Instructions to use teglad/DistilRoBERTaEmotionClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teglad/DistilRoBERTaEmotionClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="teglad/DistilRoBERTaEmotionClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("teglad/DistilRoBERTaEmotionClassifier") model = AutoModelForSequenceClassification.from_pretrained("teglad/DistilRoBERTaEmotionClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
---
|
| 4 |
+
# Model Card for DistilRoBERTaEmotionClassifier
|
| 5 |
+
|
| 6 |
+
This model was created to demonstrate several MLOps practices and was for education purposes only. Please see the following [GitHub repo](https://github.com/teg-lad/CA4015-MLOPSPipelineImplementation) covering the material
|
| 7 |
+
|
| 8 |
+
## Model Details
|
| 9 |
+
|
| 10 |
+
### Model Description
|
| 11 |
+
|
| 12 |
+
This model was trained on the [Kaggle Emotions](https://www.kaggle.com/datasets/nelgiriyewithana/emotions/data) dataset, which has 6 classes.
|
| 13 |
+
|
| 14 |
+
+ Sadness (0)
|
| 15 |
+
+ Joy (1)
|
| 16 |
+
+ Love (2)
|
| 17 |
+
+ Anger (3)
|
| 18 |
+
+ Fear (4)
|
| 19 |
+
+ Surprise (5)
|