3oclock
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distilbert-imdb

@@ -6,197 +6,67 @@ metrics:
 - accuracy
 ---
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 - accuracy
 ---
+# Model Card for DistilBERT Fine-Tuned on IMDb Dataset
+This model is a fine-tuned version of `distilbert-base-uncased` on the IMDb movie reviews dataset. It is intended for binary sentiment classification (positive or negative) of movie reviews.
 ## Model Details
 ### Model Description
+This is the model card for a fine-tuned 🤗 transformers model on the IMDb dataset.
+- **Developed by:** [Your Name or Organization]
+- **Model type:** DistilBERT for Sequence Classification
+- **Language(s) (NLP):** English
+- **License:** [Specify License, e.g., Apache 2.0]
+- **Finetuned from model:** `distilbert-base-uncased`
+### Model Sources
+- **Repository:** [Link to the model repository]
+- **Paper [optional]:** [Link to any related paper]
+- **Demo [optional]:** [Link to any demo]
 ## Uses
 ### Direct Use
+This model can be used directly for sentiment analysis on movie reviews. It is best suited for classifying English-language text that is similar in nature to movie reviews.
 ### Downstream Use [optional]
+This model can be fine-tuned on other sentiment analysis tasks or adapted for tasks like text classification in domains similar to IMDb movie reviews.
 ### Out-of-Scope Use
+The model may not perform well on non-English text or text that is significantly different in style and content from the IMDb dataset (e.g., technical documents, social media posts).
 ## Bias, Risks, and Limitations
+### Bias
+The IMDb dataset primarily consists of English-language movie reviews and may not generalize well to other languages or types of reviews.
+### Risks
+Misclassification in sentiment analysis can lead to incorrect conclusions in applications relying on this model.
+### Limitations
+The model was trained on a dataset of movie reviews, so it may not perform as well on other types of text data.
+### Recommendations
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.
+## How to Get Started with the Model
+Use the code below to get started with the model:
+```python
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis", model="your-model-id")
+result = classifier("I loved this movie!")
+print(result)