sil-ai

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This model is a fine-tuned version of microsoft/speecht5tts on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0585 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - seed: 3407 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: cosine - lrschedulerwarmupsteps: 4000 - trainingsteps: 40000 - mixedprecisiontraining: Native AMP | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:-----:|:---------------:| | 0.1109 | 7.1946 | 1000 | 0.0796 | | 0.0887 | 14.3892 | 2000 | 0.0685 | | 0.0895 | 21.5838 | 3000 | 0.0643 | | 0.0802 | 28.7784 | 4000 | 0.0635 | | 0.0758 | 35.9730 | 5000 | 0.0626 | | 0.0756 | 43.1658 | 6000 | 0.0626 | | 0.0732 | 50.3604 | 7000 | 0.0613 | | 0.0695 | 57.5550 | 8000 | 0.0601 | | 0.0669 | 64.7495 | 9000 | 0.0609 | | 0.0672 | 71.9441 | 10000 | 0.0607 | | 0.0652 | 79.1369 | 11000 | 0.0593 | | 0.067 | 86.3315 | 12000 | 0.0598 | | 0.0657 | 93.5261 | 13000 | 0.0587 | | 0.0641 | 100.7207 | 14000 | 0.0630 | | 0.0631 | 107.9153 | 15000 | 0.0587 | | 0.0604 | 115.1081 | 16000 | 0.0596 | | 0.0621 | 122.3027 | 17000 | 0.0580 | | 0.0571 | 129.4973 | 18000 | 0.0577 | | 0.0591 | 136.6919 | 19000 | 0.0586 | | 0.0591 | 143.8865 | 20000 | 0.0574 | | 0.0568 | 151.0793 | 21000 | 0.0583 | | 0.0548 | 158.2739 | 22000 | 0.0575 | | 0.0598 | 165.4685 | 23000 | 0.0585 | | 0.0556 | 172.6631 | 24000 | 0.0579 | | 0.0555 | 179.8577 | 25000 | 0.0587 | | 0.054 | 187.0505 | 26000 | 0.0590 | | 0.0539 | 194.2450 | 27000 | 0.0584 | | 0.0523 | 201.4396 | 28000 | 0.0584 | | 0.0525 | 208.6342 | 29000 | 0.0582 | | 0.0536 | 215.8288 | 30000 | 0.0580 | | 0.052 | 223.0216 | 31000 | 0.0584 | | 0.0523 | 230.2162 | 32000 | 0.0583 | | 0.0525 | 237.4108 | 33000 | 0.0583 | | 0.0511 | 244.6054 | 34000 | 0.0584 | | 0.0539 | 251.8 | 35000 | 0.0587 | | 0.052 | 258.9946 | 36000 | 0.0590 | | 0.0492 | 266.1874 | 37000 | 0.0587 | | 0.0492 | 273.3820 | 38000 | 0.0590 | | 0.0509 | 280.5766 | 39000 | 0.0587 | | 0.0531 | 287.7712 | 40000 | 0.0585 | - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1

This model is a fine-tuned version of microsoft/speecht5tts on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4800 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - seed: 3407 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 4000 - trainingsteps: 40000 - mixedprecisiontraining: Native AMP | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:-----:|:---------------:| | 0.5387 | 5.2918 | 1000 | 0.5152 | | 0.493 | 10.5836 | 2000 | 0.4955 | | 0.4935 | 15.8753 | 3000 | 0.4885 | | 0.4846 | 21.1645 | 4000 | 0.4863 | | 0.4717 | 26.4562 | 5000 | 0.4825 | | 0.4532 | 31.7480 | 6000 | 0.4804 | | 0.4841 | 37.0371 | 7000 | 0.4802 | | 0.458 | 42.3289 | 8000 | 0.4791 | | 0.4454 | 47.6207 | 9000 | 0.4822 | | 0.4461 | 52.9125 | 10000 | 0.4790 | | 0.4362 | 58.2016 | 11000 | 0.4789 | | 0.4301 | 63.4934 | 12000 | 0.4789 | | 0.43 | 68.7851 | 13000 | 0.4806 | | 0.4392 | 74.0743 | 14000 | 0.4796 | | 0.4355 | 79.3660 | 15000 | 0.4797 | | 0.4273 | 84.6578 | 16000 | 0.4778 | | 0.4324 | 89.9496 | 17000 | 0.4808 | | 0.4239 | 95.2387 | 18000 | 0.4792 | | 0.4174 | 100.5305 | 19000 | 0.4786 | | 0.4206 | 105.8223 | 20000 | 0.4777 | | 0.4104 | 111.1114 | 21000 | 0.4784 | | 0.4121 | 116.4032 | 22000 | 0.4797 | | 0.4087 | 121.6950 | 23000 | 0.4800 | | 0.4115 | 126.9867 | 24000 | 0.4788 | | 0.405 | 132.2759 | 25000 | 0.4799 | | 0.4091 | 137.5676 | 26000 | 0.4795 | | 0.4165 | 142.8594 | 27000 | 0.4799 | | 0.4059 | 148.1485 | 28000 | 0.4792 | | 0.4092 | 153.4403 | 29000 | 0.4797 | | 0.4006 | 158.7321 | 30000 | 0.4791 | | 0.4033 | 164.0212 | 31000 | 0.4789 | | 0.3929 | 169.3130 | 32000 | 0.4796 | | 0.4024 | 174.6048 | 33000 | 0.4803 | | 0.3988 | 179.8966 | 34000 | 0.4785 | | 0.3965 | 185.1857 | 35000 | 0.4792 | | 0.3914 | 190.4775 | 36000 | 0.4795 | | 0.3967 | 195.7692 | 37000 | 0.4811 | | 0.3994 | 201.0584 | 38000 | 0.4800 | | 0.4019 | 206.3501 | 39000 | 0.4805 | | 0.4005 | 211.6419 | 40000 | 0.4800 | - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1

senga-nt-asr-inferred-force-aligned-speecht5-LUK-ACT This model is a fine-tuned version of microsoft/speecht5tts on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5318 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - seed: 3407 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 200 - numepochs: 300.0 - mixedprecisiontraining: Native AMP | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:-----:|:---------------:| | 0.5415 | 29.4148 | 1000 | 0.5518 | | 0.4976 | 58.8296 | 2000 | 0.5369 | | 0.4763 | 88.2370 | 3000 | 0.5323 | | 0.4762 | 117.6519 | 4000 | 0.5341 | | 0.4607 | 147.0593 | 5000 | 0.5326 | | 0.4497 | 176.4741 | 6000 | 0.5361 | | 0.4489 | 205.8889 | 7000 | 0.5325 | | 0.4346 | 235.2963 | 8000 | 0.5331 | | 0.4373 | 264.7111 | 9000 | 0.5312 | | 0.4393 | 294.1185 | 10000 | 0.5318 | - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1

This model is a fine-tuned version of microsoft/speecht5tts on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5270 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - seed: 3407 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 200 - numepochs: 300.0 - mixedprecisiontraining: Native AMP | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:----:|:---------------:| | 0.5256 | 55.5797 | 1000 | 0.5491 | | 0.4805 | 111.1159 | 2000 | 0.5235 | | 0.4692 | 166.6957 | 3000 | 0.5226 | | 0.445 | 222.2319 | 4000 | 0.5257 | | 0.4348 | 277.8116 | 5000 | 0.5270 | - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

senga-nt-asr-inferred-force-aligned-speecht5-MAT-to-ACT This model is a fine-tuned version of microsoft/speecht5tts on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5144 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - seed: 3407 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments - lrschedulertype: linear - lrschedulerwarmupsteps: 200 - numepochs: 300.0 - mixedprecisiontraining: Native AMP | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:--------:|:-----:|:---------------:| | 0.572 | 13.5153 | 1000 | 0.5450 | | 0.5346 | 27.0271 | 2000 | 0.5272 | | 0.5276 | 40.5424 | 3000 | 0.5311 | | 0.5074 | 54.0542 | 4000 | 0.5191 | | 0.5025 | 67.5695 | 5000 | 0.5194 | | 0.4816 | 81.0814 | 6000 | 0.5211 | | 0.4842 | 94.5966 | 7000 | 0.5199 | | 0.4743 | 108.1085 | 8000 | 0.5154 | | 0.4681 | 121.6237 | 9000 | 0.5141 | | 0.4709 | 135.1356 | 10000 | 0.5234 | | 0.452 | 148.6508 | 11000 | 0.5161 | | 0.4488 | 162.1627 | 12000 | 0.5170 | | 0.4445 | 175.6780 | 13000 | 0.5159 | | 0.4511 | 189.1898 | 14000 | 0.5148 | | 0.4412 | 202.7051 | 15000 | 0.5147 | | 0.4388 | 216.2169 | 16000 | 0.5155 | | 0.4336 | 229.7322 | 17000 | 0.5161 | | 0.4447 | 243.2441 | 18000 | 0.5126 | | 0.4362 | 256.7593 | 19000 | 0.5163 | | 0.4184 | 270.2712 | 20000 | 0.5149 | | 0.4544 | 283.7864 | 21000 | 0.5156 | | 0.4261 | 297.2983 | 22000 | 0.5144 | - Transformers 4.57.1 - Pytorch 2.8.0+cu128 - Datasets 4.2.0 - Tokenizers 0.22.1

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed]