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
File size: 710 Bytes
422082e | 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-tiny-ner-improved",
"device": "cpu",
"torch_threads": 8,
"parameter_count": 4371601,
"parameter_memory_mb": 17.486404,
"artifact_size_mb": 18.209406,
"rss_before_load_mb": 442.400768,
"rss_after_load_mb": 474.218496,
"model_load_rss_delta_mb": 31.817728,
"rss_after_inference_mb": 483.885056,
"inference_rss_delta_mb": 41.484288,
"benchmark_examples": 2000,
"batch_size": 32,
"elapsed_seconds": 0.1722599580007227,
"throughput_examples_per_second": 11610.359268702534,
"mean_latency_ms_per_example_at_batch_size": 0.08612997900036135,
"scope": "end-to-end fast-tokenizer plus PyTorch CPU forward pass on the 40 wild probes"
}
|