Duplicate from Sp1786/multiclass-sentiment-analysis-dataset

Co-authored-by: Shahriar Parvez <[email protected]>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-classification
+- translation
+language:
+- en
+tags:
+- code
+pretty_name: multiclass-sentiment-analysis-dataset
+size_categories:
+- 10K<n<100K
+---
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** 
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### Dataset Summary
+
+This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]

create_dataset.py

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+from datasets import DatasetDict, load_dataset
+import csv
+import json
+
+def main():
+    for split in ["train", "val", "test"]:
+        input_file = csv.DictReader(open(f"{split}_df.csv"))
+
+        with open(f'{split}.jsonl', 'w') as fout:
+            for row in input_file:
+                fout.write(json.dumps({'id': row['id'], 'text': row['text'], 'label': row['label'], 'sentiment': row['sentiment']})+"\n")
+               
+
+    """
+    train_dset = load_dataset("csv", data_files="train_df.csv", split="train")
+    val_dset = load_dataset("csv", data_files="val_df.csv", split="val")
+    test_dset = load_dataset("csv", data_files="test_df.csv", split="test")
+    raw_dset = DatasetDict()
+    raw_dset["train"] = train_dset
+    raw_dset["test"] = test_dset
+    for split, dset in raw_dset.items():
+        dset.to_json(f"{split}.jsonl")
+    """
+
+if __name__ == "__main__":
+    main()