Instructions to use cssupport/bert-news-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cssupport/bert-news-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cssupport/bert-news-class")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("cssupport/bert-news-class") model = AutoModelForMaskedLM.from_pretrained("cssupport/bert-news-class") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -116,4 +116,10 @@ one P6000 GPU
|
|
| 116 |
|
| 117 |
#### Software
|
| 118 |
|
| 119 |
-
Pytorch and HuggingFace
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 116 |
|
| 117 |
#### Software
|
| 118 |
|
| 119 |
+
Pytorch and HuggingFace
|
| 120 |
+
|
| 121 |
+
### Citation
|
| 122 |
+
|
| 123 |
+
Misra, Rishabh. "News Category Dataset." arXiv preprint arXiv:2209.11429 (2022).
|
| 124 |
+
Misra, Rishabh and Jigyasa Grover. "Sculpting Data for ML: The first act of Machine Learning." ISBN 9798585463570 (2021).
|
| 125 |
+
Tandon, Karan. "This LLM is based on BERT (2018) a bidirectional Transformer. BERT was finetuned using AdamW with the help of NVIDIA AMP and trained in 45 minutes on one P6000 GPU. This model accepts news summary/news headlines/news article and classifies into one of 40 categories"
|