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
Download config.json from zeechimp/pc-mlp-tiny: direct link, hf CLI and curl.
- Browser
- Download file 276 Bytes
-
https://huggingface.co/zeechimp/pc-mlp-tiny/resolve/main/config.json
- Command line
-
hf download hf://zeechimp/pc-mlp-tiny/config.json
-
curl -L -o config.json https://huggingface.co/zeechimp/pc-mlp-tiny/resolve/main/config.json
276 Bytes
| { | |
| "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 | |
| } |