Datasets:
Bitext
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README.md
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@@ -8,15 +8,15 @@ This dataset can be used to train chatbots on Large Language Models such as GPT,
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The dataset is parallel to our Evaluation dataset (see [Customer Service Tagged Evaluation Dataset for Intent Detection](https://github.com/bitext/customer-support-intent-detection-evaluation-dataset)). Both datasets can be used in conjunction to first train and then evaluate the accuracy provided by training. The main difference between the two datasets is the number of utterances:
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- The training dataset contains 4,
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- The evaluation dataset contains around 270,000 utterances (around 10,000 per intent)
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Both datasets share the rest of the specifications, so they can be used in conjunction. The training dataset has the following specs, shared with the evaluation dataset:
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- Customer Service domain
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Each utterance is tagged with entities/slots when applicable. Additionally, each utterance is enriched with tags that indicate the type of language variation that the utterance expresses. Examples include:
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Utterances and Linguistic Tags
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The dataset contains 4,
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The dataset also reflects commonly occurring linguistic phenomena of real-life chatbots, such as spelling mistakes, run-on words, punctuation errors…
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- end_offset: the ending position of the entity
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- category: the high-level semantic category for the intent
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- tags: different tags that reflect the types of language variations expressed in the utterance
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- response: an example expected response from the chatbot
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The dataset contains tags that reflect different language phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”
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- refund_amount
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- Intents: get_refund, track_refund
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(c) Bitext Innovations,
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The dataset is parallel to our Evaluation dataset (see [Customer Service Tagged Evaluation Dataset for Intent Detection](https://github.com/bitext/customer-support-intent-detection-evaluation-dataset)). Both datasets can be used in conjunction to first train and then evaluate the accuracy provided by training. The main difference between the two datasets is the number of utterances:
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- The training dataset contains 4,269 utterances (around 200 per intent)
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- The evaluation dataset contains around 270,000 utterances (around 10,000 per intent)
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Both datasets share the rest of the specifications, so they can be used in conjunction. The training dataset has the following specs, shared with the evaluation dataset:
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- Customer Service domain
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- 10 categories or intent groups
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- 20 intents assigned to one of the 10 categories
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- 6 entity/slot types
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Each utterance is tagged with entities/slots when applicable. Additionally, each utterance is enriched with tags that indicate the type of language variation that the utterance expresses. Examples include:
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Utterances and Linguistic Tags
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------------------------------------
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The dataset contains 4,269 training utterances, with 200 utterances per intent. It has been split into training (80%), validation (10%) and testing (10%) sets, preserving the distribution of intents and linguistic phenomena.
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The dataset also reflects commonly occurring linguistic phenomena of real-life chatbots, such as spelling mistakes, run-on words, punctuation errors…
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- end_offset: the ending position of the entity
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- category: the high-level semantic category for the intent
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- tags: different tags that reflect the types of language variations expressed in the utterance
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- response_type: identifier for tracking the composition, and version of chatbot responses.
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- response: an example expected response from the chatbot
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The dataset contains tags that reflect different language phenomena like colloquial or offensive language. So if an utterance for intent “cancel_order” contains the “COLLOQUIAL” tag, the utterance will express an informal language variation like: “can u cancel my order”
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- refund_amount
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- Intents: get_refund, track_refund
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(c) Bitext Innovations, 2023
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