Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use msperka/dictabert_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msperka/dictabert_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="msperka/dictabert_ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("msperka/dictabert_ner") model = AutoModelForTokenClassification.from_pretrained("msperka/dictabert_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9cd6bcc46e187a8a26b9421dd40aa3bc9f03ccdd36326f5e6e78c0818559ebc
- Size of remote file:
- 4.16 kB
- SHA256:
- 7716b4262e7277768a15f42fafae42546d45f3ea9b2d67763866d55fb693d279
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.