Text Classification
Transformers
Safetensors
modernbert
unspsc
multi-label-classification
query-intent
Eval Results (legacy)
text-embeddings-inference
Instructions to use Subramanya97/modernbert-large-unspsc-query with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subramanya97/modernbert-large-unspsc-query with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Subramanya97/modernbert-large-unspsc-query")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Subramanya97/modernbert-large-unspsc-query") model = AutoModelForSequenceClassification.from_pretrained("Subramanya97/modernbert-large-unspsc-query", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "threshold": 0.8100000000000002, | |
| "evaluation_scope": "training_set", | |
| "metrics": { | |
| "threshold": 0.8100000000000002, | |
| "micro_precision": 0.9206364497535443, | |
| "micro_recall": 0.9558498712253182, | |
| "micro_f1": 0.9379127597191496, | |
| "macro_f1": 0.9789507383061357, | |
| "weighted_f1": 0.9389039513409726, | |
| "samples_f1": 0.7335267457478686, | |
| "accuracy": 0.9093033581827954, | |
| "exact_set_accuracy": 0.9093033581827954, | |
| "subset_accuracy": 0.9093033581827954, | |
| "hamming_accuracy": 0.9998384255000764, | |
| "hamming_loss": 0.00016157449992357058, | |
| "label_ranking_average_precision": 0.9855204820632935, | |
| "top1_in_targets": 0.9702593241854561, | |
| "predicted_labels_per_example": 0.9703442312635835, | |
| "true_labels_per_example": 0.9345968388991455, | |
| "empty_target_accuracy": 0.98516228748068, | |
| "precision_at_1": 0.9702593241854561, | |
| "recall_at_1": 0.8885699176401037, | |
| "ndcg_at_1": 0.9702593241854561, | |
| "precision_at_3": 0.39267363839690506, | |
| "recall_at_3": 0.9913231191157966, | |
| "ndcg_at_3": 0.9839843538730729, | |
| "precision_at_5": 0.239906103286385, | |
| "recall_at_5": 0.9986002784688296, | |
| "ndcg_at_5": 0.9866900444118982, | |
| "train_train_runtime": 8835.0724, | |
| "train_train_samples_per_second": 72.65, | |
| "train_train_steps_per_second": 0.568, | |
| "train_total_flos": 3.697673816034509e+16, | |
| "train_train_loss": 0.22032610135801053, | |
| "train_epoch": 5.0, | |
| "quality_gate_passed": 1.0 | |
| }, | |
| "quality_gate": { | |
| "metric": "micro_f1", | |
| "target": 0.9, | |
| "passed": true | |
| } | |
| } |