Token Classification
Transformers
Safetensors
Ukrainian
roberta
ner
named-entity-recognition
pii
pii-detection
personal-data
anonymization
pseudonymization
de-identification
data-privacy
privacy
redaction
legal
legal-nlp
court-decisions
ukrainian
ukraine
synthetic-data
Eval Results (legacy)
Instructions to use davidmelash/wechsel_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidmelash/wechsel_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="davidmelash/wechsel_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("davidmelash/wechsel_base") model = AutoModelForTokenClassification.from_pretrained("davidmelash/wechsel_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 388 Bytes
db4c341 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "RobertaTokenizer",
"trim_offsets": true,
"unk_token": "<unk>"
}
|