Instructions to use smtnkc/bert-ssm-uc-cosmic-total with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smtnkc/bert-ssm-uc-cosmic-total with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smtnkc/bert-ssm-uc-cosmic-total")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smtnkc/bert-ssm-uc-cosmic-total") model = AutoModelForSequenceClassification.from_pretrained("smtnkc/bert-ssm-uc-cosmic-total", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Input: Use-case description (Text)
Output: COSMIC Total Size (E+R+W+X)
Task: Regression (MSE Loss)
Dataset: uc-2040-en
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Model tree for smtnkc/bert-ssm-uc-cosmic-total
Base model
google-bert/bert-base-uncasedEvaluation results
- accuracy on uc-2040-enself-reported0.859
- mse on uc-2040-enself-reported0.124