Feature Extraction
Transformers
Safetensors
bert
research-library
repository-library
repo-paper-alignment
c2
t5_cross
v2
text-embeddings-inference
Instructions to use PeytonT/research-library-c2-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/research-library-c2-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PeytonT/research-library-c2-v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PeytonT/research-library-c2-v2") model = AutoModel.from_pretrained("PeytonT/research-library-c2-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_id": "C2", | |
| "display_name": "Repo-Paper Alignment Model", | |
| "experiment_name": "c2_repo_paper_alignment_pairs", | |
| "source_checkpoint": "models/checkpoints/C2/checkpoint-1000", | |
| "repo_id": "PeytonT/research-library-c2-v2", | |
| "version": "v2", | |
| "metrics": { | |
| "eval_loss": 0.4323210120201111, | |
| "eval_pair_accuracy": 0.4838709677419355, | |
| "eval_positive_score_mean": 0.42978216651827095, | |
| "eval_negative_score_mean": 0.3917496839421801, | |
| "eval_score_margin_mean": 0.03803248257609082, | |
| "eval_roc_auc": 0.5416666666666666, | |
| "eval_best_pair_accuracy": 0.5967741935483871, | |
| "eval_best_threshold": 0.2927139103412628 | |
| } | |
| } | |