Instructions to use ayeshayasir7543458/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayeshayasir7543458/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ayeshayasir7543458/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayeshayasir7543458/results") model = AutoModelForSequenceClassification.from_pretrained("ayeshayasir7543458/results") - Notebooks
- Google Colab
- Kaggle
IMDB Sentiment Analysis Model
This model is a fine-tuned version of distilbert-base-uncased on the IMDB movie reviews dataset.
Model Details
- Base Model: distilbert-base-uncased
- Dataset: IMDB Movie Reviews
- Task: Sentiment Classification - Positive / Negative
Evaluation Results
- Accuracy: 0.893
- Precision: 0.8699
- Recall: 0.9180
- F1-Score: 0.8933
- Loss: 0.3018
How to use
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="ayeshayasir7543458/results")
print(classifier("This movie was amazing!"))
print(classifier("I hated this movie."))
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