Instructions to use nasa-impact/science-keyword-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nasa-impact/science-keyword-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nasa-impact/science-keyword-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nasa-impact/science-keyword-classification") model = AutoModelForSequenceClassification.from_pretrained("nasa-impact/science-keyword-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
updaetd the link to techinal writeup
Browse files
README.md
CHANGED
|
@@ -38,7 +38,7 @@ We have fine-tuned [INDUS Model](https://huggingface.co/nasa-impact/nasa-smd-ibm
|
|
| 38 |
The model with focal loss and stratified sampling (alpha-1.2.1) outperformed all other configurations and previous models in terms of precision, recall, F1 score, and Jaccard similarity. The weighted metrics at various threshold for the model can be found below.
|
| 39 |

|
| 40 |
|
| 41 |
-
Please find accompanying [technical writeup here](https://
|
| 42 |
## References
|
| 43 |
|
| 44 |
- RoBERTa: [arXiv](https://arxiv.org/abs/1907.11692)
|
|
|
|
| 38 |
The model with focal loss and stratified sampling (alpha-1.2.1) outperformed all other configurations and previous models in terms of precision, recall, F1 score, and Jaccard similarity. The weighted metrics at various threshold for the model can be found below.
|
| 39 |

|
| 40 |
|
| 41 |
+
Please find accompanying [technical writeup here](https://drive.google.com/file/d/1g4l5tLjeNUu3z8fcVMKuIXs7hLifVfSu/view?usp=sharing).
|
| 42 |
## References
|
| 43 |
|
| 44 |
- RoBERTa: [arXiv](https://arxiv.org/abs/1907.11692)
|