Text Classification
Transformers
Safetensors
English
emcoder
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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model_state.json from yezdata/EmCoder: direct link, hf CLI and curl.
- Browser
- Download file 79 Bytes
-
https://huggingface.co/yezdata/EmCoder/resolve/296800dbaa3a77c656cbae6aa5a077dc91b7c558/model_state.json
- Command line
-
hf download hf://yezdata/EmCoder@296800dbaa3a77c656cbae6aa5a077dc91b7c558/model_state.json
-
curl -L -o model_state.json https://huggingface.co/yezdata/EmCoder/resolve/296800dbaa3a77c656cbae6aa5a077dc91b7c558/model_state.json
79 Bytes
| { | |
| "train_loss": 0.264223575592041, | |
| "eval_loss": 0.2328128303236821 | |
| } |