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simple_nn
feature-extraction
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custom_code
Instructions to use yasinamp/simple_nn_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yasinamp/simple_nn_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yasinamp/simple_nn_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 403 Bytes
8b17fbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | from transformers import PreTrainedModel
import torch.nn as nn
from .configuration_simple_model import SimpleNNConfig
# Define the model class
class SimpleNN(PreTrainedModel):
config_class = SimpleNNConfig
def __init__(self, config):
super().__init__(config)
self.dense = nn.Linear(config.input_size, config.num_classes)
def forward(self, x):
return self.dense(x) |