def __init__(self):
self.ctx_encoder = DPRContextEncoder.from_pretrained(
"facebook/dpr-ctx_encoder-multiset-base").to(Config.device)
self.q_encoder = DPRQuestionEncoder.from_pretrained(
"facebook/dpr-question_encoder-multiset-base").to(Config.device)
self.ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(
"facebook/dpr-ctx_encoder-multiset-base")
self.q_tokenizer = DPRQuestionEncoderTokenizerFast.from_pretrained(
"facebook/dpr-question_encoder-multiset-base")
from flask import Flask
from flask import request, jsonify
import torch
from torch import nn
import pandas as pd
import numpy as np
from datasets import Features, Sequence, Value, load_dataset, load_from_disk
from transformers import (DPRContextEncoder, DPRQuestionEncoder,
DPRQuestionEncoderTokenizerFast)
import faiss
import operator
tokenizer = DPRQuestionEncoderTokenizerFast.from_pretrained(
'facebook/dpr-question_encoder-multiset-base')
model = DPRQuestionEncoder.from_pretrained('Cencoder')
model.to(torch.device('cpu'))
model.eval()
print('--- Model Loaded ---')
dataset = load_from_disk('def_index/my_knowledge_dataset/')
dataset.load_faiss_index("embeddings",
'def_index/my_knowledge_dataset_hnsw_index.faiss')
app = Flask(__name__)
app.debug = True
@app.route("/", methods=['GET'])
def home():
return "<center><h1>Flask web API</h1><p>Returns definitions for queries</center>"
def load(cls,
pretrained_model_name_or_path,
tokenizer_class=None,
use_fast=False,
**kwargs):
"""
Enables loading of different Tokenizer classes with a uniform interface. Either infer the class from
`pretrained_model_name_or_path` or define it manually via `tokenizer_class`.
:param pretrained_model_name_or_path: The path of the saved pretrained model or its name (e.g. `bert-base-uncased`)
:type pretrained_model_name_or_path: str
:param tokenizer_class: (Optional) Name of the tokenizer class to load (e.g. `BertTokenizer`)
:type tokenizer_class: str
:param use_fast: (Optional, False by default) Indicate if FARM should try to load the fast version of the tokenizer (True) or
use the Python one (False).
Only DistilBERT, BERT and Electra fast tokenizers are supported.
:type use_fast: bool
:param kwargs:
:return: Tokenizer
"""
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
# guess tokenizer type from name
if tokenizer_class is None:
if "albert" in pretrained_model_name_or_path.lower():
tokenizer_class = "AlbertTokenizer"
elif "xlm-roberta" in pretrained_model_name_or_path.lower():
tokenizer_class = "XLMRobertaTokenizer"
elif "roberta" in pretrained_model_name_or_path.lower():
tokenizer_class = "RobertaTokenizer"
elif 'codebert' in pretrained_model_name_or_path.lower():
if "mlm" in pretrained_model_name_or_path.lower():
raise NotImplementedError(
"MLM part of codebert is currently not supported in FARM"
)
else:
tokenizer_class = "RobertaTokenizer"
elif "camembert" in pretrained_model_name_or_path.lower(
) or "umberto" in pretrained_model_name_or_path:
tokenizer_class = "CamembertTokenizer"
elif "distilbert" in pretrained_model_name_or_path.lower():
tokenizer_class = "DistilBertTokenizer"
elif "bert" in pretrained_model_name_or_path.lower():
tokenizer_class = "BertTokenizer"
elif "xlnet" in pretrained_model_name_or_path.lower():
tokenizer_class = "XLNetTokenizer"
elif "electra" in pretrained_model_name_or_path.lower():
tokenizer_class = "ElectraTokenizer"
elif "word2vec" in pretrained_model_name_or_path.lower() or \
"glove" in pretrained_model_name_or_path.lower() or \
"fasttext" in pretrained_model_name_or_path.lower():
tokenizer_class = "EmbeddingTokenizer"
elif "minilm" in pretrained_model_name_or_path.lower():
tokenizer_class = "BertTokenizer"
elif "dpr-question_encoder" in pretrained_model_name_or_path.lower(
):
tokenizer_class = "DPRQuestionEncoderTokenizer"
elif "dpr-ctx_encoder" in pretrained_model_name_or_path.lower():
tokenizer_class = "DPRContextEncoderTokenizer"
else:
raise ValueError(
f"Could not infer tokenizer_class from name '{pretrained_model_name_or_path}'. Set "
f"arg `tokenizer_class` in Tokenizer.load() to one of: AlbertTokenizer, "
f"XLMRobertaTokenizer, RobertaTokenizer, DistilBertTokenizer, BertTokenizer, or "
f"XLNetTokenizer.")
logger.info(f"Loading tokenizer of type '{tokenizer_class}'")
# return appropriate tokenizer object
ret = None
if tokenizer_class == "AlbertTokenizer":
if use_fast:
logger.error(
'AlbertTokenizerFast is not supported! Using AlbertTokenizer instead.'
)
ret = AlbertTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
else:
ret = AlbertTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
elif tokenizer_class == "XLMRobertaTokenizer":
if use_fast:
logger.error(
'XLMRobertaTokenizerFast is not supported! Using XLMRobertaTokenizer instead.'
)
ret = XLMRobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = XLMRobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "RobertaTokenizer" in tokenizer_class: # because it also might be fast tokekenizer we use "in"
if use_fast:
logger.error(
'RobertaTokenizerFast is not supported! Using RobertaTokenizer instead.'
)
ret = RobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = RobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "DistilBertTokenizer" in tokenizer_class: # because it also might be fast tokekenizer we use "in"
if use_fast:
ret = DistilBertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DistilBertTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "BertTokenizer" in tokenizer_class: # because it also might be fast tokekenizer we use "in"
if use_fast:
ret = BertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = BertTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "XLNetTokenizer":
if use_fast:
logger.error(
'XLNetTokenizerFast is not supported! Using XLNetTokenizer instead.'
)
ret = XLNetTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
else:
ret = XLNetTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
elif "ElectraTokenizer" in tokenizer_class: # because it also might be fast tokekenizer we use "in"
if use_fast:
ret = ElectraTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = ElectraTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "EmbeddingTokenizer":
if use_fast:
logger.error(
'EmbeddingTokenizerFast is not supported! Using EmbeddingTokenizer instead.'
)
ret = EmbeddingTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = EmbeddingTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "CamembertTokenizer":
if use_fast:
logger.error(
'CamembertTokenizerFast is not supported! Using CamembertTokenizer instead.'
)
ret = CamembertTokenizer._from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = CamembertTokenizer._from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "DPRQuestionEncoderTokenizer" or tokenizer_class == "DPRQuestionEncoderTokenizerFast":
if use_fast or tokenizer_class == "DPRQuestionEncoderTokenizerFast":
ret = DPRQuestionEncoderTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DPRQuestionEncoderTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "DPRContextEncoderTokenizer" or tokenizer_class == "DPRContextEncoderTokenizerFast":
if use_fast or tokenizer_class == "DPRContextEncoderTokenizerFast":
ret = DPRContextEncoderTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DPRContextEncoderTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
if ret is None:
raise Exception("Unable to load tokenizer")
else:
return ret
def load(cls,
pretrained_model_name_or_path,
revision=None,
tokenizer_class=None,
use_fast=True,
**kwargs):
"""
Enables loading of different Tokenizer classes with a uniform interface. Either infer the class from
model config or define it manually via `tokenizer_class`.
:param pretrained_model_name_or_path: The path of the saved pretrained model or its name (e.g. `bert-base-uncased`)
:type pretrained_model_name_or_path: str
:param revision: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
:type revision: str
:param tokenizer_class: (Optional) Name of the tokenizer class to load (e.g. `BertTokenizer`)
:type tokenizer_class: str
:param use_fast: (Optional, False by default) Indicate if FARM should try to load the fast version of the tokenizer (True) or
use the Python one (False).
Only DistilBERT, BERT and Electra fast tokenizers are supported.
:type use_fast: bool
:param kwargs:
:return: Tokenizer
"""
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
kwargs["revision"] = revision
if tokenizer_class is None:
tokenizer_class = cls._infer_tokenizer_class(
pretrained_model_name_or_path)
logger.info(f"Loading tokenizer of type '{tokenizer_class}'")
# return appropriate tokenizer object
ret = None
if "AlbertTokenizer" in tokenizer_class:
if use_fast:
ret = AlbertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
else:
ret = AlbertTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
elif "XLMRobertaTokenizer" in tokenizer_class:
if use_fast:
ret = XLMRobertaTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = XLMRobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "RobertaTokenizer" in tokenizer_class:
if use_fast:
ret = RobertaTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = RobertaTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "DistilBertTokenizer" in tokenizer_class:
if use_fast:
ret = DistilBertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DistilBertTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "BertTokenizer" in tokenizer_class:
if use_fast:
ret = BertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = BertTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "XLNetTokenizer" in tokenizer_class:
if use_fast:
ret = XLNetTokenizerFast.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
else:
ret = XLNetTokenizer.from_pretrained(
pretrained_model_name_or_path, keep_accents=True, **kwargs)
elif "ElectraTokenizer" in tokenizer_class:
if use_fast:
ret = ElectraTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = ElectraTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif tokenizer_class == "EmbeddingTokenizer":
if use_fast:
logger.error(
'EmbeddingTokenizerFast is not supported! Using EmbeddingTokenizer instead.'
)
ret = EmbeddingTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = EmbeddingTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "CamembertTokenizer" in tokenizer_class:
if use_fast:
ret = CamembertTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = CamembertTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "DPRQuestionEncoderTokenizer" in tokenizer_class:
if use_fast:
ret = DPRQuestionEncoderTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DPRQuestionEncoderTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
elif "DPRContextEncoderTokenizer" in tokenizer_class:
if use_fast:
ret = DPRContextEncoderTokenizerFast.from_pretrained(
pretrained_model_name_or_path, **kwargs)
else:
ret = DPRContextEncoderTokenizer.from_pretrained(
pretrained_model_name_or_path, **kwargs)
if ret is None:
raise Exception("Unable to load tokenizer")
else:
return ret