Text Classification
Transformers
ONNX
Safetensors
English
distilbert
intent-classification
multitask
iab
conversational-ai
adtech
calibrated-confidence
text-embeddings-inference
Instructions to use admesh/agentic-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use admesh/agentic-intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="admesh/agentic-intent-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("admesh/agentic-intent-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,389 Bytes
0584798 53d5d9f 0584798 53d5d9f 1519226 0584798 1519226 0584798 53d5d9f 0584798 53d5d9f 0584798 53d5d9f 0584798 53d5d9f 0584798 53d5d9f 0584798 37d98fb 0584798 37d98fb 0584798 bedab52 0584798 bedab52 0584798 53d5d9f 0584798 53d5d9f 0584798 53d5d9f 37d98fb 0584798 53d5d9f 0584798 1519226 0584798 37d98fb 1519226 0584798 53d5d9f 0584798 | 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 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | {
"accepted_accuracy": 0.9619,
"accepted_coverage": 1.0,
"accuracy": 0.9619,
"confusion_matrix_path": "/content/agentic-intent-classifier/artifacts/evaluation/latest/decision_phase_difficulty_benchmark_confusion_matrix.csv",
"count": 105,
"dataset_path": "/content/agentic-intent-classifier/data/decision_phase_benchmark.jsonl",
"difficulty_breakdown": {
"easy": {
"accepted_accuracy": 0.9714,
"accepted_coverage": 1.0,
"accuracy": 0.9714,
"count": 35,
"fallback_rate": 0.0,
"macro_f1": 0.9711
},
"hard": {
"accepted_accuracy": 0.9143,
"accepted_coverage": 1.0,
"accuracy": 0.9143,
"count": 35,
"fallback_rate": 0.0,
"macro_f1": 0.9194
},
"medium": {
"accepted_accuracy": 1.0,
"accepted_coverage": 1.0,
"accuracy": 1.0,
"count": 35,
"fallback_rate": 0.0,
"macro_f1": 1.0
}
},
"fallback_rate": 0.0,
"head": "decision_phase",
"macro_f1": 0.9635,
"per_class_metrics": {
"accuracy": 0.9619047619047619,
"action": {
"f1-score": 0.9655172413793104,
"precision": 1.0,
"recall": 0.9333333333333333,
"support": 15.0
},
"awareness": {
"f1-score": 0.9655172413793104,
"precision": 1.0,
"recall": 0.9333333333333333,
"support": 15.0
},
"consideration": {
"f1-score": 0.9655172413793104,
"precision": 1.0,
"recall": 0.9333333333333333,
"support": 15.0
},
"decision": {
"f1-score": 0.9655172413793104,
"precision": 1.0,
"recall": 0.9333333333333333,
"support": 15.0
},
"macro avg": {
"f1-score": 0.9634888438133874,
"precision": 0.9699248120300752,
"recall": 0.9619047619047619,
"support": 105.0
},
"post_purchase": {
"f1-score": 1.0,
"precision": 1.0,
"recall": 1.0,
"support": 15.0
},
"research": {
"f1-score": 0.8823529411764706,
"precision": 0.7894736842105263,
"recall": 1.0,
"support": 15.0
},
"support": {
"f1-score": 1.0,
"precision": 1.0,
"recall": 1.0,
"support": 15.0
},
"weighted avg": {
"f1-score": 0.9634888438133875,
"precision": 0.9699248120300752,
"recall": 0.9619047619047619,
"support": 105.0
}
},
"suite": "difficulty_benchmark"
}
|