The purpose built models shipped with the TAO Toolkit - Conversational AI package can be used directly in tasks like answering questions across multiple domains, improving sentence semantics and more or can be re-trained or fine tuned to deploy a Conversational AI like a Virtual Assistant to service customers in varied fields like financial services, legal services, insurance, customer service and many more!
The table below shows the network architecture and the application area in which the model is trained. These models can be re-trained or fine tuned to change the domain/language according to the user’s requirements
Model Name |
Base Architecture |
Dataset |
Purpose |
|---|---|---|---|
| Speech to Text English Jasper | Jasper | ASR Set 1.2 with Noisy (profiles: room reverb, echo, wind, keyboard, baby crying) - 7K hours | Speech Transcription |
| Speech to Text English QuartzNet | Quartznet | ASR Set 1.2 | Speech Transcription |
| Speech to Text English CitriNet | CitriNet | ASR Set 1.4 | Speech Transcription |
| Speech to Text English Conformer | Conformer | ASR Set 1.4 | Speech Transcription |
| Question Answering SQUAD2.0 Bert | BERT | SQuAD 2.0 | Answering questions in SQuADv2.0, a reading comprehension dataset consisting of Wikipedia articles. |
| Question Answering SQUAD2.0 Bert - Large | BERT Large | SQuAD 2.0 | Answering questions in SQuADv2.0, a reading comprehension dataset consisting of Wikipedia articles. |
| Question Answering SQUAD2.0 Megatron | Megatron | SQuAD 2.0 | Answering questions in SQuADv2.0, a reading comprehension dataset consisting of Wikipedia articles. |
| Named Entity Recognition Bert | BERT | GMB (Gronigen Meaning Book) | Identifying entities in a given text (Supported Categories: Geographical Entity, Organization, Person , Geopolitical Entity, Time Indicator, Natural Phenomenon/Event) |
| Joint Intent and Slot Classification Bert | BERT | Proprietary | Classifying an intent and detecting all relevant slots (Entities) for this Intent in a query. Intent and slot names are usually task specific. This model recognizes weather related intents like weather, temperature, rainfall etc. and entities like place, time, unit of temperature etc. For a comprehensive list, please check the corresponding model card. |
| Punctuation and Capitalization Bert | BERT | Tatoeba sentences, Books from the Project Gutenberg that were used as part of the LibriSpeech corpus, Transcripts from Fisher English Training Speech | Add punctuation and capitalization to text. |
| Domain Classification English Bert | BERT | Proprietary | For domain classification of queries into the 4 supported domains: weather, meteorology, personality, and none. |