Question Answering
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
text-generation-inference
Instructions to use Meziane/question_answering_T5_seq_to_seq_med_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meziane/question_answering_T5_seq_to_seq_med_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Meziane/question_answering_T5_seq_to_seq_med_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Meziane/question_answering_T5_seq_to_seq_med_dataset") model = AutoModelForQuestionAnswering.from_pretrained("Meziane/question_answering_T5_seq_to_seq_med_dataset") - Notebooks
- Google Colab
- Kaggle
question_answering_T5_seq_to_seq_med_dataset
This model is a fine-tuned version of google-t5/t5-small 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: 4e-05
- 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: 2
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Meziane/question_answering_T5_seq_to_seq_med_dataset
Base model
google-t5/t5-small