Text Classification
Transformers
ONNX
Safetensors
Rust
English
tool-use
function-calling
tool-calling
guardrails
verifier
shadow-mode
Instructions to use cowWhySo/toolcall-verifier-classifier-production with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cowWhySo/toolcall-verifier-classifier-production with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cowWhySo/toolcall-verifier-classifier-production")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cowWhySo/toolcall-verifier-classifier-production", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,821 Bytes
2114010 | 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 97 98 99 100 101 102 103 104 | {
"schema_version": "toolcall-verifier-tokenization-diagnostics/v1",
"base_model": "microsoft/deberta-v3-small",
"run_profile": "high_vram_quality",
"max_length": 1280,
"sample_rows": 5000,
"token_length_percentiles": {
"0.5": 283.0,
"0.75": 343.0,
"0.9": 436.0,
"0.95": 505.0,
"0.99": 680.0200000000004
},
"candidate_marker_missing_rows": 0,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0,
"candidate_payload_token_budget_percentiles": {
"0.01": 1084.99,
"0.05": 1177.0,
"0.1": 1202.0,
"0.5": 1232.0
},
"by_split": {
"calibration": {
"rows": 223,
"token_length_percentiles": {
"0.5": 286.0,
"0.9": 455.8,
"0.95": 514.0999999999998,
"0.99": 718.7800000000001
},
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"test": {
"rows": 473,
"token_length_percentiles": {
"0.5": 280.0,
"0.9": 444.40000000000003,
"0.95": 519.5999999999999,
"0.99": 672.8399999999999
},
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"train": {
"rows": 4072,
"token_length_percentiles": {
"0.5": 283.0,
"0.9": 435.0,
"0.95": 507.4499999999998,
"0.99": 688.1599999999999
},
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"validation": {
"rows": 232,
"token_length_percentiles": {
"0.5": 280.5,
"0.9": 418.6,
"0.95": 447.5999999999999,
"0.99": 567.0599999999998
},
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
}
},
"by_label": {
"needs_clarification": {
"rows": 7,
"token_length_p95": 403.4,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"tool_not_needed": {
"rows": 1552,
"token_length_p95": 326.89999999999964,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"valid": {
"rows": 1626,
"token_length_p95": 527.5,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"wrong_arguments_semantic": {
"rows": 1069,
"token_length_p95": 566.0,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
},
"wrong_tool_semantic": {
"rows": 746,
"token_length_p95": 566.75,
"candidate_marker_truncated_rows": 0,
"candidate_marker_truncated_rate": 0.0
}
},
"schema_marker_truncated_rows": 0,
"schema_marker_truncated_rate": 0.0,
"candidate_truncation_hard_fail_rate": 0.005
} |