Instructions to use dexhrestha/Nepali-DistilBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexhrestha/Nepali-DistilBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dexhrestha/Nepali-DistilBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dexhrestha/Nepali-DistilBERT") model = AutoModelForSequenceClassification.from_pretrained("dexhrestha/Nepali-DistilBERT") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
DistilBERT model trained on OSCAR nepali corpus from huggingface datasets.
We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as postive,negative and neutral. However, since neutral labels exceeded the positive and negative tweets we decided to use only positive and negative tweets for ease of training.
LABEL_1 = negative LABEL_0 = positive
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