Instructions to use Eurosmart/bert-uncased-qa-mash-covid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eurosmart/bert-uncased-qa-mash-covid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Eurosmart/bert-uncased-qa-mash-covid")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Eurosmart/bert-uncased-qa-mash-covid") model = AutoModelForQuestionAnswering.from_pretrained("Eurosmart/bert-uncased-qa-mash-covid") - Notebooks
- Google Colab
- Kaggle
bert-uncased-qa-mash-covid
This model is a fine-tuned version of bert-base-uncased on the mashqa_covid_dataset 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
Training results
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for Eurosmart/bert-uncased-qa-mash-covid
Base model
google-bert/bert-base-uncased