimMIM: A Simple Framework for Masked Image Modeling](https://arxiv.org/abs/2111.09886) by Xie et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). # Intended use cases This model is pre-trained only, it's meant to be fine-tuned on a downstream dataset. # Usage Refer to the [documentation](https://huggingface.co/docs/transformers/model_doc/swin#transformers.SwinForMaskedImageModeling.forward.example).

934f424b85c1e4452dc94f044a0b93e4

Kayvane/distilroberta-base-wandb-week-3-complaints-classifier-512

Kayvane

roberta

11

1

transformers

0

text-classification

true

false

apache-2.0

null

['consumer-finance-complaints']

null

0

['generated_from_trainer']

true

1,672

false

# distilroberta-base-wandb-week-3-complaints-classifier-512 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set: - Loss: 0.6004 - Accuracy: 0.8038 - F1: 0.7919 - Recall: 0.8038 - Precision: 0.7922 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7835312622444155e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 512 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.7559 | 0.61 | 1500 | 0.7307 | 0.7733 | 0.7411 | 0.7733 | 0.7286 | | 0.6361 | 1.22 | 3000 | 0.6559 | 0.7846 | 0.7699 | 0.7846 | 0.7718 | | 0.5774 | 1.83 | 4500 | 0.6004 | 0.8038 | 0.7919 | 0.8038 | 0.7922 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

ca8fc2e79413f4de2478c9a821ebae36

domenicrosati/deberta-v3-xsmall-finetuned-review_classifier

domenicrosati

deberta-v2

13

3

transformers

0

text-classification

true

false

mit

null

0

['text-classification', 'generated_from_trainer']

true

1,434

false

# deberta-v3-xsmall-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Accuracy: 0.9513 - F1: 0.7458 ## 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: 4.5e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.1518 | 1.0 | 6667 | 0.1575 | 0.9510 | 0.7155 | | 0.1247 | 2.0 | 13334 | 0.1441 | 0.9513 | 0.7458 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

9f378c2a841888a8f9cdd6739a1eed4b

jonatasgrosman/exp_w2v2r_en_xls-r_gender_male-8_female-2_s26

jonatasgrosman

wav2vec2

10

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'en']

false

true

475

false

# exp_w2v2r_en_xls-r_gender_male-8_female-2_s26 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

c4698c80589562fbf3e7a6f56d199f37

Helsinki-NLP/opus-mt-sv-srn

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-sv-srn * source languages: sv * target languages: srn * OPUS readme: [sv-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-srn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-srn/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-srn/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-srn/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.srn | 31.3 | 0.506 |

19f5f6de5943d7c9da731b9b46f6535d

elopezlopez/distilbert-base-uncased_fold_2_ternary_v1

elopezlopez

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,659

false

# distilbert-base-uncased_fold_2_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8941 - F1: 0.7889 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 294 | 0.6025 | 0.7402 | | 0.5688 | 2.0 | 588 | 0.5025 | 0.7943 | | 0.5688 | 3.0 | 882 | 0.6102 | 0.7794 | | 0.2582 | 4.0 | 1176 | 0.8896 | 0.7835 | | 0.2582 | 5.0 | 1470 | 1.0392 | 0.7821 | | 0.1185 | 6.0 | 1764 | 1.0865 | 0.7848 | | 0.0461 | 7.0 | 2058 | 1.2951 | 0.7686 | | 0.0461 | 8.0 | 2352 | 1.3348 | 0.7821 | | 0.0313 | 9.0 | 2646 | 1.4267 | 0.7876 | | 0.0313 | 10.0 | 2940 | 1.4004 | 0.7957 | | 0.0142 | 11.0 | 3234 | 1.5501 | 0.7794 | | 0.0083 | 12.0 | 3528 | 1.5564 | 0.7903 | | 0.0083 | 13.0 | 3822 | 1.5699 | 0.7876 | | 0.0067 | 14.0 | 4116 | 1.7725 | 0.7794 | | 0.0067 | 15.0 | 4410 | 1.7642 | 0.7767 | | 0.0031 | 16.0 | 4704 | 1.7891 | 0.7848 | | 0.0031 | 17.0 | 4998 | 1.8528 | 0.7740 | | 0.0054 | 18.0 | 5292 | 1.8378 | 0.7781 | | 0.003 | 19.0 | 5586 | 1.8223 | 0.7862 | | 0.003 | 20.0 | 5880 | 1.7935 | 0.7930 | | 0.0021 | 21.0 | 6174 | 1.9117 | 0.7808 | | 0.0021 | 22.0 | 6468 | 1.8891 | 0.7930 | | 0.0015 | 23.0 | 6762 | 1.9167 | 0.7916 | | 0.0006 | 24.0 | 7056 | 1.9193 | 0.7862 | | 0.0006 | 25.0 | 7350 | 1.8941 | 0.7889 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

07f362411da809aea91c0713333ef692

MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary

MoritzLaurer

deberta-v2

8

1,998

transformers

2

zero-shot-classification

true

false

mit

['en']

['multi_nli', 'anli', 'fever', 'lingnli']

null

0

['text-classification', 'zero-shot-classification']

false

true

4,522

false

# DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary ## Model description This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli). Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. The base model is [DeBERTa-v3-xsmall from Microsoft](https://huggingface.co/microsoft/deberta-v3-xsmall). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see the [DeBERTa-V3 paper](https://arxiv.org/abs/2111.09543). For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli. ## Intended uses & limitations #### How to use the model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." hypothesis = "The movie was good." input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "not_entailment"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://github.com/facebookresearch/anli). ### Training procedure DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=5, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=32, # batch size per device during training per_device_eval_batch_size=32, # batch size for evaluation warmup_ratio=0.1, # number of warmup steps for learning rate scheduler weight_decay=0.06, # strength of weight decay fp16=True # mixed precision training ) ``` ### Eval results The model was evaluated using the binary test sets for MultiNLI, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy. dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c --------|---------|----------|---------|----------|----------|------ accuracy | 0.925 | 0.922 | 0.892 | 0.676 | 0.665 | 0.888 speed (text/sec, CPU, 128 batch) | 6.0 | 6.3 | 3.0 | 5.8 | 5.0 | 7.6 speed (text/sec, GPU Tesla P100, 128 batch) | 473 | 487 | 230 | 390 | 340 | 586 ## Limitations and bias Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ### Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ### Debugging and issues Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.

3d184cdae0cef5b8f4d419f0bac3643d

tdc/hERG_Karim_Morgan

tdc

null

4

0

tdc

0

null

false

bsd-2-clause

['en']

null

1

0

1

0

['biology', 'chemistry']

false

true

1,305

false

## Dataset description An integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures labelled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature. ## Task description Binary classification. Given a drug SMILES string, predict whether it blocks (1, <10uM) or not blocks (0, >=10uM). ## Dataset statistics Total: 13445; Train_val: 12620; Test: 825 ## Dataset split: Random split on 70% training, 10% validation, and 20% testing To load the dataset in TDC, type ```python from tdc.single_pred import Tox data = Tox(name = 'herg_karim') ``` ## Model description Morgan chemical fingerprint with an MLP decoder. Model is tuned with 100 runs using Ax platform. To load the pre-trained model, type ```python from tdc import tdc_hf_interface tdc_hf_herg = tdc_hf_interface("hERG_Karim_Morgan") # load deeppurpose model from this repo dp_model = tdc_hf_herg.load_deeppurpose('./data') dp_model.predict('YOUR SMILES STRING') ``` ## References: [1] Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13, 60 (2021). https://doi.org/10.1186/s13321-021-00541-z

985e9022eb34ef3e45220600e690abfc

calcworks/distilbert-base-uncased-distilled-clinc

calcworks

distilbert

10

2

transformers

0

text-classification

true

false

apache-2.0

null

['clinc_oos']

null

1

0

['generated_from_trainer']

true

1,787

false

# distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1004 - Accuracy: 0.9410 ## 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: 48 - eval_batch_size: 48 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9037 | 1.0 | 318 | 0.5745 | 0.7326 | | 0.4486 | 2.0 | 636 | 0.2866 | 0.8819 | | 0.2537 | 3.0 | 954 | 0.1794 | 0.9210 | | 0.1762 | 4.0 | 1272 | 0.1387 | 0.9294 | | 0.1419 | 5.0 | 1590 | 0.1210 | 0.9358 | | 0.1247 | 6.0 | 1908 | 0.1119 | 0.9413 | | 0.1138 | 7.0 | 2226 | 0.1067 | 0.9387 | | 0.1078 | 8.0 | 2544 | 0.1026 | 0.9423 | | 0.1043 | 9.0 | 2862 | 0.1010 | 0.9413 | | 0.102 | 10.0 | 3180 | 0.1004 | 0.9410 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

539f96e578cf7b2006370f2edb134ad1

cwinkler/distilbert-base-uncased-finetuned-greenplastics-2

cwinkler

distilbert

12

8

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,349

false

# distilbert-base-uncased-finetuned-greenplastics-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0162 - Accuracy: 0.9958 - F1: 0.9958 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0289 | 1.0 | 123 | 0.0238 | 0.9949 | 0.9949 | | 0.0112 | 2.0 | 246 | 0.0162 | 0.9958 | 0.9958 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

5fe3e00473531e178685425d64b3a96b

henilp105/wav2vec2-large-xls-r-300m-telugu-asr

henilp105

wav2vec2

25

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,117

false

# wav2vec2-large-xls-r-300m-telugu-asr This model is a fine-tuned version of [henilp105/wav2vec2-large-xls-r-300m-telugu-asr](https://huggingface.co/henilp105/wav2vec2-large-xls-r-300m-telugu-asr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1050 - Wer: 0.6656 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.0506 | 2.3 | 200 | 0.8841 | 0.7564 | | 0.6354 | 4.59 | 400 | 0.7448 | 0.6912 | | 0.3934 | 6.89 | 600 | 0.8321 | 0.6929 | | 0.2652 | 9.19 | 800 | 0.9529 | 0.6984 | | 0.2022 | 11.49 | 1000 | 0.9490 | 0.6979 | | 0.1514 | 13.79 | 1200 | 1.0025 | 0.6869 | | 0.124 | 16.09 | 1400 | 1.0367 | 0.6799 | | 0.1007 | 18.39 | 1600 | 1.0658 | 0.6734 | | 0.0875 | 20.69 | 1800 | 1.0758 | 0.6779 | | 0.0838 | 22.98 | 2000 | 1.0999 | 0.6701 | | 0.0745 | 25.29 | 2200 | 1.1020 | 0.6708 | | 0.0641 | 27.58 | 2400 | 1.1140 | 0.6683 | | 0.0607 | 29.88 | 2600 | 1.1050 | 0.6656 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

eb50cc91bf49232b36e087dcf86ce8b3

SweetLuna/Kenshi

SweetLuna

null

12

0

diffusers

83

text-to-image

false

creativeml-openrail-m

['en']

null

0

4

0

4

['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers']

false

true

13,971

false

# <h1 style="font-size: 4em; text-align: center; color:black; font-family: Segoe UI"> <a href="https://huggingface.co/SweetLuna/Kenshi/blob/main/README.md" style="text-decoration: none; background-color: transparent;">Kenshi</a> </h1> <a href="https://lensdump.com/i/RL8CTQ"><img src="https://i1.lensdump.com/i/RXYEm2.png" alt="RXYEm2.png" onclick="window.open('https://i1.lensdump.com/i/RXYEm2.png', '_blank')"></a> <h4 style="font-size: 1em; text-align: center;"><p style="color: black;">“Do I hide or do I roam? That indecision… Now the world has changed and I’ve missed it all.”</p></h1> --- ### <h1 style="font-size: 1.75em; font-family: Segoe UI">[FULLSCREEN](https://huggingface.co/SweetLuna/Kenshi/blob/main/README.md) | [Demo (Discord Server)](https://discord.gg/pD9MKyBgNp)</h1> <hr> ### <h1 style="font-size: 1.75em; font-family: Segoe UI">[CivitAI](https://civitai.com/models/3850) | [Download](https://huggingface.co/SweetLuna/Kenshi/tree/main/KENSHI%2001) | [Changelog](https://huggingface.co/SweetLuna/Kenshi/blob/main/Changelog.md)</h1> <hr> <style>▼-preamble { font-size: 2em; }</style> <details id="#contents"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🧧 Contents</strong></summary> <hr> # <h1 style="font-size: 1.5em;"><strong> - [🏮 Preamble](#▼-preamble)<p> - [⚙️ Usage](#▼-usage)<p> - [🎨 Versatility](#▼-versatility)<p> - [🥢 VAE [ IMPORTANT ! ]](#▼-vae)<p> - [🏔️ Examples Images ](#▼-sample) - [The Celestial ☄️](#▼-celestial) - [ChatGPT Prompt ⚙️](#▼-chatgpt) - [Vivid 🌈](#▼-vivid) - [Moon 🌙](#▼-moon)<p> - [🌏 Demo](#▼-demo)<p> - [🍣 Merge Recipes](#▼-merge)<p> - [💡 Suggestions](#▼-suggestions) - [Trigger Words](#trigger-words) - [WebUI](#webui) - [VAE](#vae) - [Embeddings](#embeddings)<p> - [💛 Donate](#▼-donation)<p> - [License](#license)<p> - [Disclaimer](#disclaimer) </strong> </h1> </details> <hr> <details id="▼-preamble"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏮 What is Kenshi?</strong></summary> <hr> <h1> **Kenshi** is my personal merges which created by combining different models together. ***This includes models such as Nixeu, WLOP, Guweiz, BoChen, and many others.*** ```TypeScript My goal is to archive my own feelings towards styles I want for Semi-realistic artstyle. Through this process, I hope not only to gain a deeper understanding of my own preferences, but also to inform and refine the capabilities of my personal skills, and AI Art as it generates artwork that reflects my desired style. ``` Kenshi because it represents strength, resilience, and the ability to adapt and overcome challenges. Just like AI. </h1> </details> <hr> <details id="▼-usage"> <summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>⚙️ Usage</strong></summary> <hr> <h1> ## <h1 style="font-size: 1.5em; text-align: center; color:black; font-family: Segoe UI"> These are the settings I always use it is recommended but not essential; | Settings | Value | | ----------------- | ------------------------------------------------------------------ | | Steps | 20+ | | Sampler | DPM++ 2M Karras | | CFG scale | 2-7 | | Size |600x800 | | Clip skip | 2 | | ENSD | 31337 | | Hires Fix | Enabled | | Upscale by | 1.5 | | Upscaler Fix | https://de-next.owncube.com/index.php/s/x99pKzS7TNaErrC | | Hires Fix | Enabled | Kenshi is not recommended for new users since it requires a lot of prompt to work with I suggest using this if you still want to use the model (install it as an extension on Automatic1111 WebUI) : https://github.com/DominikDoom/a1111-sd-webui-tagcomplete </h1> </h1> <center><a href="https://i2.lensdump.com/i/TAbhx1.png"><img src="https://i2.lensdump.com/i/TAbhx1.png" alt="TAbhx1.png" onclick="window.open('https://i2.lensdump.com/i/TAbhx1.png', '_blank')"></a></center> </details> <hr> <details id="▼-versatility"> <summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>🎨 Versatility</strong></summary> <hr> <h1> ## Unlike most models, Kenshi is known for its versatility, able to perform various styles with remarkable results. I've undergone testing with over 30 to 50 styles and most of the time I get remarkable results. I recommend using Lora and Embedding to improve this even further. <center><a href="https://i2.lensdump.com/i/TAxjOD.png"><img src="https://i2.lensdump.com/i/TAxjOD.png" alt="TAxjOD.png" onclick="window.open('https://i2.lensdump.com/i/TAxjOD.png, '_blank')"></a></center> </details> <hr> <details id="▼-vae"> <summary style="font-size: 2.25em; font-family: font-family: Segoe UI"><strong>🥢 VAE ⚠️</strong></summary> <hr> <h1> ## I recommend <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**kl-f8-anime2.ckpt**</a> VAE from waifu-diffusion-v1-4 <a href="https://huggingface.co/hakurei">which is made by hakurei.</a> </h1> <a href="https://i2.lensdump.com/i/RbBe37.png"><img src="https://i2.lensdump.com/i/RbBe37.png" alt="RbBe37.png" onclick="window.open('https://i2.lensdump.com/i/RbBe37.png', '_blank')"></a> # <h1 style="font-size: 2.5em;"><a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**VAE is important, please download it.**</h1></a> </details> <hr> <details id="▼-sample"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏔️ Examples Images</strong></summary><hr> <details id="▼-celestial"> <summary style="font-size: 1.75em; font-family: monospace"><strong>The Celestial ☄️</strong></summary> <img src="https://i3.lensdump.com/i/RLEz8M.png" alt=”1”> <h1> ```c# 1girl, highly detailed face, bleak and dangerous atmosphere, moody, (dynamic pose:1.6), cataclysmic magic, dark blue wavy long hair, (glowing eyes:0.85), (reaching through a magic circle:1.35), extremely detailed 8k wallpaper, (highly detailed:1.1), [anime:Impasto:0.5], intricate, fantasy, clear sky, wind, beautiful sky, (nightsky), (galaxy), (huge blood moon in the background:1.05) ``` # **KENSHI 00** </details> <hr> <details id="▼-chatgpt"> <summary style="font-size: 1.75em; font-family: monospace"><strong>ChatGPT Prompt ⚙️</strong></summary> <img src="https://i.lensdump.com/i/RLkz3v.png" alt=”2”> <img src="https://i1.lensdump.com/i/RLkFND.png" alt=”3”> <img src="https://i3.lensdump.com/i/RLkulr.png" alt=”4”> ```c# (A cursed knight, clad in black armor,) must journey through a desolate, haunted land to reach the Elden Ring and lift the (curse that plagues their soul.)Along the way, they encounter other travelers, (each struggling with their own demons and secrets), As they draw closer to the Elden Ring, they are confronted with visions of their past mistakes, (all tinged with a red hue,) looking at viewer, highres, superb, 8k wallpaper, extremely detailed, intricate, unreal engine 5, volumetric lighting, realistic, realistic lighting, cinematic, 4k, cinematic lighting, 8k, depth of field, 3d, perfect, award-winning, hyper-detailed, photorealistic, ultra realistic, realistic light, hard lighting, intricate details, stop motion, hyperfocus, tonemapping, sharp focus, hyper detailed, detailed eyes, eyes focus, (illustration:1.1), highres, (extremely detailed CG unity 8k wallpaper:1.1), (beautiful face:1.15), (cowboy_shot:1.5) (nixeu_soft:0.7), (nixeu_white:0.7), ``` # **KENSHI 00** </details> <hr> <details id="▼-vivid"> <summary style="font-size: 1.75em; font-family: monospace"><strong>Vivid 🌈</strong></summary> <img src="https://i.lensdump.com/i/RXY1Fo.png" alt=”5”> ```c# close POV, young adult woman, blue purple green color palette, black hair with dark green shine, two symmetrical antennae on head, big blue eyes sparkling, rings around eyes, two-tone black and red, smiling at the camera, elegant pose, looking at the viewer, vivid stained glass window background, oil painting, character portrait, drawn in medibang paint, 4k wallpaper, aesthetic, masterpiece, award-winning photography, macro photography vivid colors, photorealistic, atmospheric, cinematic, moody, rule of thirds, majestic, detailed, perfect anatomy cowboy shot, contrapposto, looking at viewer, highres, superb, 8k wallpaper, extremely detailed, intricate, unreal engine 5, volumetric lighting, realistic, realistic lighting, cinematic, 4k, cinematic lighting, 8k, depth of field, 3d, masterpiece, perfect, award-winning, hyper-detailed, photorealistic, ultra realistic, realistic light, hard lighting, intricate details, stop motion, hyperfocus, tonemapping, sharp focus, hyper detailed, detailed eyes, eyes focus, (illustration:1.1), highres, (extremely detailed CG unity 8k wallpaper:1.1), (mid shot1.25), (portrait:1.25), (solo:1.2), 1girl, (beautiful face:1.15), (nixeu_soft:0.7), (nixeu_white:0.7), ``` # **KENSHI 01** </details> <hr> <details id="▼-moon"> <summary style="font-size: 1.75em; font-family: monospace"><strong>Moon 🌙</strong></summary> <img src="https://i2.lensdump.com/i/RXYt7i.png" alt=”6”> ```c# (on the moon, space, looking back into earth), white hair, black tank top, volumetric lighting, white jacket, glowing headphone, cyberpunk, futuristic, multi-color eyes, detailed eyes, hyper detailed,light smile, highly detailed, beautiful, small details, ultra detailed, best quality, intricate, hyperrealism, sharp, digital illustration, detailed, realism, intricate, 4k, 8k, trending on artstation, good anatomy, beautiful lighting, award-winning, photorealistic, realistic shadows, realistic lighting, beautiful lighting, raytracing, intricate details, moody, rule of thirds, masterpiece, (illustration:1.1), highres, (extremely detailed CG, unity, 8k wallpaper:1.1), beautiful face, highly detailed face, ultra realistic, masterpiece, bokeh, extremely detailed, intricate, zoomout, colorful, vibrant colors, red nail polish, side view, ``` # **KENSHI 01** </details> </details> <hr> </h1> <details id="▼-demo"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🌏 Demo</strong></summary> <hr> ### <h1 style="font-size: 2em;">Test out Kenshi on <a href="https://discord.gg/pD9MKyBgNp">Discord</a> in #garden_1-kenshi server</h1> <a href="https://discord.gg/pD9MKyBgNp"><img src="https://i.lensdump.com/i/RwAkqx.png" alt="RwAkqx.png" border="0" /></a> </details> <hr> <details id="▼-merge"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🍣 Merge Recipes</strong></summary> <hr> <h1><strong> <a href=" https://www.figma.com/file/aESyZAxHxBJjE63gog5ExZ/KENSHI?node-id=0%3A1&t=2ULQWeLUSIWhk1aE-0" class="no-underline" style="font-size: 1.75em;">Here is my Cookbook that you can check out. <img src="https://i2.lensdump.com/i/RLCJIH.png" alt="COOKBOOK"></strong> </h1> </a> </details> <hr> <details id="▼-donation"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>💛 Donate</strong></summary> <hr> <h1><strong> I've been working hard to complete my college education. The thing is, paying for college is no joke and I've been feeling the pressure of the mounting bills. I know times are tough for everyone, but if you're able to help in any way, I would be forever grateful. Considering supporting me on <a href="https://www.patreon.com/thesweetluna">Patreon</a> </h1> </a> </details> <hr> <details id="▼-suggestions"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>💡 Suggestions</strong></summary> <hr> ## <h1 style="font-size: 1.75em;">Trigger Words</h1> <hr> <h1 style="font-size: 1.5em;"> **Trigger Words are not required** but are meant to **enhance the effectiveness of the prompt** and improve the overall outcome. ```c# WLOP, Nixeu, Guweiz ``` </h1> <hr> ## <h1 style="font-size: 1.75em;">WebUI</h1> <hr> <h1 style="font-size: 1.5em;"> <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">AUTOMATIC1111</a> Grab it, a must-have. Have all the features you want and is easy to access. <hr> </h1> ## <h1 style="font-size: 1.75em;">Embeddings</h1> <hr> <h1 style="font-size: 1.5em;"> I recommend grabbing ***all*** <a href="https://huggingface.co/Nerfgun3">Nerfgun3</a> embeddings ***and*** Sirveggie <a href="https://huggingface.co/SirVeggie/nixeu_embeddings">nixeu_embeddings</a> </h1> </details> <hr> # License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: ``` 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against theprovisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) ``` [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) <hr> # Disclaimer ```c# The use of this learning model is entirely at the discretion of the user, and they have the freedom to choose whether or not to create NSFW content. This is important to note that the model itself does not contain any explicit or inappropriate imagery that can be easily accessed with a single click. The purpose of sharing this model is not to showcase obscene material in a public forum, but rather to provide a tool for users to utilize as they see fit. The decision of whether to engage with SFW or NSFW content lies with the user and their own personal preferences. ```

1ed83da1a80c8ee1b2672af0117184f0

gokceuludogan/ChemBERTaLM

gokceuludogan

roberta

12

3

transformers

0

text-generation

true

false

mit

null

0

['molecule-generation', 'cheminformatics', 'biochemical-language-models']

false

true

1,563

false

## ChemBERTaLM A molecule generator model finetuned from [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for targeted drug design", which has been accepted for publication in *Bioinformatics* Published by Oxford University Press and first released in [this repository](https://github.com/boun-tabi/biochemical-lms-for-drug-design). ChemBERTaLM is a RoBERTa model initialized with [ChemBERTa](https://huggingface.co/seyonec/PubChem10M_SMILES_BPE_450k) checkpoint, and then, finetuned on the MOSES dataset which comprises a collection of drug-like compounds. ## How to use ```python from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM") model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM") generator = pipeline("text-generation", model=model, tokenizer=tokenizer) generator("", max_length=128, do_sample=True) # Sample output [{'generated_text': 'Cc1ccc(C(=O)N2CCN(C(=O)c3ccc(F)cc3)CC2)cc1'}] ``` ## Citation ```bibtex @article{10.1093/bioinformatics/btac482, author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan}, title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}", journal = {Bioinformatics}, year = {2022}, doi = {10.1093/bioinformatics/btac482}, url = {https://doi.org/10.1093/bioinformatics/btac482} } ```

de4051d0cb11fa78e05c5bc7dedf1b69

jbetker/tortoise-tts-finetuned-lj

jbetker

null

9

0

null

1

null

false

apache-2.0

null

0

2

1

[]

false

true

532

false

This repository holds the finetuned weights for Tortoise v2 for the LJSpeech voice. It is a good demonstration of how powerful fine-tuning Tortoise can be. Usage: - Clone Tortoise, jbetker/tortoise-tts-v2 or https://github.com/neonbjb/tortoise-tts - Clone this repo to download weights - Run any Tortoise script with the flag `--model_dir=<path_to_where_you_cloned_this_repo>/models` and `--voice=lj` - For fine-tuned models, I recommend using the `high_quality` preset. Faster rendering modes can exhibit artifacts in the output.

0765116cfa7351ecf881f9d7aa5222b7

rvidaurre/ddpm-butterflies-128

rvidaurre

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,231

false

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

eaae0d5c7c55a754815ecffdda5d1d07

haanba/hayashida-tamaki-gfkari-concept

haanba

null

28

0

null

0

text-to-image

false

mit

['en']

null

0

['stable-diffusion', 'text-to-image']

false

true

5,476

false

# Hayashida Tamaki (GF Kari) on Waifu Diffusion v1.3.5 This is the `<wd135-hayashida-tamaki-gfkari>` concept taught to [Waifu Diffusion v1.3.5](https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/models/wd-1-3-5_80000-fp32.ckpt) via Textual Inversion. ## Credits The model card follows the format commonly used by concepts stored at [Hugging Face SD Concepts Library](https://huggingface.co/sd-concepts-library). The training images were taken from [GF Kari Database](https://gfkari.gamedbs.jp/). ## Concept Images Here is the new concept you will be able to use as an `object`: ![<wd135-hayashida-tamaki-gfkari> 0](./concept_images/4122afac10afadfe2b8fe5d6f89630dc_512x512.png) ![<wd135-hayashida-tamaki-gfkari> 1](./concept_images/4437ab2e2a07e190e361ae72e2d96fb7_512x512.png) ![<wd135-hayashida-tamaki-gfkari> 2](./concept_images/5493041b67f2d5d67c476b541dc8663d_512x512.png) ![<wd135-hayashida-tamaki-gfkari> 3](./concept_images/75d1cf668cd02a5223f68312a0657167_512x512.png) ![<wd135-hayashida-tamaki-gfkari> 4](./concept_images/profile_3_512x512.png) ## Output Examples !["best quality masterpiece, <wd135-hayashida-tamaki-gfkari> collarbone bare shoulders bare legs shiny skin standing, frilled white summer dress, ocean beach sunny day sunlight, cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn] " -s 64 -S 4020064356 -W 512 -H 768 -C 12 -A k_dpmpp_2](./examples/000049.d1659a74.4020064356.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.5", "image": { "prompt": [ { "prompt": "best quality masterpiece, <wd135-hayashida-tamaki-gfkari> collarbone bare shoulders bare legs shiny skin standing, frilled white summer dress, ocean beach sunny day sunlight, cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn] ", "weight": 1 } ], "steps": 64, "cfg_scale": 12, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 4020064356, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` !["best quality masterpiece, <wd135-hayashida-tamaki-gfkari> collarbone bare shoulders bare legs shiny skin standing, frilled white summer dress, ocean beach sunny day sunlight, cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn] " -s 64 -S 4020064356 -W 512 -H 768 -C 12 -A k_dpmpp_2_a](./examples/000050.e968c45d.4020064356.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.5", "image": { "prompt": [ { "prompt": "best quality masterpiece, <wd135-hayashida-tamaki-gfkari> collarbone bare shoulders bare legs shiny skin standing, frilled white summer dress, ocean beach sunny day sunlight, cowboy shot, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn] ", "weight": 1 } ], "steps": 64, "cfg_scale": 12, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 4020064356, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2_a", "variations": [] } } ``` !["best quality masterpiece, <wd135-hayashida-tamaki-gfkari>, school uniform collared shirt pleated skirt, lying on back on bed, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]" -s 64 -S 3329130038 -W 512 -H 768 -C 12 -A k_dpmpp_2](./examples/000061.00d75711.3329130038.png) ```json { "model": "stable diffusion", "model_weights": "waifu-diffusion-1.3.5", "model_hash": "b438efac4434af4e482d20cdfcea64067f8dfec438628261d2f2aa60ffc41452", "app_id": "invoke-ai/InvokeAI", "app_version": "2.2.5", "image": { "prompt": [ { "prompt": "best quality masterpiece, <wd135-hayashida-tamaki-gfkari>, school uniform collared shirt pleated skirt, lying on back on bed, [bad anatomy, bad hands, bad perspective, bad proportions, blurry, censored, cropped, error, extra arms, extra ears, fewer digits, jpeg artifacts, lowres, multiple legs, out of frame, poorly drawn]", "weight": 1 } ], "steps": 64, "cfg_scale": 12, "threshold": 0, "perlin": 0, "height": 768, "width": 512, "seed": 3329130038, "seamless": false, "hires_fix": false, "type": "txt2img", "postprocessing": null, "sampler": "k_dpmpp_2", "variations": [] } } ``` ## License [MIT](./LICENSE).

08e2ec20d5dae380d86554a95a8f7b62

muhtasham/small-vanilla-target-tweet

muhtasham

bert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,563

false

# small-vanilla-target-tweet This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.8718 - Accuracy: 0.7540 - F1: 0.7525 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5858 | 4.9 | 500 | 0.8189 | 0.7380 | 0.7364 | | 0.1039 | 9.8 | 1000 | 1.1965 | 0.7594 | 0.7568 | | 0.0264 | 14.71 | 1500 | 1.5387 | 0.7433 | 0.7460 | | 0.0142 | 19.61 | 2000 | 1.6758 | 0.7620 | 0.7551 | | 0.0113 | 24.51 | 2500 | 1.8718 | 0.7540 | 0.7525 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2

72144fe53d53dc5340f2cd1656d5114e

shivam/wav2vec2-xls-r-300m-hindi

shivam

wav2vec2

30

2

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['hi']

['common_voice']

null

0

['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer']

true

3,085

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 1.4031 - Wer: 0.6827 ## 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: 7.5e-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_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.3156 | 3.4 | 500 | 4.5583 | 1.0 | | 3.3329 | 6.8 | 1000 | 3.4274 | 1.0001 | | 2.1275 | 10.2 | 1500 | 1.7221 | 0.8763 | | 1.5737 | 13.6 | 2000 | 1.4188 | 0.8143 | | 1.3835 | 17.01 | 2500 | 1.2251 | 0.7447 | | 1.3247 | 20.41 | 3000 | 1.2827 | 0.7394 | | 1.231 | 23.81 | 3500 | 1.2216 | 0.7074 | | 1.1819 | 27.21 | 4000 | 1.2210 | 0.6863 | | 1.1546 | 30.61 | 4500 | 1.3233 | 0.7308 | | 1.0902 | 34.01 | 5000 | 1.3251 | 0.7010 | | 1.0749 | 37.41 | 5500 | 1.3274 | 0.7235 | | 1.0412 | 40.81 | 6000 | 1.2942 | 0.6856 | | 1.0064 | 44.22 | 6500 | 1.2581 | 0.6732 | | 1.0006 | 47.62 | 7000 | 1.2767 | 0.6885 | | 0.9518 | 51.02 | 7500 | 1.2966 | 0.6925 | | 0.9514 | 54.42 | 8000 | 1.2981 | 0.7067 | | 0.9241 | 57.82 | 8500 | 1.3835 | 0.7124 | | 0.9059 | 61.22 | 9000 | 1.3318 | 0.7083 | | 0.8906 | 64.62 | 9500 | 1.3640 | 0.6962 | | 0.8468 | 68.03 | 10000 | 1.4727 | 0.6982 | | 0.8631 | 71.43 | 10500 | 1.3401 | 0.6809 | | 0.8154 | 74.83 | 11000 | 1.4124 | 0.6955 | | 0.7953 | 78.23 | 11500 | 1.4245 | 0.6950 | | 0.818 | 81.63 | 12000 | 1.3944 | 0.6995 | | 0.7772 | 85.03 | 12500 | 1.3735 | 0.6785 | | 0.7857 | 88.43 | 13000 | 1.3696 | 0.6808 | | 0.7705 | 91.84 | 13500 | 1.4101 | 0.6870 | | 0.7537 | 95.24 | 14000 | 1.4178 | 0.6832 | | 0.7734 | 98.64 | 14500 | 1.4027 | 0.6831 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu113 - Datasets 1.18.1.dev0 - Tokenizers 0.11.0

435b2bc389d70f1c34c55de1d4e98a64

MultiBertGunjanPatrick/multiberts-seed-1-300k

MultiBertGunjanPatrick

bert

7

2

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-1']

false

true

6,483

false

# MultiBERTs Seed 1 Checkpoint 300k (uncased) Seed 1 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-1](https://hf.co/multberts-seed-1). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-300k') model = BertModel.from_pretrained("multiberts-seed-1-300k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

463dae68b1b4449f51effbeca90b180f

jha2ee/riffusion-model-db

jha2ee

null

19

8

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image', 'stable-diffusion']

false

true

426

false

### riffusion_model-db Dreambooth model trained by jha2ee 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:

bc030fd29afc646a8f2026653e30f82c

DOOGLAK/Article_100v0_NER_Model_3Epochs_UNAUGMENTED

DOOGLAK

bert

13

6

transformers

0

token-classification

true

false

apache-2.0

null

['article100v0_wikigold_split']

null

0

['generated_from_trainer']

true

1,559

false

# Article_100v0_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article100v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6037 - Precision: 0.25 - Recall: 0.0003 - F1: 0.0005 - Accuracy: 0.7772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 12 | 0.7472 | 0.0 | 0.0 | 0.0 | 0.7772 | | No log | 2.0 | 24 | 0.6443 | 0.0 | 0.0 | 0.0 | 0.7772 | | No log | 3.0 | 36 | 0.6037 | 0.25 | 0.0003 | 0.0005 | 0.7772 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

ed350286dfb119b39e60edfa346106cb

ayameRushia/indobert-base-uncased-finetuned-indonlu-smsa

ayameRushia

bert

10

5

transformers

0

text-classification

true

false

mit

null

['indonlu']

null

0

['generated_from_trainer']

true

2,252

false

# indobert-base-uncased-finetuned-indonlu-smsa This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.2277 - Accuracy: 0.9302 - F1: 0.9066 - Precision: 0.8992 - Recall: 0.9147 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 344 | 0.3831 | 0.8476 | 0.7715 | 0.7817 | 0.7627 | | 0.4167 | 2.0 | 688 | 0.2809 | 0.8905 | 0.8406 | 0.8699 | 0.8185 | | 0.2624 | 3.0 | 1032 | 0.2254 | 0.9230 | 0.8842 | 0.9004 | 0.8714 | | 0.2624 | 4.0 | 1376 | 0.2378 | 0.9238 | 0.8797 | 0.9180 | 0.8594 | | 0.1865 | 5.0 | 1720 | 0.2277 | 0.9302 | 0.9066 | 0.8992 | 0.9147 | | 0.1217 | 6.0 | 2064 | 0.2444 | 0.9262 | 0.8981 | 0.9013 | 0.8957 | | 0.1217 | 7.0 | 2408 | 0.2985 | 0.9286 | 0.8999 | 0.9035 | 0.8971 | | 0.0847 | 8.0 | 2752 | 0.3397 | 0.9278 | 0.8969 | 0.9090 | 0.8871 | | 0.0551 | 9.0 | 3096 | 0.3542 | 0.9270 | 0.8961 | 0.9010 | 0.8924 | | 0.0551 | 10.0 | 3440 | 0.3862 | 0.9222 | 0.8895 | 0.8970 | 0.8846 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

dbe97a9efa32c5385db200a01b37a57a

Nadav/bert-base-historic-multilingual-cased-squad-en

Nadav

bert

10

7

transformers

0

question-answering

true

false

mit

null

0

['generated_from_trainer']

true

1,307

false

# bert-base-historic-multilingual-cased-squad-en This model is a fine-tuned version of [dbmdz/bert-base-historic-multilingual-cased](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5307 ## 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: 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: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.881 | 1.0 | 4820 | 1.5507 | | 1.5883 | 2.0 | 9640 | 1.5307 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

29211edcee141ad5835c41c7f8f4678f

juancopi81/whisper-medium-es-train-valid-bs-64

juancopi81

whisper

34

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,326

false

# Whisper Small Spanish This model is a fine-tuned version of [juancopi81/whisper-medium-es](https://huggingface.co/juancopi81/whisper-medium-es) on the mozilla-foundation/common_voice_11_0 es dataset. It achieves the following results on the evaluation set: - Loss: 0.2338 - Wer: 95.6181 ## 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: 64 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1432 | 1.0 | 100 | 0.2338 | 95.6181 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

2b8a55001bbbd38686130a2f57994fc3

HoussemSaafi/esm2_t12_35M_UR50D-finetuned-ARG-classification

HoussemSaafi

esm

7

3

transformers

0

text-classification

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,038

false

# esm2_t12_35M_UR50D-finetuned-ARG-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

024c8e8c7e255973e3221130a8685f8b

sd-concepts-library/a-tale-of-two-empires

sd-concepts-library

null

11

0

null

1

null

false

mit

null

0

1

0

[]

false

true

1,458

false

### A Tale of Two Empires on Stable Diffusion This is the `<two-empires>` 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 a `style`: ![<two-empires> 0](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/2.jpeg) ![<two-empires> 1](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/3.jpeg) ![<two-empires> 2](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/1.jpeg) ![<two-empires> 3](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/5.jpeg) ![<two-empires> 4](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/4.jpeg) ![<two-empires> 5](https://huggingface.co/sd-concepts-library/a-tale-of-two-empires/resolve/main/concept_images/0.jpeg) Source: Reddit [u/mandal0re](https://www.reddit.com/r/StarWars/comments/kg6ovv/i_like_to_photoshop_old_paintings_heres_my_a_tale/)

e1b6907b52cea99281168020019400e7

iksenburg/andiface

iksenburg

null

38

30

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

2,532

false

### AndiFace Dreambooth model trained by iksenburg with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: iksen (use that on your prompt) ![iksen 0](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%281%29.jpg)![iksen 1](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%282%29.jpg)![iksen 2](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%283%29.jpg)![iksen 3](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%284%29.jpg)![iksen 4](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%285%29.jpg)![iksen 5](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%286%29.jpg)![iksen 6](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%287%29.jpg)![iksen 7](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%288%29.jpg)![iksen 8](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%289%29.jpg)![iksen 9](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2810%29.jpg)![iksen 10](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2811%29.jpg)![iksen 11](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2812%29.jpg)![iksen 12](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2813%29.jpg)![iksen 13](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2814%29.jpg)![iksen 14](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2815%29.jpg)![iksen 15](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2816%29.jpg)![iksen 16](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2817%29.jpg)![iksen 17](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2818%29.jpg)![iksen 18](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2819%29.jpg)![iksen 19](https://huggingface.co/iksenburg/andiface/resolve/main/concept_images/iksen_%2820%29.jpg)

ef2f4a6c444e392ea9cb95ed9549025d

rheyaas/distilbert-base-uncased-finetuned-squad

rheyaas

distilbert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,284

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1576 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2167 | 1.0 | 5533 | 1.1654 | | 0.9559 | 2.0 | 11066 | 1.1209 | | 0.7532 | 3.0 | 16599 | 1.1576 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

aae41b29999d85c2583d0e0f71890fdc

sd-concepts-library/morino-hon-style

sd-concepts-library

null

27

0

null

13

null

false

mit

null

0

[]

false

true

3,134

false

### Morino hon Style on Stable Diffusion This is the `<morino-hon>` 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 a `style`: ![<morino-hon> 0](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/10.jpeg) ![<morino-hon> 1](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/13.jpeg) ![<morino-hon> 2](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/9.jpeg) ![<morino-hon> 3](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/16.jpeg) ![<morino-hon> 4](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/21.jpeg) ![<morino-hon> 5](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/19.jpeg) ![<morino-hon> 6](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/6.jpeg) ![<morino-hon> 7](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/17.jpeg) ![<morino-hon> 8](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/5.jpeg) ![<morino-hon> 9](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/11.jpeg) ![<morino-hon> 10](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/0.jpeg) ![<morino-hon> 11](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/20.jpeg) ![<morino-hon> 12](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/18.jpeg) ![<morino-hon> 13](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/12.jpeg) ![<morino-hon> 14](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/7.jpeg) ![<morino-hon> 15](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/4.jpeg) ![<morino-hon> 16](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/14.jpeg) ![<morino-hon> 17](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/8.jpeg) ![<morino-hon> 18](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/1.jpeg) ![<morino-hon> 19](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/3.jpeg) ![<morino-hon> 20](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/2.jpeg) ![<morino-hon> 21](https://huggingface.co/sd-concepts-library/morino-hon-style/resolve/main/concept_images/15.jpeg)

ff8a54505835508d3cd525ede605b6a9

adityavithaldas/Fashion_Category_Classifier

adityavithaldas

null

2

0

null

4

null

false

cc-by-4.0

null

0

[]

false

true

621

false

This model uses the Deep Fashion dataset in order to create a category classifier among the 50 or so provided categories. https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html This model leverages the ViT (Vision transformer), loaded with the custom dataset and the 50 odd categoes to which they are assigned. The objective here, is to expand the same and get to a. An accuracy level of 90+ in the top 5 categorizes b. An accuracy of 70+ overall. In addition, we would also look forward to creating attribute extractors, to extract key attributes (primary color, checked, sleeve, collar etc) as we proceed

52a4e8c8ac52e03728700b7ed665961b

jonatasgrosman/exp_w2v2t_pt_vp-it_s996

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['pt']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'pt']

false

true

469

false

# exp_w2v2t_pt_vp-it_s996 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

f35f65b4a76f7c2173e9d94bbdd8cf90

AbhirupGhosh/opus-mt-finetuned-en-hi

AbhirupGhosh

marian

10

27

transformers

0

translation

true

false

apache-2.0

['en', 'hi', 'multilingual']

['HindiEnglishCorpora']

null

1

0

1

0

['translation', 'Hindi', 'generated_from_keras_callback']

false

true

857

false

# opus-mt-finetuned-hi-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on [HindiEnglish Corpora](https://www.clarin.eu/resource-families/parallel-corpora) ## Model description The model is a transformer model similar to the [Transformer](https://arxiv.org/abs/1706.03762?context=cs) as defined in Attention Is All You Need by Vaswani et al ## Training and evaluation data More information needed ## Training procedure The model was trained on 2 NVIDIA_TESLA_A100 GPU's on Google's vertex AI platform. ### Training hyperparameters The following hyperparameters were used during training: - optimizer: AdamWeightDecay - training_precision: float32 ### Training results ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

decfee092929bd54e8e731543cd947d3

troesy/distil-added-voca

troesy

distilbert

13

8

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,251

false

# distil-added-voca This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2515 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 174 | 0.2577 | | No log | 2.0 | 348 | 0.2488 | | 0.2546 | 3.0 | 522 | 0.2515 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

36adbcf79e21a60f134aaaa9000296cb

Helsinki-NLP/opus-mt-bzs-sv

Helsinki-NLP

marian

10

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-bzs-sv * source languages: bzs * target languages: sv * OPUS readme: [bzs-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bzs-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/bzs-sv/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.bzs.sv | 30.7 | 0.489 |

f86d0c8d3722effbe6e44921a6e93af6

premsuresh/bart-finetuned-iirc-prem-2

premsuresh

bart

8

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

960

false

# bart-finetuned-iirc-prem-2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - 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: 50 ### Training results ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

fe41a540d4e6d530a70a8c419542a2dd

liaad/srl-pt_bertimbau-base

liaad

bert

9

119

transformers

1

feature-extraction

true

apache-2.0

['multilingual', 'pt']

['PropBank.Br']

null

0

['bert-base-portuguese-cased', 'semantic role labeling', 'finetuned']

false

true

3,585

false

# BERTimbau base fine-tuned on Portuguese semantic role labeling ## Model description This model is the [`neuralmind/bert-base-portuguese-cased`](https://huggingface.co/neuralmind/bert-base-portuguese-cased) fine-tuned on Portuguese semantic role labeling data. This is part of a project from which resulted the following models: * [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Intended uses & limitations #### How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_bertimbau-base") model = AutoModel.from_pretrained("liaad/srl-pt_bertimbau-base") ``` To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Training procedure The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). ## Eval results | Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 | | `srl-pt_mbert-base` | 72.76 | 66.89 | | `srl-en_xlmr-base` | 66.59 | 65.24 | | `srl-en_xlmr-large` | 67.60 | 64.94 | | `srl-en_mbert-base` | 63.07 | 58.56 | | `srl-enpt_xlmr-base` | 76.50 | 73.74 | | `srl-enpt_xlmr-large` | **78.22** | 74.55 | | `srl-enpt_mbert-base` | 74.88 | 69.19 | | `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | | `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | | `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | ### BibTeX entry and citation info ```bibtex @misc{oliveira2021transformers, title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, year={2021}, eprint={2101.01213}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

f2e6e1577919aa1d425ad4fe798f30a0

2NRC/Fake-New-Classifier

2NRC

null

5

0

null

0

null

false

other

null

0

[]

false

true

404

false

Deep Learning for NLP: Training a text classification model to detect fake news articles! Training and test dataset gotten from https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset Dataset size = 44898 articles Training set size = 35918 articles Test set size = 8980 articles Accuracy on the training set = 0.990394788128515 Accuracy on the test set = 0.983184855233853

7fe3d2f3c4ae8d56af94212128d12dfa

Arnaudmkonan/xlm-roberta-base-finetuned-panx-de

Arnaudmkonan

xlm-roberta

12

7

transformers

0

token-classification

true

false

mit

null

['xtreme']

null

0

['generated_from_trainer']

true

1,320

false

# xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1343 - F1: 0.8637 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2578 | 1.0 | 525 | 0.1562 | 0.8273 | | 0.1297 | 2.0 | 1050 | 0.1330 | 0.8474 | | 0.0809 | 3.0 | 1575 | 0.1343 | 0.8637 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

f19c2363cc1eca89485e7f715b21d4f4

Geotrend/distilbert-base-ro-cased

Geotrend

distilbert

6

5

transformers

0

fill-mask

true

false

apache-2.0

['ro']

['wikipedia']

null

0

[]

false

true

1,215

false

# distilbert-base-ro-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-ro-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-ro-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermdistilbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact amine@geotrend.fr for any question, feedback or request.

5091f95edff25cd18111ea9e4e2ffedd

pere/nb-nn-dev

pere

null

70

3

null

0

translation

true

false

true

cc-by-4.0

False

['oscar']

null

0

['translation']

false

true

1,131

false

# Norwegian mT5 - Translation Bokmål Nynorsk - Development ## Description This is the development version of the Bokmål-Nynorsk translator. If you want something that is stable, Please do run [this version](https://huggingface.co/pere/nb-nn-translation/) instead. Here is an example of how to use the model from Python ```python # Import libraries from transformers import T5ForConditionalGeneration, AutoTokenizer model = T5ForConditionalGeneration.from_pretrained('pere/nb-nn-dev',from_flax=True) tokenizer = AutoTokenizer.from_pretrained('pere/nb-nn-dev') #Encode the text text = "Hun vil ikke gi bort sine personlige data." inputs = tokenizer.encode(text, return_tensors="pt") outputs = model.generate(inputs, max_length=255, num_beams=4, early_stopping=True) #Decode and print the result print(tokenizer.decode(outputs[0])) ``` Or if you like to use the pipeline instead ```python # Set up the pipeline from transformers import pipeline translator = pipeline("translation", model='pere/nb-nn-dev') # Do the translation text = "Hun vil ikke gi bort sine personlige data." print(translator(text, max_length=255)) ```

01ee26b1ddb8f6091672cd447d82d501

ksing193/t5-small-finetuned-wikisql

ksing193

t5

12

4

transformers

0

text2text-generation

true

false

apache-2.0

null

['wikisql']

null

0

['generated_from_trainer']

true

1,795

false

# t5-small-finetuned-wikisql This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikisql dataset. It achieves the following results on the evaluation set: - Loss: 0.1245 - Rouge2 Precision: 0.8183 - Rouge2 Recall: 0.7262 - Rouge2 Fmeasure: 0.7624 ## 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: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.1954 | 1.0 | 4049 | 0.1575 | 0.7934 | 0.7033 | 0.7386 | | 0.1643 | 2.0 | 8098 | 0.1374 | 0.8083 | 0.7169 | 0.7529 | | 0.1517 | 3.0 | 12147 | 0.1296 | 0.8135 | 0.7221 | 0.7581 | | 0.1459 | 4.0 | 16196 | 0.1256 | 0.817 | 0.7254 | 0.7614 | | 0.1414 | 5.0 | 20245 | 0.1245 | 0.8183 | 0.7262 | 0.7624 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

bbc2012cadcb565e36d94e74b7f36d5b