Instructions to use Andranik/TestQA2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andranik/TestQA2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Andranik/TestQA2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Andranik/TestQA2") model = AutoModelForQuestionAnswering.from_pretrained("Andranik/TestQA2") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Andranik/TestQA2")
model = AutoModelForQuestionAnswering.from_pretrained("Andranik/TestQA2")Quick Links
electra_large_discriminator_squad2_512
This model is a fine-tuned version of ahotrod/electra_large_discriminator_squad2_512 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: 5e-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: 3.0
Training results
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Andranik/TestQA2")