Text Classification
Transformers
PyTorch
Core ML
Safetensors
English
bert
exbert
text-embeddings-inference
Instructions to use ayjays132/Quantum-NeuralAdaptiveLearningSystem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayjays132/Quantum-NeuralAdaptiveLearningSystem with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ayjays132/Quantum-NeuralAdaptiveLearningSystem")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayjays132/Quantum-NeuralAdaptiveLearningSystem") model = AutoModelForSequenceClassification.from_pretrained("ayjays132/Quantum-NeuralAdaptiveLearningSystem") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,4 +1,19 @@
|
|
| 1 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
language: en
|
| 3 |
tags:
|
| 4 |
- exbert
|
|
|
|
| 1 |
---
|
| 2 |
+
tokenizer:
|
| 3 |
+
name_or_path: bert-base-uncased # Replace with your preferred tokenizer, or use the same as the one used in training
|
| 4 |
+
|
| 5 |
+
task_specific:
|
| 6 |
+
text_classification:
|
| 7 |
+
num_labels: 3 # Adjust based on the number of categories in your classification task
|
| 8 |
+
label_stoi:
|
| 9 |
+
CLASSIFY: 0
|
| 10 |
+
POSITIVE: 1
|
| 11 |
+
NEGATIVE: 2
|
| 12 |
+
label_itos:
|
| 13 |
+
0: CLASSIFY
|
| 14 |
+
1: POSITIVE
|
| 15 |
+
2: NEGATIVE
|
| 16 |
+
threshold: 0.5 # Adjust based on your desired probability threshold for label assignment
|
| 17 |
language: en
|
| 18 |
tags:
|
| 19 |
- exbert
|