Hugging Face's logo

Tural
/
How_to_fine-tune_a_model_for_common_downstream_tasks

Question Answering
Transformers
PyTorch
distilbert
Generated from Trainer
Model card Files
xet
 Community
1

How_to_fine-tune_a_model_for_common_downstream_tasks

This model is a fine-tuned version of distilbert-base-uncased 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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.0
  • Datasets 2.14.5
  • Tokenizers 0.14.1
Downloads last month
11
Inference Providers NEW
Question Answering
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Tural/How_to_fine-tune_a_model_for_common_downstream_tasks

Base model

distilbert/distilbert-base-uncased
Finetuned
(12259)
this model

Dataset used to train Tural/How_to_fine-tune_a_model_for_common_downstream_tasks