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sentiment_model_14mar – AI Model by manjinder | AlphaNeural AI
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sentiment_model_14mar
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transformers
pytorch
tensorboard
distilbert
text-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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sentiment_model_14mar
This model is a fine-tuned version of
distilbert-base-uncased
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.2916
Y True: [1 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
Y Pred: [0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
Accuracy: 0.9204
F1: 0.9202
Precision: 0.9227
Recall: 0.9204
Confusion Matrix: [[142 6] [ 17 124]]
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
lr_scheduler_warmup_steps: 50
num_epochs: 5
Training results
Framework versions
Transformers 4.26.1
Pytorch 1.12.1+cu113
Datasets 2.9.0
Tokenizers 0.13.2
