Instructions to use THemidli/applied-ner-stage3-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage3-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage3-bert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage3-bert-tiny") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage3-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "stage": 3, | |
| "dataset_id": "THemidli/applied-ner-stage2-expanded", | |
| "base_model": "google/bert_uncased_L-2_H-128_A-2", | |
| "seed": 20260802, | |
| "labels": [ | |
| "O", | |
| "B-PERSON", | |
| "I-PERSON", | |
| "B-ORGANIZATION", | |
| "I-ORGANIZATION", | |
| "B-LOCATION", | |
| "I-LOCATION", | |
| "B-TIMEDATE", | |
| "I-TIMEDATE", | |
| "B-PRODUCT", | |
| "I-PRODUCT", | |
| "B-WORKOFART", | |
| "I-WORKOFART", | |
| "B-JOB", | |
| "I-JOB", | |
| "B-AMOUNT", | |
| "I-AMOUNT" | |
| ], | |
| "parameter_count": 4371601, | |
| "trainable_parameter_count": 4371601, | |
| "device": "mps", | |
| "platform": "macOS-27.0-arm64-arm-64bit", | |
| "torch_version": "2.13.0", | |
| "train_records": 641, | |
| "test_records": 159, | |
| "train_chunks": 665, | |
| "test_chunks": 165, | |
| "max_length": 256, | |
| "wall_seconds": 17.78, | |
| "trainer_metrics": { | |
| "train_runtime": 17.7226, | |
| "train_samples_per_second": 750.453, | |
| "train_steps_per_second": 23.699, | |
| "total_flos": 6790820960700.0, | |
| "train_loss": 0.4253458108220782, | |
| "epoch": 20.0 | |
| }, | |
| "hyperparameters": { | |
| "epochs": 20, | |
| "learning_rate": 0.0005, | |
| "train_batch_size": 32, | |
| "eval_batch_size": 64, | |
| "weight_decay": 0.01, | |
| "warmup_steps": 42, | |
| "scheduler": "linear" | |
| }, | |
| "train_overall": { | |
| "overall_precision": 0.9580712788259959, | |
| "overall_recall": 0.9725990955041235, | |
| "overall_f1": 0.9652805280528053, | |
| "overall_accuracy": 0.9956925601429952 | |
| }, | |
| "test_overall": { | |
| "overall_precision": 0.42148148148148146, | |
| "overall_recall": 0.5273401297497683, | |
| "overall_f1": 0.4685055578427336, | |
| "overall_accuracy": 0.8269166759125708 | |
| } | |
| } | |