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README.md

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+---
+language: en
+license: mit
+dataset_info:
+  features:
+  - name: _id
+    dtype: string
+  - name: sentence
+    dtype: string
+  - name: target
+    dtype: string
+  - name: aspect
+    dtype: string
+  - name: score
+    dtype: float64
+  - name: type
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 119567
+    num_examples: 822
+  - name: valid
+    num_bytes: 17184
+    num_examples: 117
+  - name: test
+    num_bytes: 33728
+    num_examples: 234
+  download_size: 102225
+  dataset_size: 170479
+---
+# Dataset Name
+
+## Dataset Description
+
+This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively.
+
+## Dataset Structure
+
+- `_id`: ID of the data point
+- `sentence`: The sentence
+- `target`: The target of the sentiment
+- `aspect`: The aspect of the sentiment
+- `score`: The sentiment score
+- `type`: The type of the data point (headline or post)
+
+## Additional Information
+
+- Homepage: [FiQA Challenge](https://sites.google.com/view/fiqa/home)
+- Citation: [A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News](https://arxiv.org/pdf/2211.00083.pdf)
+
+## Downloading CSV
+```python
+from datasets import load_dataset
+
+# Load the dataset from the hub
+dataset = load_dataset("ChanceFocus/fiqa-sentiment-classification")
+
+# Save the dataset to a CSV file
+dataset["train"].to_csv("train.csv")
+dataset["valid"].to_csv("valid.csv")
+dataset["test"].to_csv("test.csv")
+```

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