Update parquet files

.gitattributes

@@ -1,51 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,132 +0,0 @@
----
-language:
-- fa
-license: mit
-multilinguality:
-- monolingual
-size_categories:
-- 30k<n<50k
-task_categories:
-- question-answering
-- text2text-generation
-- text-generation
-task_ids: []
-pretty_name: SynTranFa
-tags:
-- conditional-text-generation
-- conversational-question-answering
----
-
-# SynTran-fa 
-Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:
-
-```python
-import datasets
-data = datasets.load_dataset('SLPL/syntran-fa', split="train")
-```
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-- [Dataset Creation](#dataset-creation)
-  - [Source Data](#source-data)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
- 
-- **Homepage:** [Sharif-SLPL](https://github.com/Sharif-SLPL)
-- **Repository:** [SynTran-fa](https://github.com/agp-internship/syntran-fa)
-- **Point of Contact:** [Sadra Sabouri](mailto:[email protected])
-
-### Dataset Summary
-
-Generating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).
-
-This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in [Source Data section](#source-data).
-
-The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
-
-### Supported Tasks and Leaderboards
-
-This dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf).
-
-### Languages
-
-+ Persian (fa)
-
-## Dataset Structure
-Each row of the dataset will look like something like the below:
-```json
-{
-  'id': 0,
-  'question': 'باشگاه هاکی ساوتهمپتون چه نام دارد؟',
-  'short_answer': 'باشگاه هاکی ساوتهمپتون',
-  'fluent_answer': 'باشگاه هاکی ساوتهمپتون باشگاه هاکی ساوتهمپتون نام دارد.',
-  'bert_loss': 1.110097069682014
-}
-```
-+ `id` : the entry id in dataset
-+ `question` : the question
-+ `short_answer` : the short answer corresponding to the `question` (the primary answer)
-+ `fluent_answer` : fluent (long) answer generated from both `question` and the `short_answer` (the secondary answer)
-+ `bert_loss` : the loss that [pars-bert](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) gives when inputting the `fluent_answer` to it. As it increases the sentence is more likely to be influent.
-
-Note: the dataset is sorted increasingly by the `bert_loss`, so first sentences are more likely to be fluent.
-
-### Data Splits
-
-Currently, the dataset just provided the `train` split. There would be a `test` split soon.
-
-## Dataset Creation
-
-### Source Data
-The source datasets that we used are as follows:
-
-+ [PersianQA](https://github.com/sajjjadayobi/PersianQA)
-+ [PersianQuAD](https://ieeexplore.ieee.org/document/9729745)
-
-#### Initial Data Collection and Normalization
-
-We extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-The dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.
-
-## Additional Information
-
-### Dataset Curators
-
-The dataset is gathered together completely in the Asr Gooyesh Pardaz company's summer internship under the supervision of Soroush Gooran, Prof. Hossein Sameti, and the mentorship of Sadra Sabouri. This project was Farhan Farsi's first internship project. 
-
-### Licensing Information
-
-MIT
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@farhaaaaa](https://github.com/farhaaaaa) and [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset.

data/train-00000-of-00001-3e7f957a24fef68a.parquet → SLPL--syntran-fa/parquet-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bdbf5332e5c2fe4d4cf3a65868aee21fc2046c862154498ce63bdd8cbb066606
-size 6677365
+oid sha256:ef27ce89d81d98d86d2797b00938dd3eb87bf387dea04766219dfed68f93e97a
+size 6814459

dataset_infos.json

@@ -1,52 +0,0 @@
-{"SLPL--syntran-fa": {
-    "description": "Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers.",
-    "citation": "",
-    "homepage": "[Sharif-SLPL](https://github.com/Sharif-SLPL)",
-    "license": "mit",
-    "features": {
-        "id": {
-            "dtype": "int64",
-            "id": null,
-            "_type": "Value"
-        },
-        "question": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "short_answer": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "fluent_answer": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "bert_loss": {
-            "dtype": "float64",
-            "id": null,
-            "_type": "Value"
-        }
-    },
-    "post_processed": null,
-    "supervised_keys": null,
-    "task_templates": null,
-    "builder_name": null,
-    "config_name": null,
-    "version": null,
-    "splits": {
-        "train": {
-            "name": "train",
-            "num_bytes": 11704035,
-            "num_examples": 48106,
-            "dataset_name": "syntran-fa"
-        }
-    },
-    "download_checksums": null,
-    "download_size": 6677365,
-    "post_processing_size": null,
-    "dataset_size": 11704035,
-    "size_in_bytes": 18381400
-}}