Token Classification
PEFT
Safetensors
English
Hindi
lora
hinglish
language-identification
education
code-mixing
Instructions to use persuasivepost/eduhinglish-muril-lid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use persuasivepost/eduhinglish-muril-lid with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("/content/drive/MyDrive/EduHinglish/models/muril_lid_v3") model = PeftModel.from_pretrained(base_model, "persuasivepost/eduhinglish-muril-lid") - Notebooks
- Google Colab
- Kaggle
EduHinglish β MuRIL word-level LID (LoRA)
Token-level language identification for Hinglish text. Labels: HI, EN, NE, UNIV, MIX. Part of the EduHinglish pipeline (K.J. Somaiya College of Engineering).
from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
base = AutoModelForTokenClassification.from_pretrained("google/muril-base-cased", num_labels=5)
model = PeftModel.from_pretrained(base, "persuasivepost/eduhinglish-muril-lid")
tok = AutoTokenizer.from_pretrained("google/muril-base-cased")
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Model tree for persuasivepost/eduhinglish-muril-lid
Base model
google/muril-base-cased