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Update README.md

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  1. README.md +14 -8
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@@ -75,13 +75,16 @@ This dataset is taken from `marcel-gohsen/dstc3` and formatted with our [AutoInt
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  ```python
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  from datasets import load_dataset
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  from autointent import Dataset
 
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  # load original data
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  dstc3 = load_dataset("marcel-gohsen/dstc3")
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  # extract intent names
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  dstc3["test"] = dstc3["test"].filter(lambda example: example["transcript"] != "")
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- intent_names = sorted(set(name for intents in dstc3["test"]["intent"] for name in intents))
 
 
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  intent_names.remove("reqmore")
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  dstc3["test"].filter(lambda example: "reqmore" in example["intent"])
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  name_to_id = {name: i for i, name in enumerate(intent_names)}
@@ -90,20 +93,23 @@ name_to_id = {name: i for i, name in enumerate(intent_names)}
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  def transform(example: dict):
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  return {
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  "utterance": example["transcript"],
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- "label": [name_to_id[intent_name] for intent_name in example["intent"] if intent_name != "reqmore"],
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  }
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- dstc_converted = dstc3["test"].map(transform, remove_columns=dstc3["test"].features.keys())
 
 
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  # format to autointent.Dataset
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  intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
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  utterances = []
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- oos_utterances = []
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  for rec in dstc_converted.to_list():
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- if len(rec["label"]) == 0:
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  rec.pop("label")
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- oos_utterances.append(rec["utterance"])
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  else:
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  utterances.append(rec)
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- oos_records = [{"utterance": ut} for ut in set(oos_utterances)]
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- dstc_converted = Dataset.from_dict({"intents": intents, "train": utterances + oos_records})
 
 
 
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  ```
 
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  ```python
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  from datasets import load_dataset
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  from autointent import Dataset
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+ from autointent.context.data_handler import split_dataset
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  # load original data
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  dstc3 = load_dataset("marcel-gohsen/dstc3")
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  # extract intent names
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  dstc3["test"] = dstc3["test"].filter(lambda example: example["transcript"] != "")
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+ intent_names = sorted(
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+ set(name for intents in dstc3["test"]["intent"] for name in intents)
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+ )
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  intent_names.remove("reqmore")
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  dstc3["test"].filter(lambda example: "reqmore" in example["intent"])
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  name_to_id = {name: i for i, name in enumerate(intent_names)}
 
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  def transform(example: dict):
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  return {
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  "utterance": example["transcript"],
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+ "label": [int(name in example["intent"]) for name in intent_names],
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  }
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+ dstc_converted = dstc3["test"].map(
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+ transform, remove_columns=dstc3["test"].features.keys()
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+ )
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  # format to autointent.Dataset
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  intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
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  utterances = []
 
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  for rec in dstc_converted.to_list():
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+ if sum(rec["label"]) == 0:
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  rec.pop("label")
 
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  else:
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  utterances.append(rec)
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+
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+ dstc_converted = Dataset.from_dict({"intents": intents, "train": utterances})
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+ dstc_converted["train"], dstc_converted["test"] = split_dataset(
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+ dstc_converted, split="train", test_size=0.2, random_seed=42
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+ )
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  ```