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
File size: 1,549 Bytes
e6e51d0 bcfa0ad d9ca62e bcfa0ad d9ca62e e6e51d0 d9ca62e e6e51d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"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
}
} |