Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("vagrawal787/todos_task_model")
model = AutoModelForSequenceClassification.from_pretrained("vagrawal787/todos_task_model")Quick Links
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
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Model tree for vagrawal787/todos_task_model
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vagrawal787/todos_task_model")