# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("tyavika/pytorch")
model = AutoModelForQuestionAnswering.from_pretrained("tyavika/pytorch")Quick Links
pytorch
This model is a fine-tuned version of on the None 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
Training results
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="tyavika/pytorch")