todos_task_model

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todos_task_model

This model is a fine-tuned version of distilbert-base-uncased on the vagrawal787/todo_task_list_types dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.2696
• eval_accuracy: 0.95
• eval_runtime: 0.2417
• eval_samples_per_second: 248.265
• eval_steps_per_second: 62.066
• step: 0

Model description

Input: Text string of a todo-like task such as "get groceries" Output: A type label for what type of task it is (home, personal, work, emergency, etc.)

Intended uses & limitations

More information needed

Training and evaluation data

The dataset used is provided in the card.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.1
• Tokenizers 0.13.3