Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use vimal52/T5-base-SQUAD-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vimal52/T5-base-SQUAD-Finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vimal52/T5-base-SQUAD-Finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("vimal52/T5-base-SQUAD-Finetuned") - Notebooks
- Google Colab
- Kaggle
T5-base-SQUAD-Finetuned
This model is a fine-tuned version of google/flan-t5-base on an unknown 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 100
Training results
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
- Downloads last month
- 7
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Model tree for vimal52/T5-base-SQUAD-Finetuned
Base model
google/flan-t5-base