Instructions to use THemidli/applied-ner-stage4-bert-tiny-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage4-bert-tiny-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage4-bert-tiny-improved")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "stage": 4, | |
| "dataset_id": "THemidli/applied-ner-stage4-improved", | |
| "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": 841, | |
| "test_records": 159, | |
| "train_chunks": 865, | |
| "test_chunks": 165, | |
| "max_length": 256, | |
| "wall_seconds": 14.937, | |
| "trainer_metrics": { | |
| "train_runtime": 14.8851, | |
| "train_samples_per_second": 929.789, | |
| "train_steps_per_second": 30.097, | |
| "total_flos": 6246600094602.0, | |
| "train_loss": 0.4432598276595984, | |
| "epoch": 16.0 | |
| }, | |
| "hyperparameters": { | |
| "epochs": 16, | |
| "learning_rate": 0.0005, | |
| "train_batch_size": 32, | |
| "eval_batch_size": 64, | |
| "weight_decay": 0.02, | |
| "label_smoothing_factor": 0.0, | |
| "warmup_steps": 45, | |
| "scheduler": "linear", | |
| "hidden_dropout": 0.1, | |
| "attention_dropout": 0.1, | |
| "classifier_dropout": 0.1 | |
| }, | |
| "train_overall": { | |
| "overall_precision": 0.9539951573849879, | |
| "overall_recall": 0.9708781362007168, | |
| "overall_f1": 0.9623626068613301, | |
| "overall_accuracy": 0.9948478767829058 | |
| }, | |
| "test_overall": { | |
| "overall_precision": 0.42610652663165793, | |
| "overall_recall": 0.5264133456904542, | |
| "overall_f1": 0.4709784411276949, | |
| "overall_accuracy": 0.8332408742926883 | |
| }, | |
| "test_f1_change_vs_stage3": 0.002472883284961258 | |
| } | |