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
- Downloads last month
- 23
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for vagrawal787/todos_task_model
Base model
distilbert/distilbert-base-uncased