keras-bert | could load official pre-trained models | Natural Language Processing library
kandi X-RAY | keras-bert Summary
kandi X-RAY | keras-bert Summary
Implementation of the BERT. Official pre-trained models could be loaded for feature extraction and prediction.
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Top functions reviewed by kandi - BETA
- Loads a trained model from a checkpoint file
- Get a trained model
- Loads weights from a checkpoint file
- Build a trained model from a config file
- Encodes two tokens
- Tokenize text
- Pack two tokens
- Checks if the character is a CJK character
- Get a pre - trained model
- Get embedding layer
- Get input tensors
- Evaluate the function
- Compute the masked mask
- Match text
- Tokenize two tokens
- Load a vocabulary
- Return checkpoint paths
- Find the version string
keras-bert Key Features
keras-bert Examples and Code Snippets
Lines of Code : 87License : Permissive (MIT)
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[
"揭秘趣步骗局，趣步是什么，趣步是怎么赚钱的？趣步公司可靠吗？趣步合法吗？相信是众多小伙伴最关心的话题，今天小编就来给大家揭开趣步这面“丑恶”且神秘的面纱，让小伙伴们看清事情的真相。接下来，我用简单的文字，给大家详细剖析一下趣步公司及趣步app的逻辑到底是什么样>的？3分钟时间...全文：?揭秘趣步骗局，趣步是什么，趣步是怎么赚钱的？趣步公司可靠吗？趣步合法吗？相信是众多小伙伴最关心的话题，今天小编就来给大家揭开趣步这面“丑恶”且神秘的面纱，让小伙伴们看清事 (nlp) liushaoweihua@ai-server-6:~/jupyterlab/Keras-Bert-Ner$ python keras_bert_ner/train/help.py --help
usage: help.py [-h] -train_data TRAIN_DATA [-dev_data DEV_DATA]
[-save_path SAVE_PATH] [-albert] -bert_config BERT_CONFIG
PRETRAINED_LM_DIR="/home1/liushaoweihua/pretrained_lm/albert_tiny_250k" # your pretrained language model path
DATA_DIR="../data" # your train/dev data path
OUTPUT_DIR="../models" # where to store the NER model
python run_train.py \
-train_data=$ name: bert_env
channels:
- defaults
dependencies:
- numpy
- keras
- pip
- pip:
- keras-bert
conda env create -f bert_env.yaml
> reticulate::use_condaenv("bert_env", requireinput_layer= keras.layers.Input(shape=(SEQ_LEN,768))(layer_output)
tensor_instance = Layer(...)(tensor_instance)
conv_layer_output_tensor = Conv1D(...)(layer_output)
load_model(...., custom_objects={'BertLayer':BertLayer})
embedding_size = 768
in_id = Input(shape=(max_seq_length,), name="input_ids")
in_mask = Input(shape=(max_seq_length,), name="input_masks")
in_segment = Input(shape=(max_seq_length,), name="segment_ids")
bert_inputs = [in_id, in_mask, in_question_indices_layer = Input(shape=(256,), dtype='float16')
question_segments_layer = Input(shape=(256,), dtype='float16')
context_indices_layer = Input(shape=(256,), dtype='float16')
context_segments_layer = Input(shape=(256,), dtype='fSystem | Seq Length | Max Batch Size
------------ | ---------- | --------------
`BERT-Base` | 64 | 64
... | 128 | 32
... | 256 | 16
... | 320 | 14
... | 384 | 1elif 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 keras-bert
QUESTION
I'm using keras-bert for classification. On some datasets, it runs well and calculates the loss, while on others the loss is NaN.
The different datasets are similar in that they are augmented versions of the original one. Working with keras-bert, the original data and some augmented versions of the data run well while the other augmented versions of data don't run well.
When I use a regular one-layer BiLSTM on the augmented versions of data that don't run well with keras-bert, it works out fine which means I can rule out the possibility of the data being faulty or containing spurious values that may affect the way the loss is calculated.
The data in working with has three classes.
I'm using bert based uncased
...ANSWER
Answered 2021-May-08 at 16:05I noticed one issue in your code but I'm not sure if this the main cause; better if you can possibly provide some reproducible code.
In your above code snippet, you set sigmoid in your last layer activation with unit < 1 which indicate the problem dataset is probably multi-label and that's why the loss function should be binary_crossentropy but you set sparse_categorical_crossentropy which is typical uses multi-class problem and with integer labels.
QUESTION
I'm trying to install keras-bert as explained here: BERT from R. This tutorial shows how to load and train the BERT model from R, using Keras.
But when, in Anaconda prompt (Windows), I run:
...ANSWER
Answered 2020-Jun-24 at 06:40Since this requires mixing PyPI packages with Conda, the best practice recommendation for this is to create a dedicated environment using a YAML file. You may need additional version constraints in here to achieve a setup that works with the tutorial, but this YAML is sufficient for me to do the first steps:
bert_env.yaml
QUESTION
I am running a fairly customized tensorflow model from the following repo:
https://github.com/strongio/keras-bert/blob/master/keras-bert.py
...ANSWER
Answered 2020-Feb-05 at 19:02Well, you literally reconstruct the entire model, exactly the same way you constructed it for the first time. It seems build_model contains it entirely.
Then you do model.load_weights(path).
Your approach will not save the optimizer, though. If you want to "continue" training a loaded model, you'd better have the optimizer saved.
For using model.save you just need to write the get_config method for the BertLayer.
You can find a lot of examples on how to write this method by looking at how Keras writes it in its own layers:
- Dense: https://github.com/keras-team/keras/blob/master/keras/layers/core.py#L796
- Conv: https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py#L361
- etc.: https://keras.io/layers/core/
Remember that the model loader doesn't know your layer, you have to inform it:
QUESTION
I am experimenting with BERT embeddings for text classification. I am using this code that creates a BERT embedding layer and a dense layer for binary classification.
...ANSWER
Answered 2020-Jan-16 at 23:09First, make batch size smaller.
Then change to this: this adds a global max pooling 1d layer to flatten out.
QUESTION
My setup has an NVIDIA P100 GPU. I am working on a Google BERT model to answer questions. I am using the SQuAD question-answering dataset, which gives me questions, and paragraphs from which the answers should be drawn, and my research indicates this architecture should be OK, but I keep getting OutOfMemory errors during training:
ResourceExhaustedError: OOM when allocating tensor with shape[786432,1604] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node dense_3/kernel/Initializer/random_uniform/RandomUniform}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Below, please find a full program that uses someone else's implementation of Google's BERT algorithm inside my own model. Please let me know what I can do to fix my error. Thank you!
...ANSWER
Answered 2020-Jan-10 at 07:58Check out this Out-of-memory issues section on their github page.
Often it's because that batch size or sequence length is too large to fit in the GPU memory, followings are the maximum batch configurations for a 12GB memory GPU, as listed in the above link
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