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
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"... is not valid JSON
| { | |
| "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 | |
| } |