Create README.md

README.md

@@ -1,27 +1,25 @@
 ---
-dataset_info:
-  features:
-  - name: labels
-    dtype: string
-  - name: sentence1
-    dtype: string
-  - name: sentence2
-    dtype: string
-  - name: task
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1027206414.0
-    num_examples: 2095288
-  - name: validation
-    num_bytes: 47771886.0
-    num_examples: 63296
-  - name: test
-    num_bytes: 48695495.0
-    num_examples: 65919
-  download_size: 562394652
-  dataset_size: 1123673795.0
+license: apache-2.0
+task_categories:
+- zero-shot-classification
+- text-classification
+task_ids:
+- natural-language-inference
+language:
+- en
 ---
-# Dataset Card for "zero-shot-label-nli"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+[tasksource](https://github.com/sileod/tasksource) classification tasks recasted as natural language inference. 
+This dataset is intended to improve label understanding in [zero-shot classification HF pipelines](https://huggingface.co/docs/transformers/main/main_classes/pipelines#transformers.ZeroShotClassificationPipeline
+).
+
+Inputs that are text pairs are separated by a newline (\n).
+```python
+from transformers import pipeline
+classifier = pipeline(model="sileod/deberta-v3-base-tasksource-nli")
+classifier(
+    "I have a problem with my iphone that needs to be resolved asap!!",
+    candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
+)
+```
+`sileod/deberta-v3-base-tasksource-nli` will include `label-nli` in its training mix (a relatively small portion, to keep the model general, but note that nli models work for label-like zero shot classification without specific supervision (https://aclanthology.org/D19-1404.pdf).