Instructions to use hfvladkon/bert_token_classification_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfvladkon/bert_token_classification_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hfvladkon/bert_token_classification_detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hfvladkon/bert_token_classification_detector") model = AutoModelForTokenClassification.from_pretrained("hfvladkon/bert_token_classification_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 908 Bytes
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"data": "/home/vladigur/qwen-bert-tagger-20260919/data/mix_v2_grammar",
"out": "/home/vladigur/qwen-bert-tagger-20260919/runs/NF2_native_mbert_final_overlap",
"base": "deepvk/RuModernBERT-base",
"qwen_tokenizer": "/home/vladigur/qwen-bert-tagger-20260919/assets/qwen_tokenizer",
"mapped_cache": "/home/vladigur/qwen-bert-tagger-20260919/cache/mapped_rumodernbert_base.safetensors",
"freeze_embeddings": "False",
"lr_emb": "None",
"max_len": "2048",
"stride": "256",
"epochs": "6.0",
"max_steps": "-1",
"lr": "3e-05",
"batch": "8",
"grad_accum": "1",
"class_weights": "None",
"grouped": "True",
"synth_weight": "0.3",
"no_synth": "False",
"no_corrected": "False",
"only_synth": "False",
"init_from": "/home/vladigur/qwen-bert-tagger-20260919/runs/NS1_native_mbert_stage1_wiki_e3/final",
"eval_limit": "None",
"select": "overlap_f1",
"seed": "13",
"attn": "sdpa",
"arch": "native"
} |