Text Classification
Transformers
Safetensors
English
modernbert
fill-mask
decision-model
routing
classification
verification
brier-score
calibration
bidirectional
Eval Results (legacy)
Instructions to use mpnikhil/dev-0.4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpnikhil/dev-0.4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpnikhil/dev-0.4b")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mpnikhil/dev-0.4b") model = AutoModelForMaskedLM.from_pretrained("mpnikhil/dev-0.4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,200 Bytes
7692b80 | 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 | {
"train": "data/train_refine.jsonl",
"validation": "data/validation_refine.jsonl",
"model": "answerdotai/ModernBERT-large",
"revision": null,
"output": "runs/jev-unified-large",
"device": "mps",
"epochs": 3,
"batch_size": 8,
"accumulation": 4,
"max_length": 1024,
"max_steps": 0,
"encoder_lr": 5e-06,
"head_lr": 0.0001,
"freeze_encoder": false,
"gradient_checkpointing": true,
"lora_rank": 0,
"resume": "runs/jev-unified-large/trainer.pt",
"init_checkpoint": null,
"seed": 17,
"schema_version": 2,
"device_used": "mps",
"parameters": 396880897,
"trainable_parameters": 396880897,
"status": "calibrated",
"temperatures": {
"noul": 1.3575,
"choice": 4.2542,
"score": 3.7097
},
"calibration_method": "temperature_scaling_guo_2017",
"calibration_metrics": {
"noul": {
"temperature": 1.3575,
"ece_equal_mass": 0.016,
"accuracy": 0.9665,
"note": "Fitted on held-out validation; on external BoolQ (n=500), reduces ECE from 0.1033 to 0.0771 (-25%) with 100% accuracy invariance."
},
"choice": {
"temperature": 4.2542,
"ece_before": 0.1887,
"ece_after": 0.083,
"nll_before": 2.2566,
"nll_after": 0.7259,
"accuracy": 0.7819
},
"score": {
"temperature": 3.7097,
"ece_before": 0.3315,
"ece_after": 0.1165,
"nll_before": 2.5899,
"nll_after": 1.1448,
"mae_before": 0.7243,
"mae_after": 0.7165,
"accuracy": 0.5448
}
},
"before": {
"choice_accuracy": 0.8006968641114982,
"choice_nll": Infinity,
"choice_recall_at_3": 0.9665505226480836,
"score_accuracy": 0.5380281690140845,
"score_nll": Infinity,
"score_mae": 0.7156380733637743,
"noul_brier": 0.05362682237181553,
"noul_accuracy": 0.9386046511627907
},
"steps": 1566,
"after": {
"choice_accuracy": 0.7804878048780488,
"choice_nll": Infinity,
"choice_recall_at_3": 0.9616724738675958,
"score_accuracy": 0.5442253521126761,
"score_nll": Infinity,
"score_mae": 0.7245084745783201,
"noul_brier": 0.02923501114512599,
"noul_accuracy": 0.9665116279069768
},
"elapsed_s": 10037.545325374987
} |