Add FSPC V1.0 Chinese classical poetry sentiment dataset

.gitattributes

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+test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text
+train-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: poem_id
+      dtype: int64
+    - name: poet
+      dtype: string
+    - name: dynasty
+      dtype: string
+    - name: title
+      dtype: string
+    - name: text
+      dtype: string
+    - name: setiments
+      dtype: string
+  splits:
+    - name: train
+      num_bytes: 474283
+      num_examples: 4000
+    - name: test
+      num_bytes: 127061
+      num_examples: 1000
+  download_size: 1865728
+  dataset_size: 601344
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: train-*.parquet
+      - split: test
+        path: test-*.parquet
+---
+
+# FSPC - Fine-grained Sentimental Poetry Corpus
+
+FSPC is a manually-labelled Fine-grained Sentiment Poetry Corpus. Each poem and each line is annotated into 5 classes, namely negative, implicit negative, neutral, implicit positive and positive.
+
+## Dataset Information
+
+- **Version**: V1.0
+- **Number of poems**: 5,000
+- **Sentiment Classes**: 
+  - 1: negative
+  - 2: implicit negative
+  - 3: neutral
+  - 4: implicit positive
+  - 5: positive
+
+## Data Format
+
+The dataset is stored in Parquet format with the following fields:
+
+- `id`: Unique identifier (poem_id as string)
+- `poem_id`: Poem sequence number
+- `poet`: Poet name
+- `dynasty`: Dynasty name (e.g., "唐", "宋")
+- `title`: Poem title
+- `text`: Full poem text with lines separated by newline characters (original "|" replaced with "
+")
+- `setiments`: JSON string containing sentiment labels in original format: `{"holistic": "1", "line1": "1", "line2": "1", "line3": "2", "line4": "2"}`
+
+The dataset is split into train (80%) and test (20%) sets with stratified sampling based on holistic sentiment to ensure balanced distribution.
+
+## Citation
+
+If you use this corpus, please cite the following paper:
+
+```
+@inproceedings{chensentiment:19,
+    author  = {Huimin Chen and Xiaoyuan Yi and Maosong Sun and Cheng Yang and Wenhao Li and Zhipeng Guo},
+    title   = {Sentiment-Controllable Chinese Poetry Generation},
+    year    = "2019",
+    booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence},
+    address = {Macao, China}  
+}
+```
+
+## Original Dataset
+
+The original dataset can be found in JSON format where each line is a poem with the following structure:
+
+```json
+{
+    "poet": "韦庄", 
+    "poem": "自有春愁正断魂|不堪芳草思王孙|落花寂寂黄昏雨|深院无人独倚门", 
+    "dynasty": "唐", 
+    "setiments": {"holistic": "1", "line1": "1", "line2": "1", "line3": "2", "line4": "2"}, 
+    "title": "春愁"
+}
+```
+
+Lines and sentiments in a poem are split by "|". The sentiments dict contains the holistic sentiment of whole poem, sentiment of the first line, ..., sentiment of the fourth line.

dataset_infos.yaml

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+configs:
+- config_name: default
+  data_files:
+  - path: train-*.parquet
+    split: train
+  - path: test-*.parquet
+    split: test
+dataset_info:
+  configs:
+  - config_name: default
+    features:
+    - name: id
+      dtype: string
+    - name: poem_id
+      dtype: int64
+    - name: poet
+      dtype: string
+    - name: dynasty
+      dtype: string
+    - name: title
+      dtype: string
+    - name: text
+      dtype: string
+    - name: setiments
+      dtype: string
+    splits:
+    - name: train
+      num_bytes: 474283
+      num_examples: 4000
+    - name: test
+      num_bytes: 127061
+      num_examples: 1000
+    download_size: 1865728
+    dataset_size: 601344

test-00000-of-00001.parquet

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+oid sha256:bfcf70ab7099826752c805a8e1571e297ace1399b2908e5a3b17fa6381456891
+size 127061

train-00000-of-00001.parquet

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