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bert-cosine-sim | Finetune BERT to generate sentence embedding | Natural Language Processing library

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kandi X-RAY | bert-cosine-sim Summary

bert-cosine-sim is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow, Bert applications. bert-cosine-sim has no bugs, it has no vulnerabilities and it has low support. However bert-cosine-sim build file is not available. You can download it from GitHub.
Fine-tune BERT to generate sentence embedding for cosine similarity

kandi-support Support

  • bert-cosine-sim has a low active ecosystem.
  • It has 61 star(s) with 10 fork(s). There are 3 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 3 open issues and 0 have been closed. On average issues are closed in 423 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of bert-cosine-sim is current.

quality kandi Quality

  • bert-cosine-sim has 0 bugs and 0 code smells.

securitySecurity

  • bert-cosine-sim has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • bert-cosine-sim code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.

license License

  • bert-cosine-sim does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.

buildReuse

  • bert-cosine-sim releases are not available. You will need to build from source code and install.
  • bert-cosine-sim has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions, examples and code snippets are available.
  • bert-cosine-sim saves you 811 person hours of effort in developing the same functionality from scratch.
  • It has 1862 lines of code, 177 functions and 8 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA

kandi has reviewed bert-cosine-sim and discovered the below as its top functions. This is intended to give you an instant insight into bert-cosine-sim implemented functionality, and help decide if they suit your requirements.

  • Setup the command line arguments .
  • Convert examples to features .
  • Perform a single step .
  • Convert pair features from BERT features .
  • Evaluate the model .
  • Tokenize text .
  • load weights from a file
  • Load a pre - trained model from a pre - trained model .
  • Train a single epoch .
  • Returns evaluation data .

bert-cosine-sim Key Features

Fine-tune BERT to generate sentence embedding for cosine similarity

bert-cosine-sim Examples and Code Snippets

  • Model and Fine-tuning

Model and Fine-tuning

 class BertPairSim(BertPreTrainedModel):
    def __init__(self, config, emb_size=1024):
        super(BertPairSim, self).__init__(config)
        self.emb_size = emb_size
        self.bert = BertModel(config)
        self.emb = nn.Linear(config.hidden_size, emb_size)
        self.activation = nn.Tanh()
        self.cos_fn = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
        self.apply(self.init_bert_weights)

    def calcSim(self, emb1, emb2):
        return self.cos_fn(emb1, emb2)
        
    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.bert(input_ids, None, attention_mask,
                                     output_all_encoded_layers=False)
        emb = self.activation(self.emb(pooled_output))
        return emb

Community Discussions

Trending Discussions on Natural Language Processing
  • number of matches for keywords in specified categories
  • Apple's Natural Language API returns unexpected results
  • Tokenize text but keep compund hyphenated words together
  • Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
  • ModuleNotFoundError: No module named 'milvus'
  • Which model/technique to use for specific sentence extraction?
  • Assigning True/False if a token is present in a data-frame
  • How to calculate perplexity of a sentence using huggingface masked language models?
  • Mapping values from a dictionary's list to a string in Python
  • What are differences between AutoModelForSequenceClassification vs AutoModel
Trending Discussions on Natural Language Processing

QUESTION

number of matches for keywords in specified categories

Asked 2022-Apr-14 at 13:32

For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

df_texts <- tibble(
  text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
  grasshopper"),
  mammals=NA,
  reptiles=NA,
  birds=NA,
  insects=NA
)

df_animals <- tibble(animals=c("ape", "fox", "tortoise", "hare", "owl", "grasshopper"),
           type=c("mammal", "mammal", "reptile", "mammal", "bird", "insect"))

So my desired result would be:

df_result <- tibble(
  text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
  grasshopper"),
  mammals=c(2,1,0),
  reptiles=c(0,1,0),
  birds=c(0,0,1),
  insects=c(0,0,1)
)

Is there a straightforward way to achieve this keyword-matching-and-counting that would be applicable to a much larger dataset?

Thanks in advance!

ANSWER

Answered 2022-Apr-14 at 13:32

Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

library(tidyverse)

cbind(df_texts[, 1], sapply(df_animals$animals, grepl, df_texts$text)) %>% 
  pivot_longer(-text, names_to = "animals") %>% 
  left_join(df_animals) %>% 
  group_by(text, type) %>% 
  summarise(sum = sum(value)) %>% 
  pivot_wider(id_cols = text, names_from = type, values_from = sum)

  text                                   bird insect mammal reptile
  <chr>                                 <int>  <int>  <int>   <int>
1 "the ape and the fox"                     0      0      2       0
2 "the owl and the the \n  grasshopper"     1      0      0       0
3 "the tortoise and the hare"               0      0      1       1

To account for the several occurrences per text:

cbind(df_texts[, 1], t(sapply(df_texts$text, str_count, df_animals$animals, USE.NAMES = F))) %>% 
  setNames(c("text", df_animals$animals)) %>% 
  pivot_longer(-text, names_to = "animals") %>% 
  left_join(df_animals) %>% 
  group_by(text, type) %>% 
  summarise(sum = sum(value)) %>% 
  pivot_wider(id_cols = text, names_from = type, values_from = sum)

Source https://stackoverflow.com/questions/71871613

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install bert-cosine-sim

python prerun.py downloads, extracts and saves model and training data (STS-B) in relevant folder, after which you can simply modify hyperparameters in run.sh.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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