Instructions to use Akul/t5-small-command-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akul/t5-small-command-extractor with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Akul/t5-small-command-extractor") model = AutoModelForSeq2SeqLM.from_pretrained("Akul/t5-small-command-extractor") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-command-extractor
results: []
t5-small-command-extractor
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
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:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Tokenizers 0.19.1