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
File size: 703 Bytes
911190a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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"
}
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