Token Classification
Transformers
ONNX
Safetensors
English
bert
asr-post-processing
paragraph-segmentation
dictation
voice-notes
on-device
Instructions to use danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks") model = AutoModelForTokenClassification.from_pretrained("danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks/resolve/main/tokenizer.json
- Command line
-
hf download hf://danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/danielrosehill/MiniLM-L12-ASR-Paragraph-Breaks/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.