Text Classification
Transformers
Safetensors
English
emcoder
feature-extraction
emotion-recognition
bayesian-deep-learning
mc-dropout
uncertainty-quantification
multi-label-classification
custom_code
Eval Results (legacy)
Instructions to use yezdata/EmCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yezdata/EmCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yezdata/EmCoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 291 Bytes
c47a352 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"bayesian_train": true,
"loss_weights": "log",
"tokenized_ds_dir": "data/goemotions_v1_seq512",
"encoder_lr": 0.00001,
"head_lr": 0.0005,
"lr_warmup": 0.05,
"weight_decay": 0.01,
"batch_size": 32,
"gradient_accumulation_steps": 8,
"num_epochs": 10
} |