Update README.md

README.md

@@ -1,3 +1,83 @@
 ---
 license: cc-by-nc-4.0
+language:
+- en
+tags:
+- instruction-finetuning
+pretty_name: Tapir-Cleaned
+task_categories:
+- text-generation
+size_categories:
+- 100K<n<1M
 ---
+# Dataset Card for Tapir-Cleaned
+
+This is a revised version of the DAISLab dataset of IFTTT rules, which has been thoroughly cleaned, scored, and adjusted for the purpose of instruction-tuning.
+
+## Tapir Dataset Summary
+
+Tapir is a subset of the larger DAISLab dataset, which comprises 242,480 recipes extracted from the IFTTT platform. 
+
+After a thorough cleaning process that involved the removal of redundant and inconsistent recipes, the refined dataset was condensed to include 116,862 high-quality recipes. 
+
+This curated set of instruction data is particularly useful for conducting instruction-tuning exercises for language models, 
+allowing them to more accurately follow instructions and achieve superior performance.
+
+The last version of Tapir includes a correlation score that helps to identify the most appropriate description-rule pairs for instruction tuning. 
+Description-rule pairs with a score greater than 0.75 are deemed good enough and are prioritized for further analysis and tuning.
+
+### Supported Tasks and Leaderboards
+
+The Tapir dataset designed for instruction training pretrained language models
+
+### Languages
+
+The data in Tapir are mainly in English (BCP-47 en).
+
+# Dataset Structure
+
+### Data Instances
+
+```json
+{
+    "instruction":"From the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.",
+    "input":"If lostphone is texted to my phone the volume will turn up to 100 so I can find it.",
+    "output":"IF Android SMS New SMS received matches search THEN Android Device Set ringtone volume",
+    "score":"0.804322",
+    "text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nFrom the description of a rule: identify the 'trigger', identify the 'action', write a IF 'trigger' THEN 'action' rule.\n\n### Input:\nIf lostphone is texted to my phone the volume will turn up to 100 so I can find it.\n\n### Response:\nIF Android SMS New SMS received matches search THEN Android Device Set ringtone volume",
+}
+```
+
+### Data Fields
+
+The data fields are as follows:
+
+* `instruction`: describes the task the model should perform.
+* `input`: context or input for the task.  Each of the 68K input is unique.
+* `output`: the answer taken from the original Tapir Dataset formatted as an IFTTT recipe.
+* `score`: the correlation score obtained via BertForNextSentencePrediction 
+* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors of Alpaca for fine-tuning their models.
+
+### Data Splits
+
+|               | train |
+|---------------|------:|
+| tapir         | 116862 |
+
+### Licensing Information
+
+The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
+
+
+### Citation Information
+
+```
+@misc{tapir,
+  author = {Mattia Limone, Gaetano Cimino, Annunziata Elefante},
+  title = {TAPIR: Trigger Action Platform for Information Retrieval},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/MattiaLimone/ifttt_recommendation_system}},
+}
+```