Instructions to use 0xMaka/based-bert-sc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0xMaka/based-bert-sc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="0xMaka/based-bert-sc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("0xMaka/based-bert-sc") model = AutoModelForSequenceClassification.from_pretrained("0xMaka/based-bert-sc") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,9 +7,12 @@ metrics:
|
|
| 7 |
- accuracy
|
| 8 |
- f1
|
| 9 |
widget:
|
| 10 |
-
- text:
|
| 11 |
-
example_title:
|
| 12 |
-
- text:
|
| 13 |
-
|
|
|
|
|
|
|
|
|
|
| 14 |
---
|
| 15 |
# Based Bert
|
|
|
|
| 7 |
- accuracy
|
| 8 |
- f1
|
| 9 |
widget:
|
| 10 |
+
- text: 'identify candle: 17284.58,17264.41,17284.58,17264.41'
|
| 11 |
+
example_title: Sequence classification
|
| 12 |
+
- text: >-
|
| 13 |
+
identify candle: open: 17343.43, close: 17625.18, high: 17804.68, low:
|
| 14 |
+
17322.15
|
| 15 |
+
example_title: Sequence classification
|
| 16 |
+
license: gpl
|
| 17 |
---
|
| 18 |
# Based Bert
|