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
File size: 651 Bytes
31f777c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"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
}
}
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