Metadata-Version: 2.1
Name: keras-xlnet
Version: 0.20.0
Summary: XLNet implemented in Keras
Home-page: https://github.com/CyberZHG/keras-xlnet
Author: CyberZHG
Author-email: CyberZHG@users.noreply.github.com
License: MIT
Description: # Keras XLNet
        
        [![Version](https://img.shields.io/pypi/v/keras-xlnet.svg)](https://pypi.org/project/keras-xlnet/)
        [License](https://img.shields.io/pypi/l/keras-xlnet.svg)
        
        \[[中文](https://github.com/CyberZHG/keras-xlnet/blob/master/README.zh-CN.md)|[English](https://github.com/CyberZHG/keras-xlnet/blob/master/README.md)\]
        
        Unofficial implementation of [XLNet](https://arxiv.org/pdf/1906.08237). [Embedding extraction](demo/extract/token_embeddings.py) and [embedding extract with memory](demo/extract/token_embeddings_with_memory.py) show how to get the outputs of the last transformer layer using pre-trained checkpoints.
        
        ## Install
        
        ```bash
        pip install keras-xlnet
        ```
        
        ## Usage
        
        ### Fine-tuning on GLUE
        
        Click the task name to see the demos with base model:
        
        |Task Name                       |Metrics                       |Approximate Results on Dev Set|
        |:-------------------------------|:----------------------------:|----:|
        |[CoLA](demo/GLUE/CoLA/cola.py)  |Matthew Corr.                 |52   |
        |[SST-2](demo/GLUE/SST-2/sst2.py)|Accuracy                      |93   |
        |[MRPC](demo/GLUE/MRPC/mrpc.py)  |Accuracy/F1                   |86/89|
        |[STS-B](demo/GLUE/STS-B/stsb.py)|Pearson Corr. / Spearman Corr.|86/87|
        |[QQP](demo/GLUE/QQP/qqp.py)     |Accuracy/F1                   |90/86|
        |[MNLI](demo/GLUE/MNLI/mnli.py)  |Accuracy                      |84/84|
        |[QNLI](demo/GLUE/QNLI/qnli.py)  |Accuracy                      |86   |
        |[RTE](demo/GLUE/RTE/rte.py)     |Accuracy                      |64   |
        |[WNLI](demo/GLUE/WNLI/wnli.py)  |Accuracy                      |56   |
        
        (Only 0s are predicted in WNLI dataset)
        
        ### Load Pretrained Checkpoints
        
        ```python
        import os
        from keras_xlnet import Tokenizer, load_trained_model_from_checkpoint, ATTENTION_TYPE_BI
        
        checkpoint_path = '.../xlnet_cased_L-24_H-1024_A-16'
        
        tokenizer = Tokenizer(os.path.join(checkpoint_path, 'spiece.model'))
        model = load_trained_model_from_checkpoint(
            config_path=os.path.join(checkpoint_path, 'xlnet_config.json'),
            checkpoint_path=os.path.join(checkpoint_path, 'xlnet_model.ckpt'),
            batch_size=16,
            memory_len=512,
            target_len=128,
            in_train_phase=False,
            attention_type=ATTENTION_TYPE_BI,
        )
        model.summary()
        ```
        
        Arguments `batch_size`, `memory_len` and `target_len` are maximum sizes used for initialization of memories. The model used for training a language model is returned if `in_train_phase` is `True`, otherwise a model used for fine-tuning will be returned.
        
        ### About I/O
        
        **Note that** `shuffle` should be `False` in either `fit` or `fit_generator` if memories are used. 
        
        #### `in_train_phase` is `False`
        
        3 inputs:
        
        * IDs of tokens, with shape `(batch_size, target_len)`.
        * IDs of segments, with shape `(batch_size, target_len)`.
        * Length of memories, with shape `(batch_size, 1)`.
        
        1 output:
        
        * The feature for each token, with shape `(batch_size, target_len, units)`.
        
        #### `in_train_phase` is `True`
        
        4 inputs:
        
        * IDs of tokens, with shape `(batch_size, target_len)`.
        * IDs of segments, with shape `(batch_size, target_len)`.
        * Length of memories, with shape `(batch_size, 1)`.
        * Masks of tokens, with shape `(batch_size, target_len)`.
        
        1 output:
        
        * The probability of each token in each position, with shape `(batch_size, target_len, num_token)`.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
