Question Answering
Transformers
TensorBoard
Safetensors
distilbert
persian
nlp
Generated from Trainer
Instructions to use OmidSakaki/qa_nlp_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OmidSakaki/qa_nlp_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="OmidSakaki/qa_nlp_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("OmidSakaki/qa_nlp_model") model = AutoModelForQuestionAnswering.from_pretrained("OmidSakaki/qa_nlp_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
qa_nlp_model
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 2.6646
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: 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: 3
Training results
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for OmidSakaki/qa_nlp_model
Base model
distilbert/distilbert-base-uncased