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transformers | 🤗 Transformers : State-of-the-art Machine Learning | Natural Language Processing library

 by   huggingface Python Version: 4.25.1 License: Apache-2.0

 by   huggingface Python Version: 4.25.1 License: Apache-2.0

kandi X-RAY | transformers Summary

transformers is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Tensorflow, Bert, Transformer applications. transformers has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install transformers' or download it from GitHub, PyPI.
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
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kandi-support Support

  • transformers has a medium active ecosystem.
  • It has 78856 star(s) with 17698 fork(s). There are 884 watchers for this library.
  • There were 10 major release(s) in the last 6 months.
  • There are 414 open issues and 10348 have been closed. On average issues are closed in 36 days. There are 120 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of transformers is 4.25.1
transformers Support
Best in #Natural Language Processing
Average in #Natural Language Processing
transformers Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • transformers has 0 bugs and 0 code smells.
transformers Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
transformers Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • transformers has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • transformers code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
transformers Security
Best in #Natural Language Processing
Average in #Natural Language Processing
transformers Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • transformers is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
transformers License
Best in #Natural Language Processing
Average in #Natural Language Processing
transformers License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • transformers releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • It has 429963 lines of code, 21977 functions and 1495 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
transformers Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
transformers Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Generates a beam search output .
    • Perform beam search .
      • Performs the Bigbird block - sparse attention .
        • Instantiate a pipeline .
          • Fetches the given model .
            • Train a discriminator .
              • Perform beam search .
                • Convert bort checkpoint to pytorch .
                  • Convert a Segformer checkpoint checkpoint .
                    • Wrapper for selftrain .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      transformers Key Features

                      📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.

                      🖼️ Images, for tasks like image classification, object detection, and segmentation.

                      🗣️ Audio, for tasks like speech recognition and audio classification.

                      transformers Examples and Code Snippets

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on transformers
                      • Unpickle instance from Jupyter Notebook in Flask App
                      • ModuleNotFoundError: No module named 'milvus'
                      • Which model/technique to use for specific sentence extraction?
                      • What is this GHC feature called? `forall` in type definitions
                      • Relation between Arrow suspend functions and monad comprehension
                      • Jest encountered an unexpected token - SyntaxError: Unexpected token 'export'
                      • Why Reader implemented based ReaderT?
                      • attributeerror: 'dataframe' object has no attribute 'data_type'
                      • How to calculate perplexity of a sentence using huggingface masked language models?
                      • Determine whether the Columns of a Dataset are invariant under any given Scikit-Learn Transformer
                      Trending Discussions on transformers

                      QUESTION

                      Unpickle instance from Jupyter Notebook in Flask App

                      Asked 2022-Feb-28 at 18:03

                      I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

                      AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

                      I am creating the model on Google Colab.

                      Code in Jupyter Notebook:

                      # Word2Vec Model
                      import numpy as np
                      from sklearn.base import BaseEstimator, TransformerMixin
                      from gensim.models import Word2Vec
                      
                      class GensimWord2VecVectorizer(BaseEstimator, TransformerMixin):
                      
                          def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None,
                                       sample=0.001, seed=1, workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5,
                                       ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0,
                                       trim_rule=None, sorted_vocab=1, batch_words=10000, compute_loss=False,
                                       callbacks=(), max_final_vocab=None):
                              self.size = size
                              self.alpha = alpha
                              self.window = window
                              self.min_count = min_count
                              self.max_vocab_size = max_vocab_size
                              self.sample = sample
                              self.seed = seed
                              self.workers = workers
                              self.min_alpha = min_alpha
                              self.sg = sg
                              self.hs = hs
                              self.negative = negative
                              self.ns_exponent = ns_exponent
                              self.cbow_mean = cbow_mean
                              self.hashfxn = hashfxn
                              self.iter = iter
                              self.null_word = null_word
                              self.trim_rule = trim_rule
                              self.sorted_vocab = sorted_vocab
                              self.batch_words = batch_words
                              self.compute_loss = compute_loss
                              self.callbacks = callbacks
                              self.max_final_vocab = max_final_vocab
                      
                          def fit(self, X, y=None):
                              self.model_ = Word2Vec(
                                  sentences=X, corpus_file=None,
                                  size=self.size, alpha=self.alpha, window=self.window, min_count=self.min_count,
                                  max_vocab_size=self.max_vocab_size, sample=self.sample, seed=self.seed,
                                  workers=self.workers, min_alpha=self.min_alpha, sg=self.sg, hs=self.hs,
                                  negative=self.negative, ns_exponent=self.ns_exponent, cbow_mean=self.cbow_mean,
                                  hashfxn=self.hashfxn, iter=self.iter, null_word=self.null_word,
                                  trim_rule=self.trim_rule, sorted_vocab=self.sorted_vocab, batch_words=self.batch_words,
                                  compute_loss=self.compute_loss, callbacks=self.callbacks,
                                  max_final_vocab=self.max_final_vocab)
                              return self
                      
                          def transform(self, X):
                              X_embeddings = np.array([self._get_embedding(words) for words in X])
                              return X_embeddings
                      
                          def _get_embedding(self, words):
                              valid_words = [word for word in words if word in self.model_.wv.vocab]
                              if valid_words:
                                  embedding = np.zeros((len(valid_words), self.size), dtype=np.float32)
                                  for idx, word in enumerate(valid_words):
                                      embedding[idx] = self.model_.wv[word]
                      
                                  return np.mean(embedding, axis=0)
                              else:
                                  return np.zeros(self.size)
                      
                      # column transformer
                      from sklearn.compose import ColumnTransformer
                      
                      ct = ColumnTransformer([
                          ('step1', GensimWord2VecVectorizer(), 'STATUS')
                      ], remainder='drop')
                      
                      # Create Model
                      from sklearn.svm import SVC
                      from sklearn.pipeline import Pipeline
                      from sklearn.model_selection import GridSearchCV
                      import pickle
                      import numpy as np
                      import dill
                      import torch
                      # ##########
                      # SVC - support vector classifier
                      # ##########
                      # defining parameter range
                      hyperparameters = {'C': [0.1, 1],
                                         'gamma': [1, 0.1],
                                         'kernel': ['rbf'],
                                         'probability': [True]}
                      model_sv = Pipeline([
                          ('column_transformers', ct),
                          ('model', GridSearchCV(SVC(), hyperparameters,
                                                 refit=True, verbose=3)),
                      ])
                      model_sv_cEXT = model_sv.fit(X_train, y_train['cEXT'])
                      # Save the trained cEXT - SVM Model.
                      import joblib
                      joblib.dump(model_sv_cEXT, 'model_Word2Vec_sv_cEXT.pkl')
                      

                      Code in Flask App:

                      # Word2Vec
                      model_EXT_WV_SV = joblib.load('utility/model/MachineLearning/SVM/model_Word2Vec_sv_cEXT.pkl')
                      

                      I tried to copy the same class into my Flask file, but it is also not working.

                      import numpy as np
                      from sklearn.base import BaseEstimator, TransformerMixin
                      from gensim.models import Word2Vec
                      
                      class GensimWord2VecVectorizer(BaseEstimator, TransformerMixin):
                      
                          def __init__(self, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None,
                                       sample=0.001, seed=1, workers=3, min_alpha=0.0001, sg=0, hs=0, negative=5,
                                       ns_exponent=0.75, cbow_mean=1, hashfxn=hash, iter=5, null_word=0,
                                       trim_rule=None, sorted_vocab=1, batch_words=10000, compute_loss=False,
                                       callbacks=(), max_final_vocab=None):
                              self.size = size
                              self.alpha = alpha
                              self.window = window
                              self.min_count = min_count
                              self.max_vocab_size = max_vocab_size
                              self.sample = sample
                              self.seed = seed
                              self.workers = workers
                              self.min_alpha = min_alpha
                              self.sg = sg
                              self.hs = hs
                              self.negative = negative
                              self.ns_exponent = ns_exponent
                              self.cbow_mean = cbow_mean
                              self.hashfxn = hashfxn
                              self.iter = iter
                              self.null_word = null_word
                              self.trim_rule = trim_rule
                              self.sorted_vocab = sorted_vocab
                              self.batch_words = batch_words
                              self.compute_loss = compute_loss
                              self.callbacks = callbacks
                              self.max_final_vocab = max_final_vocab
                      
                          def fit(self, X, y=None):
                              self.model_ = Word2Vec(
                                  sentences=X, corpus_file=None,
                                  size=self.size, alpha=self.alpha, window=self.window, min_count=self.min_count,
                                  max_vocab_size=self.max_vocab_size, sample=self.sample, seed=self.seed,
                                  workers=self.workers, min_alpha=self.min_alpha, sg=self.sg, hs=self.hs,
                                  negative=self.negative, ns_exponent=self.ns_exponent, cbow_mean=self.cbow_mean,
                                  hashfxn=self.hashfxn, iter=self.iter, null_word=self.null_word,
                                  trim_rule=self.trim_rule, sorted_vocab=self.sorted_vocab, batch_words=self.batch_words,
                                  compute_loss=self.compute_loss, callbacks=self.callbacks,
                                  max_final_vocab=self.max_final_vocab)
                              return self
                      
                          def transform(self, X):
                              X_embeddings = np.array([self._get_embedding(words) for words in X])
                              return X_embeddings
                      
                          def _get_embedding(self, words):
                              valid_words = [word for word in words if word in self.model_.wv.vocab]
                              if valid_words:
                                  embedding = np.zeros((len(valid_words), self.size), dtype=np.float32)
                                  for idx, word in enumerate(valid_words):
                                      embedding[idx] = self.model_.wv[word]
                      
                                  return np.mean(embedding, axis=0)
                              else:
                                  return np.zeros(self.size)
                      
                      # Word2Vec
                      model_EXT_WV_SV = joblib.load('utility/model/MachineLearning/SVM/model_Word2Vec_sv_cEXT.pkl')
                      

                      ANSWER

                      Answered 2022-Feb-24 at 11:48

                      Import GensimWord2VecVectorizer in your Flask Web app python file.

                      Source https://stackoverflow.com/questions/71231611

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install transformers

                      You can install using 'pip install transformers' or download it from GitHub, PyPI.
                      You can use transformers like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                      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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                      Install
                      • pip install transformers

                      Clone
                      • https://github.com/huggingface/transformers.git

                      • gh repo clone huggingface/transformers

                      • git@github.com:huggingface/transformers.git

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