Instructions to use Hiveurban/dictabert-ner-handler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hiveurban/dictabert-ner-handler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Hiveurban/dictabert-ner-handler")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Hiveurban/dictabert-ner-handler") model = AutoModelForTokenClassification.from_pretrained("Hiveurban/dictabert-ner-handler", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 843 Bytes
4aa700d a640db1 8c22397 4aa700d a640db1 4aa700d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
from typing import Dict, List, Any
from tokenizers.decoders import WordPiece
class EndpointHandler:
def __init__(self, path="."):
model = AutoModelForTokenClassification.from_pretrained(path)
tokenizer = AutoTokenizer.from_pretrained(path)
self.pipeline = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy='simple')
self.pipeline.tokenizer.backend_tokenizer.decoder = WordPiece()
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
return self.pipeline(data['inputs'])
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