Instructions to use Fenix125/bert-spam-ham-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fenix125/bert-spam-ham-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fenix125/bert-spam-ham-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fenix125/bert-spam-ham-classifier") model = AutoModelForSequenceClassification.from_pretrained("Fenix125/bert-spam-ham-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -6,4 +6,11 @@ base_model:
|
|
| 6 |
- google-bert/bert-base-uncased
|
| 7 |
pipeline_tag: text-classification
|
| 8 |
library_name: transformers
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
---
|
|
|
|
| 6 |
- google-bert/bert-base-uncased
|
| 7 |
pipeline_tag: text-classification
|
| 8 |
library_name: transformers
|
| 9 |
+
metrics:
|
| 10 |
+
- accuracy
|
| 11 |
+
- precision
|
| 12 |
+
- recall
|
| 13 |
+
- f1
|
| 14 |
+
tags:
|
| 15 |
+
- code
|
| 16 |
---
|