Transformers
PyTorch
Hindi
t5
text2text-generation
t5-leaderboard
Generated from Trainer
text-generation-inference
Instructions to use Hunzla/output_urdu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hunzla/output_urdu with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hunzla/output_urdu") model = AutoModelForSeq2SeqLM.from_pretrained("Hunzla/output_urdu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- hi
license: apache-2.0
base_model: T5-base
tags:
- t5-leaderboard
- generated_from_trainer
datasets:
- hunzla-usman/custom-dataset
model-index:
- name: T5-urdu-to-code
results: []
T5-urdu-to-code
This model is a fine-tuned version of T5-base on the Custom dataset dataset.
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3