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import torch
from torch import Tensor
from torch import nn
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
import logging
logger = logging.getLogger(__name__)
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: List[str], word_weights: Dict[str, float], unknown_word_weight: float = 1):
"""
:param vocab:
Vocabulary of the tokenizer
:param word_weights:
Mapping of tokens to a float weight value. Words embeddings are multiplied by this float value. Tokens in word_weights must not be equal to the vocab (can contain more or less values)
:param unknown_word_weight:
Weight for words in vocab, that do not appear in the word_weights lookup. These can be for example rare words in the vocab, where no weight exists.
"""
super(WordWeights, self).__init__()
self.config_keys = ['vocab', 'word_weights', 'unknown_word_weight']
self.vocab = vocab
self.word_weights = word_weights
self.unknown_word_weight = unknown_word_weight
weights = []
num_unknown_words = 0
for word in vocab:
weight = unknown_word_weight
if word in word_weights:
weight = word_weights[word]
elif word.lower() in word_weights:
weight = word_weights[word.lower()]
else:
num_unknown_words += 1
weights.append(weight)
logger.info("{} of {} words without a weighting value. Set weight to {}".format(num_unknown_words, len(vocab), unknown_word_weight))
self.emb_layer = nn.Embedding(len(vocab), 1)
self.emb_layer.load_state_dict({'weight': torch.FloatTensor(weights).unsqueeze(1)})
def forward(self, features: Dict[str, Tensor]):
attention_mask = features['attention_mask']
token_embeddings = features['token_embeddings']
#Compute a weight value for each token
token_weights_raw = self.emb_layer(features['input_ids']).squeeze(-1)
token_weights = token_weights_raw * attention_mask.float()
token_weights_sum = torch.sum(token_weights, 1)
#Multiply embedding by token weight value
token_weights_expanded = token_weights.unsqueeze(-1).expand(token_embeddings.size())
token_embeddings = token_embeddings * token_weights_expanded
features.update({'token_embeddings': token_embeddings, 'token_weights_sum': token_weights_sum})
return features
def get_config_dict(self):
return {key: self.__dict__[key] for key in self.config_keys}
def save(self, output_path):
with open(os.path.join(output_path, 'config.json'), 'w') as fOut:
json.dump(self.get_config_dict(), fOut, indent=2)
@staticmethod
def load(input_path):
with open(os.path.join(input_path, 'config.json')) as fIn:
config = json.load(fIn)
return WordWeights(**config)
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