Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use AlexWang99/byt5_add with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWang99/byt5_add with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AlexWang99/byt5_add") model = AutoModelForSeq2SeqLM.from_pretrained("AlexWang99/byt5_add") - Notebooks
- Google Colab
- Kaggle
byt5_add
This model is a fine-tuned version of google/byt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0003
- eval_runtime: 10.8156
- eval_samples_per_second: 924.594
- eval_steps_per_second: 1.202
- epoch: 51.0
- step: 1275
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: 5e-05
- train_batch_size: 800
- eval_batch_size: 800
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
Framework versions
- Transformers 4.35.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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google/byt5-small