# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Yannis98/squad_albert_finetuned")
model = AutoModelForQuestionAnswering.from_pretrained("Yannis98/squad_albert_finetuned")Quick Links
squad_albert_finetuned4
This model is a fine-tuned version of albert/albert-base-v2 on the squad 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: 3e-05
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.21.0
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Model tree for Yannis98/squad_albert_finetuned
Base model
albert/albert-base-v2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Yannis98/squad_albert_finetuned")