Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 927 Bytes
2216aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import pytest
import networkx as nx
# smoke tests for exceptions
def test_raises_networkxexception():
with pytest.raises(nx.NetworkXException):
raise nx.NetworkXException
def test_raises_networkxerr():
with pytest.raises(nx.NetworkXError):
raise nx.NetworkXError
def test_raises_networkx_pointless_concept():
with pytest.raises(nx.NetworkXPointlessConcept):
raise nx.NetworkXPointlessConcept
def test_raises_networkxalgorithmerr():
with pytest.raises(nx.NetworkXAlgorithmError):
raise nx.NetworkXAlgorithmError
def test_raises_networkx_unfeasible():
with pytest.raises(nx.NetworkXUnfeasible):
raise nx.NetworkXUnfeasible
def test_raises_networkx_no_path():
with pytest.raises(nx.NetworkXNoPath):
raise nx.NetworkXNoPath
def test_raises_networkx_unbounded():
with pytest.raises(nx.NetworkXUnbounded):
raise nx.NetworkXUnbounded
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