bert | TensorFlow code and pre-trained models for BERT | Natural Language Processing library
kandi X-RAY | bert Summary
kandi X-RAY | bert Summary
BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here:
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Top functions reviewed by kandi - BETA
- Writes predictions
- Compute softmax
- Returns the n_best_size of the logits
- Return the final prediction
- Convert examples to features
- Convert a single example
- Return a string representation of text
- Truncate a sequence pair
- Validate flags
- Validate a case insensitive case
- Returns a list of input examples
- Embed word embedding
- Return a list of input examples
- Builds the input function
- Tokenize text
- Validates that the case matches the given checkpoint
- Build a file - based input function
- Create TrainingInstances
- Reads input_file
- Creates an attention mask from from_tensor
- Converts examples into features
- Reads squad examples
- Process a feature
- Write examples to examples
- Transformer transformer model
- Embedding postprocessor
- Build a function for TPUEstimator
bert Key Features
bert Examples and Code Snippets
Lines of Code : 18License : 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
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown pip install -U sentence-transformers
conda install -c conda-forge sentence-transformers
pip install -e .
def generator(X_data,y_data,batch_size):
while True:
for step in range(X_data.shape[0]//batch_size):
start=step*batch_size
end=step*(batch_size+1)
current_x=X_data.iloc[start]
current_y=y_d!pip install bert-tensorflow
class AdamWeightDecayOptimizer(tf.train.Optimizer):
class AdamWeightDecayOptimizer(tf.compat.v1.train.Optimizer):
embeddings = bert_model.bert.get_input_embeddings()
word_embeddings = embeddings.word_embeddings
inputs_embeds = tf.gather(word_embeddings, input_ids)
full_embeddings = embeddings(inputs=[None, None, token_type_idsinputs_embeds = result[-1][0]
embeddings = bert_model.bert.get_input_embeddings().word_embeddings
inputs_embeds = tf.gather(embeddings, input_ids)
python run_classifier.py \
--task_name=MRPC \
--do_predict=true \
--data_dir=$GLUE_DIR/MRPC \
--vocab_file=$BERT_BASE_DIR/vocab.txt \
--bert_config_file=$BERT_BASE_DIR/bert_config.json \
--init_checkpoint=$TRAINED_CLASSIFIER
<elif self.pooling == "mean":
result = self.bert(inputs=bert_inputs, signature="tokens", as_dict=True)["sequence_output" ]
pooled = result
embedding_size = 768
in_id = Input(shape=(max_seq_length,), name="Community Discussions
Trending Discussions on bert
QUESTION
I cannot find anywhere how to convert a pandas dataframe to type datasets.dataset_dict.DatasetDict, for optimal use in a BERT workflow with a huggingface model. Take these simple dataframes, for example.
ANSWER
Answered 2022-Mar-25 at 15:47One possibility is to first create two Datasets and then join them:
QUESTION
What is the loss function used in Trainer from the Transformers library of Hugging Face?
I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face.
In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class. However, if I do not do the method override and use the Trainer to fine tine a BERT model directly for sentiment classification, what is the default loss function being use? Is it the categorical crossentropy? Thanks!
ANSWER
Answered 2022-Mar-23 at 10:12It depends!
Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.
You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:
loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.
Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.
The code relevant for defining a loss function looks like this:
QUESTION
I am following this tutorial on how to train a siamese bert network:
https://keras.io/examples/nlp/semantic_similarity_with_bert/
all good, but I am not sure what is the best way to save the model after train it and save it. any suggestion?
I was trying with
model.save('models/bert_siamese_v1')
which creates a folder with save_model.bp keras_metadata.bp and two subfolders (variables and assets)
then I try to load it with:
...ANSWER
Answered 2022-Mar-08 at 16:13Try using tf.saved_model.save to save your model:
QUESTION
Currently i'm able to train a Semantic Role Labeling model using the config file below. This config file is based on the one provided by AllenNLP and works for the default bert-base-uncased model and also GroNLP/bert-base-dutch-cased.
ANSWER
Answered 2022-Feb-24 at 02:14The easiest way to resolve this is to patch SrlReader so that it uses PretrainedTransformerTokenizer (from AllenNLP) or AutoTokenizer (from Huggingface) instead of BertTokenizer. SrlReader is an old class, and was written against an old version of the Huggingface tokenizer API, so it's not so easy to upgrade.
If you want to submit a pull request in the AllenNLP project, I'd be happy to help you get it merged into AllenNLP!
QUESTION
I have a simple transformers script looking like this.
...ANSWER
Answered 2022-Feb-22 at 11:54Use this model instead.
QUESTION
I have a corpus of synonyms and non-synonyms. These are stored in a list of python dictionaries like {"sentence1": , "sentence2": , "label": <1.0 or 0.0> }. Note that this words (or sentences) do not have to be a single token in the tokenizer.
I want to fine-tune a BERT-based model to take both sentences like: [[CLS], ], ...,, [SEP], ], ..., , [SEP]] and predict the "label" (a measurement between 0.0 and 1.0).
What is the best approach to organized this data to facilitate the fine-tuning of the huggingface transformer?
...ANSWER
Answered 2022-Feb-02 at 14:58You can use the Tokenizer __call__ method to join both sentences when encoding them.
In case you're using the PyTorch implementation, here is an example:
QUESTION
I am getting the following error : attributeerror: 'dataframe' object has no attribute 'data_type'" . I am trying to recreate the code from this link which is based on this article with my own dataset which is similar to the article
ANSWER
Answered 2022-Jan-10 at 08:41The error means you have no data_type column in your dataframe because you missed this step
QUESTION
I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.
However, I run into the following error:
...ANSWER
Answered 2021-Dec-31 at 08:18As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this
set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1
QUESTION
I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?
From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.
For example in this SO question they calculated it using the function
...ANSWER
Answered 2021-Dec-25 at 21:51There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.
As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:
QUESTION
So what I want to do is identify the 1st node in some subtree of a xml tree.
here's an example
...ANSWER
Answered 2021-Dec-23 at 19:40This seems to be what you’re after, using the descendant axis:
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Install bert
You can use bert 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.
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