sentence-transformers | Multilingual Sentence & Image Embeddings with BERT | Natural Language Processing library
kandi X-RAY | sentence-transformers Summary
kandi X-RAY | sentence-transformers Summary
This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity. We provide an increasing number of state-of-the-art pretrained models for more than 100 languages, fine-tuned for various use-cases. Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task. For the full documentation, see www.SBERT.net.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Fit a learning objective function
- Create model card
- Evaluate the given evaluation
- Save the SentenceModel to a file
- Fit the optimizer
- Save the pretrained model
- Save model to file
- Evaluate the given evaluator
- Loads data from a file
- Start a multi - process multi - process
- Collate a batch of texts
- Save a pretrained model
- Get input examples from a file
- Get input examples from a CSV file
- Reads an evaluation dataset
- Perform kNN search
- Save the trained model
- Forward the encoder
- Compute the degree centrality score
- Forward mini batch prediction
- Encodes the model
- Save the model to the hub
- Performs community detection on embedding
- Predict given sentences
- Encodes sentences into multiple sentences
- Load embeddings from a text file
- Loads the Sbert model
- Load TREC dataset
sentence-transformers Key Features
sentence-transformers Examples and Code Snippets
Lines of Code : 67License : Permissive (Apache-2.0)
Copy@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Nat from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
# Two lists of sentences
sentences1 = ['The cat sits outside',
'A man is playing guitar',
'The new movie is awesome from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
#Sentences are encoded by calling model.encode()
emb1 = model.encode("This is a red cat with a hat.")
emb2 = model.encode("Have you seen my r """
The Quora Duplicate Questions dataset contains questions pairs from Quora (www.quora.com)
along with a label whether the two questions are a duplicate, i.e., have an identical itention.
Example of a duplicate pair:
How do I enhance my English? import sys
import json
from torch.utils.data import DataLoader
from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample
import logging
from datetime import datetime
import gzip
import os
im """
This examples show how to train a Bi-Encoder for the MS Marco dataset (https://github.com/microsoft/MSMARCO-Passage-Ranking).
The queries and passages are passed independently to the transformer network to produce fixed sized embeddings.
These e pip install -U farm-haystack pinecone-client
document_store = PineconeDocumentStore(
api_key="", # from https://app.pinecone.io
environment="us-west1-gcp"
)
retriever = EmbeddingRetriever(
document_storScore: 100.000%
.\cat1 copy.jpg
.\cat1.jpg
Score: 91.116%
.\cat1 copy.jpg
.\cat2.jpg
Score: 91.116%
.\cat1.jpg
.\cat2.jpg
Score: 91.097%
.\bear1.jpg
.\bear2.jpg
Score: 59.086%
.\bear2.jpg
.\cat2.jpg
Score: 56.0['1_Pooling', 'config_sentence_transformers.json', 'tokenizer.json', 'tokenizer_config.json', 'modules.json', 'sentence_bert_config.json', 'pytorch_model.bin', 'special_tokens_map.json', 'config.json', 'train_script.py', 'data_config.json'X_train = pd.DataFrame({
'tweet':['foo', 'foo', 'bar'],
'feature1':[1, 1, 0],
'feature2':[1, 0, 1],
})
y_train = [1, 1, 0]
model = SentenceTransformer('mrm8488/bert-tiny-finetuned-squadv2') # model nameCommunity Discussions
Trending Discussions on sentence-transformers
QUESTION
I am having trouble when switching a model from some local dummy data to using a TF dataset.
Sorry for the long model code, I have tried to shorten it as much as possible.
The following works fine:
...ANSWER
Answered 2022-Mar-10 at 08:57You will have to explicitly set the shapes of the tensors coming from tf.py_functions. Using None will allow variable input lengths. The Bert output dimension (384,) is, however, necessary:
QUESTION
I would like to use a model from sentence-transformers inside of a larger Keras model.
Here is the full example:
...ANSWER
Answered 2022-Mar-09 at 17:10tf.py_function does not seem to work with a dict output that’s why you can try returning three separate tensors. Also, I am decoding the inputs to remove the b in the front of each string:
QUESTION
I have access to the latest packages but I cannot access internet from my python enviroment.
Package versions that I have are as below
...ANSWER
Answered 2022-Jan-19 at 13:27Based on the things you mentioned, I checked the source code of sentence-transformers on Google Colab. After running the model and getting the files, I check the directory and I saw the pytorch_model.bin there.
And according to sentence-transformers code:
Link
the flax_model.msgpack , rust_model.ot, tf_model.h5 are getting ignored when the it is trying to download.
and these are the files that it downloads :
QUESTION
I have a dataset, one feature is text and 4 more features. Sentence-Bert vectorizer transforms text data into tensors. I can use these sparse matrices directly with a machine learning classifier. Can I replace the text column with tensors? And, how can I train the model. The code below is how I transform the text into vectors.
...ANSWER
Answered 2021-Oct-16 at 12:47Let's assume this is your data
QUESTION
I implemented a string comparison method using SentenceTransformers and BERT like following
...ANSWER
Answered 2021-Sep-08 at 22:07The results are not surprising. You have passed two sentences which are very similar, but have opposite meanings. The sentence embeddings are obtained from a model trained on generic corpora, hence, the embeddings given by the model are generally expected to close to each other if the sentences are similar. And that's what is happening, that the cosine similarity shows that the embedding are close to each other and so is the sentence. The sentences in the example may have opposite meanings, but they are similar to each other.
In case, if you expect two similar sentences with opposite meaning to be far away from each other, then you have to further fine-tune the model with kind of classification model (such as sentiment analysis, if your examples are based on positive and negative sentiments). or with some other relevant task.
QUESTION
I am running a sentence transformer model and trying to truncate my tokens, but it doesn't appear to be working. My code is
...ANSWER
Answered 2021-Aug-19 at 20:18You need to add the max_length parameter while creating the tokenizer like below:
text_tokens = tokenizer(text, padding=True, max_length=512, truncation=True, return_tensors="pt")
truncation=True without max_length parameter takes sequence length equal to maximum acceptable input length by the model.
It is 1e30 or 1000000000000000019884624838656 for this model. You can check by printing out tokenizer.model_max_length.
According to the Huggingface documentation about truncation,
True or 'only_first' truncate to a maximum length specified by the max_length argument or the maximum length accepted by the model if no max_length is provided (max_length=None).
QUESTION
I have a requirements.txt file which holds all information of my python packages I need for my Flask application. Here is what I did:
python3 -m venv venvsource venv/bin/activatesudo pip install -r requirements.txt
When I tried to check if the packages were installed on the virtual environment using pip list, I do not see the packages. Can someone tell what went wrong?
ANSWER
Answered 2021-Aug-18 at 18:05If you want to use python3+ to install the packages try to use pip3 install package_name
And to solve the errno 13 try to add --user at the end
QUESTION
can anyone help me to resolve this error?
...ANSWER
Answered 2021-Aug-17 at 13:41[Updated]
I skimmed several lines of documentation here about how to use the fit() method and I realized there is a simpler solution to do what you desired. The only changes you need to consider are to define proper InputExample for constructing a DataLoader & create a loss!
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I am using BERT model for context search in Italian language but it does not understand the contextual meaning of the sentence and returns wrong result.
in below example code when I compare "milk with chocolate flavour" with two other type of milk and one chocolate so it returns high similarity with chocolate. it should return high similarity with other milks.
can anyone suggest me any improvement on the below code so that it can return semantic results?
Code :
...ANSWER
Answered 2021-Aug-09 at 19:33The problem is not with your code, it is just the insufficient model performance.
There are a few things you can do. First, you can try Universal Sentence Encoder (USE). From my experience their embeddings are a little bit better, at least in English.
Second, you can try a different model, for example sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1. It is based on ROBERTa and might give a better performance.
Now you can combine together embeddings from several models (just by concatenating the representations). In some cases it helps, on expense of much heavier compute.
And finally you can create your own model. It is well known that single language models perform significantly better than multilingual ones. You can follow the guide and train your own Italian model.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install sentence-transformers
See Quickstart in our documenation. This example shows you how to use an already trained Sentence Transformer model to embed sentences for another task. First download a pretrained model. Then provide some sentences to the model. And that's it already. We now have a list of numpy arrays with the embeddings.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
