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)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
remove locally saved tokenizer
Browse files
README.md
CHANGED
|
@@ -44,7 +44,7 @@ model-index:
|
|
| 44 |
# EmCoder
|
| 45 |
<blockquote>
|
| 46 |
<b>Probabilistic Emotion Recognition & Uncertainty Quantification</b><br>
|
| 47 |
-
<b>28 Emotion multi-label Transformer
|
| 48 |
<b>Live Demo & API Service:</b> <a href="https://yezdata-emcoder-api-ui.hf.space">Try EmCoder on Hugging Face Spaces</a>
|
| 49 |
</blockquote>
|
| 50 |
|
|
@@ -76,7 +76,7 @@ from transformers import AutoModel, AutoTokenizer
|
|
| 76 |
repo_id = "yezdata/EmCoder"
|
| 77 |
|
| 78 |
# Load the same tokenizer used during training
|
| 79 |
-
tokenizer = AutoTokenizer.from_pretrained(
|
| 80 |
|
| 81 |
# Initialize with same config as training
|
| 82 |
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
|
|
|
|
| 44 |
# EmCoder
|
| 45 |
<blockquote>
|
| 46 |
<b>Probabilistic Emotion Recognition & Uncertainty Quantification</b><br>
|
| 47 |
+
<b>28 Emotion multi-label Transformer classifier</b><br>
|
| 48 |
<b>Live Demo & API Service:</b> <a href="https://yezdata-emcoder-api-ui.hf.space">Try EmCoder on Hugging Face Spaces</a>
|
| 49 |
</blockquote>
|
| 50 |
|
|
|
|
| 76 |
repo_id = "yezdata/EmCoder"
|
| 77 |
|
| 78 |
# Load the same tokenizer used during training
|
| 79 |
+
tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
|
| 80 |
|
| 81 |
# Initialize with same config as training
|
| 82 |
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
|