Datasets:

Mouwiya
/

imdb_reviews_with_labels

@@ -41,113 +41,30 @@ This dataset card aims to be a base template for new datasets. It has been gener
 ### Dataset Description
 <!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- In this Task , we conducted one-shot sentiment analysis on a subset of the IMDb movie reviews dataset using multiple language models. The goal was to predict the sentiment (positive or negative) of movie reviews without fine-tuning the models on the specific task.
 We utilized three different pre-trained language models for zero-shot classification: BART-large, DistilBERT-base, and RoBERTa-base. For each model, we generated predicted sentiment labels for a subset of 100 movie reviews from the IMDb dataset. The reviews were randomly sampled, ensuring a diverse representation of sentiments.
 After processing the reviews through each model, we saved the predicted sentiment labels alongside the original reviews in a CSV file named "imdb_reviews_with_labels.csv". This file contains the reviews and the predicted sentiment labels for each model.
-Additionally, we uploaded both the dataset and the CSV file to the Hugging Face Hub for easy access and sharing. The dataset can be found at the following link after uploading:  -->
-### Direct Use
-<!-- This task demonstrates the effectiveness of zero-shot classification using pre-trained language models for sentiment analysis tasks and provides a valuable resource for further analysis and experimentation. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
-## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
-## Dataset Creation
-### Curation Rationale
-<!-- This task demonstrates the effectiveness of zero-shot classification using pre-trained language models for sentiment analysis tasks and provides a valuable resource for further analysis and experimentation. -->
-[More Information Needed]
-### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 [More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 ## Citation [optional]
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
 **APA:**
@@ -157,16 +74,3 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Dataset Card Authors [optional]
-[More Information Needed]
-## Dataset Card Contact
-[More Information Needed]

 ### Dataset Description
 <!-- Provide a longer summary of what this dataset is. -->
+ In this Task , we conducted one-shot sentiment analysis on a subset of the IMDb movie reviews dataset using multiple language models. The goal was to predict the sentiment (positive or negative) of movie reviews without fine-tuning the models on the specific task.
 We utilized three different pre-trained language models for zero-shot classification: BART-large, DistilBERT-base, and RoBERTa-base. For each model, we generated predicted sentiment labels for a subset of 100 movie reviews from the IMDb dataset. The reviews were randomly sampled, ensuring a diverse representation of sentiments.
 After processing the reviews through each model, we saved the predicted sentiment labels alongside the original reviews in a CSV file named "imdb_reviews_with_labels.csv". This file contains the reviews and the predicted sentiment labels for each model.
+Additionally, we uploaded both the dataset and the CSV file to the Hugging Face Hub for easy access and sharing. The dataset can be found at the following link after uploading:
+https://huggingface.co/datasets/Mouwiya/imdb_reviews_with_labels
+This task demonstrates the effectiveness of zero-shot classification using pre-trained language models for sentiment analysis tasks and provides a valuable resource for further analysis and experimentation.
+- **Curated by:** [Mouwiya S. A. AlQaisieh]
+<!--  -->
 [More Information Needed]
 ## Citation [optional]
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 **APA:**
 <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->