# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("LemiSt/code-segmentor-distilbert")
model = AutoModelForTokenClassification.from_pretrained("LemiSt/code-segmentor-distilbert", device_map="auto")Quick Links
This is a distilbert-base-multilingual-cased-Model fine-tuned with a NER objective to tag tokens based on whether they belong to a code block or natural language text. The dataset of 78210 examples was generated by randomly combining code and text blocks from other permissively-licensed datasets, with some examples containing only code and some only regular text.
The model achieves the following stats on the validation set:
| Metric | Value |
|---|---|
| Loss | 0.0788 |
| F1 Score | 0.8619 |
| Precision | 0.8362 |
| Recall | 0.8893 |
| Accuracy | 0.9792 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LemiSt/code-segmentor-distilbert")