Token Classification
Transformers
PyTorch
TensorFlow
JAX
ONNX
Safetensors
English
bert
Eval Results (legacy)
Instructions to use dslim/bert-base-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-base-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-base-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add root fast tokenizer serialization
#27
by michaelfeil - opened
Add tokenizer.json at the repository root for consumers that load the fast tokenizer serialization directly, including Infinity v3.
Converted from the original vocabulary and tokenizer configuration using AutoTokenizer with use_fast=True. The resulting JSON is identical to the existing onnx/tokenizer.json at this revision. No weights or tokenizer configuration are changed.
Validation: 156 comparisons of token IDs, offsets, token type IDs and attention masks covering casing, Unicode, sentence pairs, padding and left/right truncation. Infinity 3.0.0 GPU NER inference and all aggregation modes passed using this tokenizer.