Gokuls
Rank:Average Model Cost: $0.0000
Number of Runs: 1,605
Models by this creator
bert-tiny-emotion-KD-BERT
bert-tiny-emotion-KD-BERT
bert-tiny-emotion-KD-BERT This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the emotion dataset. It achieves the following results on the evaluation set: Loss: 0.4810 Accuracy: 0.9175 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: 33 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.22.1 Pytorch 1.12.1+cu113 Datasets 2.5.1 Tokenizers 0.12.1
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832
Huggingface
BERT-tiny-sst2
BERT-tiny-sst2
BERT-tiny-sst2 This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset. It achieves the following results on the evaluation set: Loss: 0.4422 Accuracy: 0.8372 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: 33 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.22.1 Pytorch 1.12.1+cu113 Datasets 2.5.1 Tokenizers 0.12.1
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505
Huggingface
distilroberta-emotion-intent
distilroberta-emotion-intent
distilroberta-emotion-intent This model is a fine-tuned version of distilroberta-base on the emotion dataset. It achieves the following results on the evaluation set: Loss: 0.1496 Accuracy: 0.9435 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: 33 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 15 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.22.1 Pytorch 1.12.1+cu113 Datasets 2.5.1 Tokenizers 0.12.1
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55
Huggingface
BERT-tiny-emotion-intent
BERT-tiny-emotion-intent
BERT-tiny-emotion-intent This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the emotion dataset. It achieves the following results on the evaluation set: Loss: 0.3620 Accuracy: 0.91 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: 33 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.22.1 Pytorch 1.12.1+cu113 Datasets 2.5.1 Tokenizers 0.12.1
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53
Huggingface
bert-base-uncased-sst2
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31
Huggingface
hBERTv2_new_pretrain_48_emb_com_sst2
hBERTv2_new_pretrain_48_emb_com_sst2
hBERTv2_new_pretrain_48_emb_com_sst2 This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48 on the GLUE SST2 dataset. It achieves the following results on the evaluation set: Loss: 0.4789 Accuracy: 0.8050 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 4e-05 train_batch_size: 128 eval_batch_size: 128 seed: 10 distributed_type: multi-GPU optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 Training results Framework versions Transformers 4.30.2 Pytorch 1.14.0a0+410ce96 Datasets 2.12.0 Tokenizers 0.13.3
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28
Huggingface
sa_BERT_no_pretrain_sst2
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28
Huggingface
add_BERT_no_pretrain_sst2
add_BERT_no_pretrain_sst2
add_BERT_no_pretrain_sst2 This model is a fine-tuned version of on the GLUE SST2 dataset. It achieves the following results on the evaluation set: Loss: 0.6936 Accuracy: 0.5092 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 4e-05 train_batch_size: 128 eval_batch_size: 128 seed: 10 distributed_type: multi-GPU optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 Training results Framework versions Transformers 4.30.2 Pytorch 1.14.0a0+410ce96 Datasets 2.12.0 Tokenizers 0.13.3
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26
Huggingface
hBERTv1_new_pretrain_48_emb_com_sst2
hBERTv1_new_pretrain_48_emb_com_sst2
hBERTv1_new_pretrain_48_emb_com_sst2 This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48 on the GLUE SST2 dataset. It achieves the following results on the evaluation set: Loss: 0.4656 Accuracy: 0.7947 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 4e-05 train_batch_size: 128 eval_batch_size: 128 seed: 10 distributed_type: multi-GPU optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 50 Training results Framework versions Transformers 4.30.2 Pytorch 1.14.0a0+410ce96 Datasets 2.12.0 Tokenizers 0.13.3
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24
Huggingface
distilbert-base-Massive-intent
distilbert-base-Massive-intent
distilbert-base-Massive-intent This model is a fine-tuned version of distilbert-base-uncased on the massive dataset. It achieves the following results on the evaluation set: Loss: 0.7693 Accuracy: 0.8947 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: 33 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 15 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.22.1 Pytorch 1.12.1+cu113 Datasets 2.5.1 Tokenizers 0.12.1
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23
Huggingface