Instructions to use THemidli/applied-ner-stage4-bert-mini-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage4-bert-mini-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage4-bert-mini-final")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage4-bert-mini-final") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage4-bert-mini-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "stage": 4, | |
| "model_path": "models/stage4-bert-mini-final", | |
| "device": "cpu", | |
| "torch_threads": 8, | |
| "parameter_count": 11109137, | |
| "parameter_memory_mb": 44.436548, | |
| "artifact_size_mb": 45.163283, | |
| "rss_before_load_mb": 442.351616, | |
| "rss_after_load_mb": 474.64448, | |
| "model_load_rss_delta_mb": 32.292864, | |
| "rss_after_inference_mb": 500.875264, | |
| "inference_rss_delta_mb": 58.523648, | |
| "benchmark_examples": 2000, | |
| "batch_size": 32, | |
| "elapsed_seconds": 0.48290212500069174, | |
| "throughput_examples_per_second": 4141.626007541663, | |
| "mean_latency_ms_per_example_at_batch_size": 0.24145106250034587, | |
| "scope": "end-to-end fast-tokenizer plus PyTorch CPU forward pass on the 40 wild probes" | |
| } | |