Create README.md

README.md

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+## Dataset Summary
+
+Original source - [https://github.com/microsoft/OpenKP](https://github.com/microsoft/OpenKP)
+## Dataset Structure
+
+
+### Data Fields
+
+- **id**: unique identifier of the document.
+- **document**: Whitespace separated list of words in the document.
+- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
+- **extractive_keyphrases**: List of all the present keyphrases.
+- **abstractive_keyphrase**: List of all the absent keyphrases.
+
+
+### Data Splits
+
+|Split| #datapoints  |
+|--|--|
+| Train | 134894 |
+| Test | 6614 |
+| Validation | 6616 |
+
+
+## Usage
+
+### Full Dataset
+
+```python
+from datasets import load_dataset
+
+# get entire dataset
+dataset = load_dataset("midas/openkp", "raw")
+
+# sample from the train split
+print("Sample from training data split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+print("Document BIO Tags: ", train_sample["doc_bio_tags"])
+print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+
+# sample from the validation split
+print("Sample from validation data split")
+validation_sample = dataset["validation"][0]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
+print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+
+# sample from the test split
+print("Sample from test data split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Document BIO Tags: ", test_sample["doc_bio_tags"])
+print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+```
+**Output**
+
+```bash
+
+
+```
+
+### Keyphrase Extraction
+```python
+from datasets import load_dataset
+
+# get the dataset only for keyphrase extraction
+dataset = load_dataset("midas/openkp", "extraction")
+
+print("Samples for Keyphrase Extraction")
+
+# sample from the train split
+print("Sample from training data split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+print("Document BIO Tags: ", train_sample["doc_bio_tags"])
+print("\n-----------\n")
+
+# sample from the validation split
+print("Sample from validation data split")
+validation_sample = dataset["validation"][0]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
+print("\n-----------\n")
+
+# sample from the test split
+print("Sample from test data split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Document BIO Tags: ", test_sample["doc_bio_tags"])
+print("\n-----------\n")
+```
+
+### Keyphrase Generation
+```python
+# get the dataset only for keyphrase generation
+dataset = load_dataset("midas/openkp", "generation")
+
+print("Samples for Keyphrase Generation")
+
+# sample from the train split
+print("Sample from training data split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+
+# sample from the validation split
+print("Sample from validation data split")
+validation_sample = dataset["validation"][0]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+
+# sample from the test split
+print("Sample from test data split")
+test_sample = dataset["test"][0]
+print("Fields in the sample: ", [key for key in test_sample.keys()])
+print("Tokenized Document: ", test_sample["document"])
+print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
+print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
+print("\n-----------\n")
+```
+## Citation Information
+```
+@inproceedings{Xiong2019OpenDW,
+
+  title={Open Domain Web Keyphrase Extraction Beyond Language Modeling},
+
+  author={Lee Xiong and Chuan Hu and Chenyan Xiong and Daniel Fernando Campos and Arnold Overwijk},
+
+  booktitle={EMNLP},
+
+  year={2019}
+
+}
+```
+
+## Contributions
+Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset