Instructions to use cgt/pert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cgt/pert-qa with Transformers:
# 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="cgt/pert-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("cgt/pert-qa") model = AutoModelForQuestionAnswering.from_pretrained("cgt/pert-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
train completed.
Browse files- pytorch_model.bin +1 -1
- tokenizer.json +16 -2
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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tokenizer.json
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"truncation": {
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"direction": "Right",
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"max_length": 384,
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"strategy": "OnlySecond",
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"stride": 128
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"padding": {
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"strategy": {
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"Fixed": 384
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"direction": "Right",
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"pad_token": "[PAD]"
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"added_tokens": [
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