Instructions to use Tejas003/distillbert_base_uncased_amazon_review_sentiment_300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas003/distillbert_base_uncased_amazon_review_sentiment_300 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tejas003/distillbert_base_uncased_amazon_review_sentiment_300")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tejas003/distillbert_base_uncased_amazon_review_sentiment_300") model = AutoModelForSequenceClassification.from_pretrained("Tejas003/distillbert_base_uncased_amazon_review_sentiment_300") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Product Review Sentiment Classification
- Label0 - Negative
- Label1 - Positive
Trained so far on 20000 Balanced Positive and Negative Reviews
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