Instructions to use Heng666/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heng666/codecarbon-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Heng666/codecarbon-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Heng666/codecarbon-text-classification") model = AutoModelForSequenceClassification.from_pretrained("Heng666/codecarbon-text-classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -4,10 +4,10 @@ datasets:
|
|
| 4 |
- imdb
|
| 5 |
language:
|
| 6 |
- en
|
| 7 |
-
co2_eq_emissions:
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
|
| 13 |
---
|
|
|
|
| 4 |
- imdb
|
| 5 |
language:
|
| 6 |
- en
|
| 7 |
+
co2_eq_emissions:
|
| 8 |
+
emissions: 1.2207030395688
|
| 9 |
+
source: "from AutoTrain, code carbon"
|
| 10 |
+
training_type: "fine-tuning"
|
| 11 |
+
geographical_location: "Singapore(SGP)"
|
| 12 |
+
hardware_used: "1 x NVIDIA A100-SXM4-40GB"
|
| 13 |
---
|