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.safetensors from yezdata/EmCoder: direct link, hf CLI and curl.
- Browser
- Download file 327 MB
-
https://huggingface.co/yezdata/EmCoder/resolve/main/model.safetensors
- Command line
-
hf download hf://yezdata/EmCoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/yezdata/EmCoder/resolve/main/model.safetensors
327 MB
- Xet hash:
- e3e4e95e2ace645c28acaf8c23bf6eafd6b5dbc399f1446f7912a3f647ce0a0c
- Size of remote file:
- 327 MB
- SHA256:
- dcafbdeeb27dee7d1b20b881df4998fc4bb90be7ebda16a36c316fcc19b2127c
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