Create README.md

README.md

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+---
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+task_categories:
+- feature-extraction
+- sentence-similarity
+pretty_name: AllNLI
+tags:
+- sentence-transformers
+dataset_info:
+- config_name: pair-class-distill
+  features:
+  - name: premise
+    dtype: string
+  - name: hypothesis
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': contradiction
+          '1': entailment
+          '2': neutral
+---
+
+# Dataset Card for AllNLI
+
+This dataset is a concatenation of the [SNLI](https://huggingface.co/datasets/stanfordnlp/snli) and [MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli) datasets.
+
+This is the same dataset as `sentence-transformers/all-nli` `pair-class` split;
+however, the label ids are not identical, and teacher scores have been added from `dleemiller/ModernCE-large-nli`.
+
+I have also added hashes for score lookup, since a lookup must be added into a custom loss function,
+if using the sentence transformers CrossEncoder trainer.
+
+The hashes were computed straightforwardly as follows:
+```
+df.hash = df.apply(lambda x: hashlib.md5(f"{x.premise}\n{x.hypothesis}".encode()).hexdigest(), axis=1)
+```
+