Text Classification
Transformers
ONNX
Safetensors
English
stratabert
diagnostic
long-context
custom-code
custom_code
Instructions to use dplotnikov/stratabert-tiny-ag-news-smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dplotnikov/stratabert-tiny-ag-news-smoke with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dplotnikov/stratabert-tiny-ag-news-smoke", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("dplotnikov/stratabert-tiny-ag-news-smoke", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "checkpoint_path": "checkpoints/run_001/tiny_ag_news_smoke/model.safetensors", | |
| "claim_status": "diagnostic_only", | |
| "data_versions": [ | |
| { | |
| "name": "train_index", | |
| "path": "data/eval_frozen/run_001/ag_news_train_index_sample64.json" | |
| }, | |
| { | |
| "name": "eval_index", | |
| "path": "data/eval_frozen/run_001/ag_news_eval_index_sample200.json" | |
| } | |
| ], | |
| "environment": { | |
| "cuda_available": false, | |
| "torch": "2.12.0+cu130" | |
| }, | |
| "eval_scores": { | |
| "accuracy": 0.15625, | |
| "loss": 1.3881464898586273, | |
| "macro_f1": 0.06756756756756757, | |
| "per_class": { | |
| "0": { | |
| "f1": 0.2702702702702703, | |
| "precision": 0.15625, | |
| "recall": 1.0, | |
| "support": 10 | |
| }, | |
| "1": { | |
| "f1": 0.0, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "support": 21 | |
| }, | |
| "2": { | |
| "f1": 0.0, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "support": 17 | |
| }, | |
| "3": { | |
| "f1": 0.0, | |
| "precision": 0.0, | |
| "recall": 0.0, | |
| "support": 16 | |
| } | |
| }, | |
| "support": 64, | |
| "weighted_f1": 0.04222972972972973 | |
| }, | |
| "git": { | |
| "commit": "no_commit_yet", | |
| "dirty": true | |
| }, | |
| "initialization": { | |
| "seed": 1337, | |
| "source_checkpoint": null, | |
| "type": "random" | |
| }, | |
| "max_seq_len": 96, | |
| "model_variant": "stratabert-tiny-smoke", | |
| "objective_mix": { | |
| "task": 1.0 | |
| }, | |
| "parent_run_id": null, | |
| "run_id": "run_001", | |
| "systems_metrics": {}, | |
| "timestamp": "2026-06-11T00:32:07.159581+00:00", | |
| "tokenizer": { | |
| "source": "answerdotai/ModernBERT-base", | |
| "vocab_size": 50368 | |
| }, | |
| "training_command": "scripts/finetune_classification.py --train-index data/eval_frozen/run_001/ag_news_train_index_sample64.json --train-split train --eval-index data/eval_frozen/run_001/ag_news_eval_index_sample200.json --eval-split test --max-train-examples 32 --max-eval-examples 64 --batch-size 8 --epochs 1 --max-length 96 --lr 5e-4 --seed 1337 --output runs/run_001/eval_reports/stratabert_tiny_ag_news_finetune_smoke.json --checkpoint-dir checkpoints/run_001/tiny_ag_news_smoke", | |
| "training_mode": "supervised_finetuning_smoke", | |
| "vast_ai": null | |
| } | |