Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,111 @@
+# ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
+
+ContractNLI is a dataset for document-level natural language inference (NLI) on contracts whose goal is to automate/support a time-consuming procedure of contract review.
+In this task, a system is given a set of hypotheses (such as "Some obligations of Agreement may survive termination.") and a contract, and it is asked to classify whether each hypothesis is _entailed by_, _contradicting to_ or _not mentioned by_ (neutral to) the contract as well as identifying _evidence_ for the decision as spans in the contract.
+
+ContractNLI is the first dataset to utilize NLI for contracts and is also the largest corpus of annotated contracts (as of September 2021).
+ContractNLI is an interesting challenge to work on from a machine learning perspective (the label distribution is imbalanced and it is naturally multi-task, all the while training data being scarce) and from a linguistic perspective (linguistic characteristics of contracts, particularly negations by exceptions, make the problem difficult).
+
+Details of ContractNLI can be found in our paper that was published in "Findings of EMNLP 2021".
+If you have a question regarding our dataset, you can contact us by emailing koreeda@stanford.edu or by creating an issue in this repository.
+
+## Dataset specification
+
+More formally, the task consists of:
+* **Natural language inference (NLI)**: Document-level three-class classification (one of `Entailment`, `Contradiction` or `NotMentioned`).
+* **Evidence identification**: Multi-label binary classification over _span_s, where a _span_ is a sentence or a list item within a sentence. This is only defined when NLI label is either `Entailment` or `Contradiction`. Evidence spans need not be contiguous but need to be comprehensively identified where they are redundant.
+
+We have 17 hypotheses annotated on 607 non-disclosure agreements (NDAs).
+The hypotheses are fixed throughout all the contracts including the test dataset.
+
+Our dataset is provided as JSON files.
+
+```json
+{
+  "documents": [
+    {
+      "id": 1,
+      "file_name": "example.pdf",
+      "text": "NON-DISCLOSURE AGREEMENT\nThis NON-DISCLOSURE AGREEMENT (\"Agreement\") is entered into this ...",
+      "document_type": "search-pdf",
+      "url": "https://examplecontract.com/example.pdf",
+      "spans": [
+        [0, 24],
+        [25, 89],
+        ...
+      ],
+      "annotation_sets": [
+        {
+          "annotations": {
+            "nda-1": {
+              "choice": "Entailment",
+              "spans": [
+                12,
+                13,
+                91
+              ]
+            },
+            "nda-2": {
+              "choice": "NotMentioned",
+              "spans": []
+            },
+            ...
+          }
+        }
+      ]
+    },
+    ...
+  ],
+  "labels": {
+    "nda-1": {
+      "short_description": "Explicit identification",
+      "hypothesis": "All Confidential Information shall be expressly identified by the Disclosing Party."
+    },
+    ...
+  }
+}
+```
+
+The core information in our dataset is:
+* `text`: The full document text
+* `spans`: List of spans as pairs of the start and end character indices.
+* `annotation_sets`: It is provided as a list to accommodate multiple annotations per document. Since we only have a single annotation for each document, you may safely access the appropriate annotation by `document['annotation_sets'][0]['annotations']`.
+* `annotations`: Each key represents a hypothesis key. `choice` is either `Entailment`, `Contradiction` or `NotMentioned`. `spans` is given as indices of `spans` above. `spans` is empty when `choice` is `NotMentioned`.
+* `labels`: Each key represents a hypothesis key. `hypothesis` is the hypothesis text that should be used in NLI.
+
+The JSON file comes with supplemental information. Users may simply ignore the information if you are only interested in developing machine learning systems.
+* `id`: A unique ID throughout train, development and test datasets.
+* `file_name`: The filename of the original document in the dataset zip file.
+* `document_type`: One of `search-pdf` (a PDF from a search engine), `sec-text` (a text file from SEC filing) or `sec-html` (an HTML file from SEC filing).
+* `url`: The URL that we obtained the document from.
+
+
+## Baseline system
+
+In our paper, we introduced Span NLI BERT, a strong baseline for our task.
+It (1) makes the problem of evidence identification easier by modeling the problem as multi-label classification over spans instead of trying to predict the start and end tokens, and (b) introduces more sophisticated context segmentation to deal with long documents.
+We showed in our paper that Span NLI BERT significantly outperforms the existing models.
+
+You can find the implementation of Span NLI BERT in [another repository](https://github.com/stanfordnlp/contract-nli-bert).
+
+## License
+
+Our dataset is released under CC BY 4.0.
+Please refer attached "[LICENSE](./LICENSE)" or https://creativecommons.org/licenses/by/4.0/ for the exact terms.
+
+When you use our dataset in your work, please cite our paper:
+
+```bibtex
+@inproceedings{koreeda-manning-2021-contractnli,
+    title = "ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts",
+    author = "Koreeda, Yuta  and
+      Manning, Christopher D.",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    year = "2021",
+    publisher = "Association for Computational Linguistics"
+}
+```
+
+## Changelog and release note
+
+* 10/5/2021: Initial release