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Merge pull request #165 from deepmipt/dev
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Release v0.1.9
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@dilyararimovna
dilyararimovna committed May 27, 2022
2 parents de510bc + 726d80c commit ab2dcbd
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2 changes: 2 additions & 0 deletions .env
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Expand Up @@ -29,3 +29,5 @@ WIKIDATA_DIALOGUE_SERVICE_URL=http://wikidata-dial-service:8092/model
NEWS_API_ANNOTATOR_URL=http://news-api-annotator:8112/respond
WIKI_FACTS_URL=http://wiki-facts:8116/respond
FACT_RANDOM_SERVICE_URL=http://fact-random:8119/respond
INFILLING_SERVICE_URL=http://infilling:8122/respond

6 changes: 6 additions & 0 deletions README.md
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Expand Up @@ -187,6 +187,12 @@ Dream Architecture is presented in the following image:
| User Persona Extractor | 40 MiB RAM | determines which age category the user belongs to based on some key words |
| Wiki parser | 100 MiB RAM | extracts Wikidata triplets for the entities detected with Entity Linking |

## Services
| Name | Requirements | Description |
|---------------------------|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| DialoGPT | 1.3 GiB RAM, 1 GiB GPU | generative service based on Transformers generative model, the model is set in docker compose argument `PRETRAINED_MODEL_NAME_OR_PATH` (for example, `microsoft/DialoGPT-small` with 0.2-0.5 sec on GPU) |
| Infilling | 1.7 GiB RAM, 1 GiB GPU | generative service based on Infilling model, for the given utterance returns utterance where `_` from original text is replaced with generated tokens |

## Skills
| Name | Requirements | Description |
|---------------------------|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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4 changes: 4 additions & 0 deletions annotators/IntentCatcherTransformers/.gitignore
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metrics/*
data/*
*.ipynb
29 changes: 29 additions & 0 deletions annotators/IntentCatcherTransformers/Dockerfile
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FROM deeppavlov/base-gpu:0.17.2

RUN apt-key del 7fa2af80 && \
rm -f /etc/apt/sources.list.d/cuda*.list && \
wget -q https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb && \
dpkg -i cuda-keyring_1.0-1_all.deb

RUN apt-get update && apt-get install -y --allow-unauthenticated wget && rm -rf /var/lib/apt/lists/*

WORKDIR /src

ARG CONFIG_NAME
ENV CONFIG_NAME ${CONFIG_NAME}
ARG SERVICE_PORT
ENV SERVICE_PORT ${SERVICE_PORT}

COPY annotators/IntentCatcherTransformers/requirements.txt /src/requirements.txt
RUN pip install -r /src/requirements.txt

COPY ./common/ ./common/
COPY annotators/IntentCatcherTransformers/ /src
WORKDIR /src

RUN python -m deeppavlov install ${CONFIG_NAME}
RUN python -m deeppavlov download ${CONFIG_NAME}
RUN python train_model_if_not_exist.py


CMD gunicorn --workers=1 server:app -b 0.0.0.0:${SERVICE_PORT} --timeout=300
13 changes: 13 additions & 0 deletions annotators/IntentCatcherTransformers/README.md
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## IntentCatcher based on Transformers


English version was trained on `intent_phrases.json` dataset using `DeepPavlov` library via command:
```
python -m deeppavlov train intents_model_dp_config.json
```

It consumes 3.5Gb GPU RAM during fine-tuning. Classification results after 5 epochs are the following:
```json
{"train": {"eval_examples_count": 209297, "metrics": {"accuracy": 0.9997, "f1_weighted": 1.0, "f1_macro": 0.9999, "roc_auc": 1.0}, "time_spent": "0:03:46"}}
{"valid": {"eval_examples_count": 52325, "metrics": {"accuracy": 0.9995, "f1_weighted": 0.9999, "f1_macro": 0.9999, "roc_auc": 1.0}, "time_spent": "0:00:57"}}
```
1,594 changes: 1,594 additions & 0 deletions annotators/IntentCatcherTransformers/intent_phrases.json
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109 changes: 109 additions & 0 deletions annotators/IntentCatcherTransformers/intents_dataset_reader.py
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from collections import OrderedDict
from itertools import chain
from pathlib import Path
from random import shuffle
from typing import Optional

import json
from deeppavlov.core.data.dataset_reader import DatasetReader
from xeger import Xeger


class IntentsJsonReader(DatasetReader):
"""
Class provides reading intents dataset in .json format:
```json
{
"intent_phrases": {
"intent_0": {
"phrases": [
"(alexa ){0,1}(hi|hello)(( there)|( alexa)){0,1}"
],
"reg_phrases": [
"hi",
"hello"
],
"punctuation": [
".",
"!"
]
}
}
}
```
to make it compatible with classification models in DeepPavlov pipelines:
```json
[
("alexa hi", "intent_0"),
...,
]
```
"""

@staticmethod
def generate_phrases(template_re, punctuation, limit=2500):
x = Xeger(limit=limit)
phrases = []
for regex in template_re:
try:
phrases += list({x.xeger(regex) for _ in range(limit)})
except Exception as e:
print(e)
print(regex)
raise e
phrases = [phrases] + [[phrase + punct for phrase in phrases] for punct in punctuation]
return list(chain.from_iterable(phrases))

def read(self, data_path: str, generated_data_path: Optional[str] = None, *args, **kwargs) -> dict:
"""
Read dataset from `data_path` file with extension `.json`
Args:
data_path: file with `.json` extension
Returns:
dictionary with data samples.
Each field of dictionary is a list of tuples (x_i, y_i)
where `x_i` is a text sample, `y_i` is a class name
"""
data_types = ["train", "valid", "test"]
data = {data_type: [] for data_type in data_types}

for data_type in data_types:
file_name = kwargs.get(data_type, f"{data_type}.json")
if file_name is None:
continue

file = Path(data_path).joinpath(file_name)
if file.exists():
if generated_data_path and Path(generated_data_path).joinpath(file_name).exists():
with open(Path(generated_data_path).joinpath(file_name), "r") as fp:
data[data_type] = json.load(fp)
else:
with open(file, "r") as fp:
all_data = json.load(fp)
intent_phrases = OrderedDict(all_data["intent_phrases"])
random_phrases = all_data["random_phrases"]
random_phrases = self.generate_phrases(random_phrases["phrases"], random_phrases["punctuation"])

intent_data = {}
for intent, intent_samples in intent_phrases.items():
phrases = self.generate_phrases(intent_samples["phrases"], intent_samples["punctuation"])
intent_data[intent] = {
"generated_phrases": phrases,
"num_punctuation": len(intent_samples["punctuation"]),
}

data[data_type] = [
(gen_phrase, [intent])
for intent in intent_phrases.keys()
for gen_phrase in intent_data[intent]["generated_phrases"]
]
data[data_type] += [(gen_phrase, []) for gen_phrase in random_phrases]
shuffle(data[data_type])
if generated_data_path:
Path(generated_data_path).mkdir(exist_ok=True)
with open(Path(generated_data_path).joinpath(file_name), "w") as fp:
json.dump(data[data_type], fp, indent=2)
elif data_type == "train":
raise FileNotFoundError(f"Train file `{file_name}` is not provided in `{data_path}`.")

return data
190 changes: 190 additions & 0 deletions annotators/IntentCatcherTransformers/intents_model_dp_config.json
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{
"dataset_reader": {
"class_name": "intents_dataset_reader:IntentsJsonReader",
"data_path": "./",
"train": "intent_phrases.json",
"generated_data_path": "./generated_data"
},
"dataset_iterator": {
"class_name": "basic_classification_iterator",
"seed": 42,
"split_seed": 23,
"field_to_split": "train",
"split_fields": [
"train",
"valid"
],
"split_proportions": [
0.8,
0.2
]
},
"chainer": {
"in": [
"x"
],
"in_y": [
"y"
],
"pipe": [
{
"class_name": "torch_transformers_preprocessor",
"vocab_file": "{TRANSFORMER}",
"do_lower_case": true,
"max_seq_length": 64,
"in": [
"x"
],
"out": [
"bert_features"
]
},
{
"id": "classes_vocab",
"class_name": "simple_vocab",
"fit_on": [
"y"
],
"save_path": "{MODEL_PATH}/classes.dict",
"load_path": "{MODEL_PATH}/classes.dict",
"in": [
"y"
],
"out": [
"y_ids"
]
},
{
"id": "my_one_hotter",
"in": [
"y_ids"
],
"out": [
"y_onehot"
],
"class_name": "one_hotter",
"depth": "#classes_vocab.len",
"single_vector": true
},
{
"class_name": "torch_transformers_classifier",
"n_classes": "#classes_vocab.len",
"return_probas": true,
"one_hot_labels": true,
"multilabel": true,
"pretrained_bert": "{TRANSFORMER}",
"save_path": "{MODEL_PATH}/model",
"load_path": "{MODEL_PATH}/model",
"optimizer": "AdamW",
"optimizer_parameters": {
"lr": 1e-05
},
"learning_rate_drop_patience": 5,
"learning_rate_drop_div": 2.0,
"in": [
"bert_features"
],
"in_y": [
"y_onehot"
],
"out": [
"y_pred_probas"
]
},
{
"in": [
"y_pred_probas"
],
"out": [
"y_pred_ids"
],
"class_name": "proba2labels",
"max_proba": false,
"confidence_threshold": 0.5
},
{
"ref": "my_one_hotter",
"in": [
"y_pred_ids"
],
"out": [
"y_pred_onehot"
]
},
{
"in": [
"y_pred_ids"
],
"out": [
"y_pred_labels"
],
"ref": "classes_vocab"
}
],
"out": [
"y_pred_labels",
"y_pred_probas"
]
},
"train": {
"epochs": 5,
"batch_size": 64,
"metrics": [
{
"name": "accuracy",
"inputs": [
"y",
"y_pred_labels"
]
},
{
"name": "f1_weighted",
"inputs": [
"y_onehot",
"y_pred_onehot"
]
},
{
"name": "f1_macro",
"inputs": [
"y_onehot",
"y_pred_onehot"
]
},
{
"name": "roc_auc",
"inputs": [
"y_onehot",
"y_pred_probas"
]
}
],
"validation_patience": 5,
"val_every_n_epochs": 1,
"log_every_n_epochs": 1,
"show_examples": false,
"evaluation_targets": [
"train",
"valid"
],
"class_name": "torch_trainer"
},
"metadata": {
"imports": [
"intents_dataset_reader"
],
"variables": {
"TRANSFORMER": "distilbert-base-uncased",
"ROOT_PATH": "~/.deeppavlov",
"DOWNLOADS_PATH": "{ROOT_PATH}/downloads",
"MODELS_PATH": "{ROOT_PATH}/models",
"MODEL_PATH": "{MODELS_PATH}/classifiers/intents_model_v2"
},
"download": [
{
"url": "http://files.deeppavlov.ai/deeppavlov_data/intents_model_v2.tar.gz",
"subdir": "{MODELS_PATH}/classifiers"
}
]
}
}
15 changes: 15 additions & 0 deletions annotators/IntentCatcherTransformers/requirements.txt
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transformers==4.6.0
sentencepiece==0.1.94
flask==1.1.1
itsdangerous==2.0.1
gunicorn==19.9.0
requests==2.22.0
sentry-sdk[flask]==0.14.1
healthcheck==1.3.3
jinja2<=3.0.3
Werkzeug<=2.0.3
pandas==0.25.3
huggingface-hub==0.0.8
datasets==1.11.0
scikit-learn==0.21.2
xeger==0.3.5

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