Instructions to use ronit33/intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ronit33/intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ronit33/intent-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ronit33/intent-classifier") model = AutoModelForSequenceClassification.from_pretrained("ronit33/intent-classifier") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
datasets:
|
| 3 |
+
- http://kaggle.com/datasets/bitext/training-dataset-for-chatbotsvirtual-assistants
|
| 4 |
+
metrics:
|
| 5 |
+
- accuracy
|
| 6 |
+
model-index:
|
| 7 |
+
- name: distilbert-base-uncased-finetuned-emotion-dataset
|
| 8 |
+
results:
|
| 9 |
+
- task:
|
| 10 |
+
name: Text Classification
|
| 11 |
+
type: text-classification
|
| 12 |
+
metrics:
|
| 13 |
+
- name: Accuracy
|
| 14 |
+
type: accuracy
|
| 15 |
+
value: 0.996
|
| 16 |
+
---
|
| 17 |
+
|