bert-cosine-sim | Finetune BERT to generate sentence embedding | Natural Language Processing library
kandi X-RAY | bert-cosine-sim Summary
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- 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
- bert-cosine-sim has 0 bugs and 0 code smells.
Security
- 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.
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- Without a license, all rights are reserved, and you cannot use the library in your applications.
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- 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.
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
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
QUESTION
number of matches for keywords in specified categories
Asked 2022-Apr-14 at 13:32For 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 13:32Here'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)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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