# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("nbroad/splinter-base-squad2")
model = AutoModelForQuestionAnswering.from_pretrained("nbroad/splinter-base-squad2")Quick Links
splinter-base-squad2_3
This model is a fine-tuned version of tau/splinter-base-qass on the squad_v2 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: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.2.2
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="nbroad/splinter-base-squad2")