Instructions to use hilmansw/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hilmansw/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hilmansw/emotion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hilmansw/emotion_classification") model = AutoModelForImageClassification.from_pretrained("hilmansw/emotion_classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -23,6 +23,7 @@ model-index:
|
|
| 23 |
- name: Accuracy
|
| 24 |
type: accuracy
|
| 25 |
value: 0.45
|
|
|
|
| 26 |
---
|
| 27 |
|
| 28 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -81,4 +82,4 @@ The following hyperparameters were used during training:
|
|
| 81 |
- Transformers 4.33.1
|
| 82 |
- Pytorch 2.0.1+cu118
|
| 83 |
- Datasets 2.14.5
|
| 84 |
-
- Tokenizers 0.13.3
|
|
|
|
| 23 |
- name: Accuracy
|
| 24 |
type: accuracy
|
| 25 |
value: 0.45
|
| 26 |
+
pipeline_tag: image-classification
|
| 27 |
---
|
| 28 |
|
| 29 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 82 |
- Transformers 4.33.1
|
| 83 |
- Pytorch 2.0.1+cu118
|
| 84 |
- Datasets 2.14.5
|
| 85 |
+
- Tokenizers 0.13.3
|