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: 555 Bytes
2216aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | import numpy as np
from numpy.testing import assert_, assert_array_equal, assert_equal
class TestView:
def test_type(self):
x = np.array([1, 2, 3])
assert_(isinstance(x.view(np.matrix), np.matrix))
def test_keywords(self):
x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype='<i2', type=np.matrix)
assert_array_equal(y, [[513]])
assert_(isinstance(y, np.matrix))
assert_equal(y.dtype, np.dtype('<i2'))
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