# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Deehan1866/PR-pass-structbert-large")
model = AutoModelForQuestionAnswering.from_pretrained("Deehan1866/PR-pass-structbert-large", device_map="auto")Quick Links
finetuned
This model is a fine-tuned version of bayartsogt/structbert-large on the PiC/phrase_retrieval PR-pass 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: 3e-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: 2.0
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Deehan1866/PR-pass-structbert-large
Base model
bayartsogt/structbert-large
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Deehan1866/PR-pass-structbert-large")