Text Classification
Transformers
JAX
pcmlp
feature-extraction
predictive-coding
local-loss
flax
tiny-model
custom-architecture
custom_code
Eval Results (legacy)
Instructions to use zeechimp/pc-mlp-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeechimp/pc-mlp-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zeechimp/pc-mlp-tiny", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zeechimp/pc-mlp-tiny", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 276 Bytes
f38819e | 1 2 3 4 5 6 7 8 9 10 11 12 13 | {
"architectures": ["PCMLP"],
"auto_map": {
"AutoConfig": "configuration_pcmlp.PCMLPConfig",
"AutoModel": "modeling_pcmlp.PCMLP"
},
"model_type": "pcmlp",
"vocab_size": 16,
"seq_len": 16,
"num_classes": 2,
"hidden_dim": 32,
"local_loss_weight": 0.1
} |