Annotators
dimakarp1996 edited this page Jul 8, 2023
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Annotators are components (connectors/services) that annotate a given user's utterance.
An example of an annotator is NER: this annotator may return a dictionary with tokens and tags keys:
{"tokens": ["Paris"], "tags": ["I-LOC"]}
Another example is Sentiment Classification annotator. It can return a list of labels, e.g.:
["neutral", "speech"]
| Name | Requirements | Description |
|---|---|---|
| ASR | 40 MB RAM | calculates overall ASR confidence for a given utterance and grades it as either very low, low, medium, or high (for Amazon markup) |
| Badlisted Words | 150 MB RAM | detects words and phrases from the badlist |
| Combined Classification | 1.5 GB RAM, 3.5 GB GPU | BERT-based model including topic classification, dialog acts classification, sentiment, toxicity, emotion, factoid classification |
| COMeT Atomic | 2 GB RAM, 1.1 GB GPU | Commonsense prediction models COMeT Atomic |
| COMeT ConceptNet | 2 GB RAM, 1.1 GB GPU | Commonsense prediction models COMeT ConceptNet |
| Convers Evaluator Annotator | 1 GB RAM, 4.5 GB GPU | is trained on the Alexa Prize data from the previous competitions and predicts whether the candidate response is interesting, comprehensible, on-topic, engaging, or erroneous |
| Emotion Classification | 2.5 GB RAM | emotion classification annotator |
| Entity Detection | 1.5 GB RAM, 3.2 GB GPU | extracts entities and their types from utterances |
| Entity Linking | 2.5 GB RAM, 1.3 GB GPU | finds Wikidata entity ids for the entities detected with Entity Detection |
| Entity Storer | 220 MB RAM | a rule-based component, which stores entities from the user's and socialbot's utterances if opinion expression is detected with patterns or MIDAS Classifier and saves them along with the detected attitude to dialogue state |
| Fact Random | 50 MB RAM | returns random facts for the given entity (for entities from user utterance) |
| Fact Retrieval | 7.4 GB RAM, 1.2 GB GPU | extracts facts from Wikipedia and wikiHow |
| Intent Catcher | 1.7 GB RAM, 2.4 GB GPU | classifies user utterances into a number of predefined intents which are trained on a set of phrases and regexps |
| KBQA | 2 GB RAM, 1.4 GB GPU | answers user's factoid questions based on Wikidata KB |
| MIDAS Classification | 1.1 GB RAM, 4.5 GB GPU | BERT-based model trained on a semantic classes subset of MIDAS dataset |
| MIDAS Predictor | 30 MB RAM | BERT-based model trained on a semantic classes subset of MIDAS dataset |
| NER | 2.2 GB RAM, 5 GB GPU | extracts person names, names of locations, organizations from uncased text |
| News API Annotator | 80 MB RAM | extracts the latest news about entities or topics using the GNews API. DeepPavlov Dream deployments utilize our own API key. |
| Personality Catcher | 30 MB RAM | the skill is to change the system's personality description via chatting interface, it works as a system command, the response is system-like message |
| Prompt Selector | 50 MB RAM | Annotator utilizing Sentence Ranker to rank prompts and selecting N_SENTENCES_TO_RETURN most relevant prompts (based on questions provided in prompts) |
| Property Extraction | 6.3 GiB RAM | extracts user attributes from utterances |
| Rake Keywords | 40 MB RAM | extracts keywords from utterances with the help of RAKE algorithm |
| Relative Persona Extractor | 50 MB RAM | Annotator utilizing Sentence Ranker to rank persona sentences and selecting N_SENTENCES_TO_RETURN the most relevant sentences |
| Sentrewrite | 200 MB RAM | rewrites user's utterances by replacing pronouns with specific names that provide more useful information to downstream components |
| Sentseg | 1 GB RAM | allows us to handle long and complex user's utterances by splitting them into sentences and recovering punctuation |
| Spacy Nounphrases | 180 MB RAM | extracts nounphrases using Spacy and filters out generic ones |
| Speech Function Classifier | 1.1 GB RAM, 4.5 GB GPU | a hierarchical algorithm based on several linear models and a rule-based approach for the prediction of speech functions described by Eggins and Slade |
| Speech Function Predictor | 1.1 GB RAM, 4.5 GB GPU | yields probabilities of speech functions that can follow a speech function predicted by Speech Function Classifier |
| Spelling Preprocessing | 50 MB RAM | pattern-based component to rewrite different colloquial expressions to a more formal style of conversation |
| Topic Recommendation | 40 MB RAM | offers a topic for further conversation using the information about the discussed topics and user's preferences. Current version is based on Reddit personalities (see Dream Report for Alexa Prize 4). |
| Toxic Classification | 3.5 GB RAM, 3 GB GPU | Toxic classification model from Transformers specified as PRETRAINED_MODEL_NAME_OR_PATH |
| User Persona Extractor | 40 MB RAM | determines which age category the user belongs to based on some key words |
| Wiki Parser | 100 MB RAM | extracts Wikidata triplets for the entities detected with Entity Linking |
| Wiki Facts | 1.7 GB RAM | model that extracts related facts from Wikipedia and WikiHow pages |
| Name | Requirements | Description |
|---|---|---|
| Badlisted Words | 50 MB RAM | detects obscene Russian words from the badlist |
| Entity Detection | 5.5 GB RAM | extracts entities and their types from utterances |
| Entity Linking | 400 MB RAM | finds Wikidata entity ids for the entities detected with Entity Detection |
| Fact Retrieval | 6.5 GiB RAM, 1 GiB GPU | Аннотатор извлечения параграфов Википедии, релевантных истории диалога. |
| Intent Catcher | 900 MB RAM | classifies user utterances into a number of predefined intents which are trained on a set of phrases and regexps |
| NER | 1.7 GB RAM, 4.9 GB GPU | extracts person names, names of locations, organizations from uncased text using ruBert-based (pyTorch) model |
| Sentseg | 2.4 GB RAM, 4.9 GB GPU | recovers punctuation using ruBert-based (pyTorch) model and splits into sentences |
| Spacy Annotator | 250 MB RAM | token-wise annotations by Spacy |
| Spelling Preprocessing | 8 GB RAM | Russian Levenshtein correction model |
| Toxic Classification | 3.5 GB RAM, 3 GB GPU | Toxic classification model from Transformers specified as PRETRAINED_MODEL_NAME_OR_PATH |
| Wiki Parser | 100 MB RAM | extracts Wikidata triplets for the entities detected with Entity Linking |
| DialogRPT | 3.8 GB RAM, 2 GB GPU | DialogRPT model which is based on Russian DialoGPT by DeepPavlov and fine-tuned on Russian Pikabu Comment sequences |
TBD
- Annotators @ ReadTheDocs