Edit model card

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

deberta-base-azerbaijani-v2

This model is a fine-tuned version of microsoft/mdeberta-v3-base on a various cleaned community corpus. It achieves the following results on the evaluation set:

  • Loss: 0.9572

We thank Microsoft Accelerating Foundation Models Research Program for supporting our research. Authors: Mammad Hajili, Duygu Ataman

Model description

The model was trained on masked language model task on a 4 X A100 80GB GPU for 23 hours. For downstream tasks, it requires to be fine-tuned based on objective of the task.

Training and evaluation data

The training data is clean mix of various Azerbaijani corpus shared by the community.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Perplexity at epoch 5: 2.6

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
Downloads last month
0
Safetensors
Model size
279M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for hajili/deberta-base-azerbaijani-v2

Finetuned
(198)
this model