Instructions to use Souvikcmsa/SentimentAnalysisDistillBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Souvikcmsa/SentimentAnalysisDistillBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Souvikcmsa/SentimentAnalysisDistillBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Souvikcmsa/SentimentAnalysisDistillBERT") model = AutoModelForSequenceClassification.from_pretrained("Souvikcmsa/SentimentAnalysisDistillBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5be5f00dcbca19378757ae8b6733f58c81147230cc4fb06887651950b23c0f14
- Size of remote file:
- 268 MB
- SHA256:
- 58d1ce066293338e96cd302c553abcdb158401f11f2e353aaf4b0e5a756dd1d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.