rajeevmohan
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finetuning-sentiment-model-1500-samples

Text Classification

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text-embeddings-inference

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finetuning-sentiment-model-1500-samples

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

eval_loss: 0.3715
eval_model_preparation_time: 0.004
eval_accuracy: 0.8367
eval_f1: 0.8361
eval_runtime: 184.8159
eval_samples_per_second: 1.623
eval_steps_per_second: 0.103
step: 0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 1

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cpu
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 25

Safetensors

Model size

67M params

Tensor type

F32

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distilbert/distilbert-base-uncased

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