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
File size: 699 Bytes
d2feccb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"stage": 3,
"model_path": "models/stage3-bert-tiny-ner",
"device": "cpu",
"torch_threads": 8,
"parameter_count": 4371601,
"parameter_memory_mb": 17.486404,
"artifact_size_mb": 18.209407,
"rss_before_load_mb": 442.44992,
"rss_after_load_mb": 474.300416,
"model_load_rss_delta_mb": 31.850496,
"rss_after_inference_mb": 483.753984,
"inference_rss_delta_mb": 41.304064,
"benchmark_examples": 2000,
"batch_size": 32,
"elapsed_seconds": 0.1745212919995538,
"throughput_examples_per_second": 11459.919744377743,
"mean_latency_ms_per_example_at_batch_size": 0.0872606459997769,
"scope": "end-to-end fast-tokenizer plus PyTorch CPU forward pass on the 40 wild probes"
}
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