معرفی شرکت ها
dnnnlp-1.1.4
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Deep Neural Networks for Natural Language Processing classification or sequential task written by PyTorch.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dnnnlp-1.1.4 |
| نام | dnnnlp |
| نسخه کتابخانه | 1.1.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | KzXuan |
| ایمیل نویسنده | kaizhouxuan@gmail.com |
| آدرس صفحه اصلی | https://github.com/NUSTM/pytorch-dnnnlp |
| آدرس اینترنتی | https://pypi.org/project/dnnnlp/ |
| مجوز | MIT |
## PyTorch - Deep Neural Network - Natural Language Processing
Version 1.1 by KzXuan
**Contains CNN, RNN and Transformer layers and models implemented by pytorch for classification and sequence labeling tasks in NLP.**
* Newly designed modules.
* Reduce usage complexity.
* Use `mask` as the sequence length identifier.
* Multi-GPU parallel for grid search.
<br>
### Dependecies
python 3.5+ & pytorch 1.2.0+
<br>
### [Install](https://pypi.org/project/dnnnlp/)
```bash
> pip install dnnnlp
```
<br>
### [API Document](./docs.md) (In Chinese)
* [Layer](./docs.md#Layer) - [layer.py](./dnnnlp/layer.py)
* [Model](./docs.md#Model) - [model.py](./dnnnlp/model.py)
* [Execution](./docs.md#Execution) - [exec.py](./dnnnlp/exec.py)
* [Utility](./docs.md#Utility) - [utils.py](./dnnnlp/utils.py)
<br>
### Hyperparameters
| Name | Type | Default | Description |
| ------------- | ----- | ----------- | -------------------------------------------------------------- |
| n_gpu | int | 1 | The number of GPUs (0 means no GPU acceleration). |
| space_turbo | bool | True | Accelerate with more GPU memories. |
| rand_seed | int | 100 | Random seed setting. |
| data_shuffle | bool | Ture | Disrupt data for training. |
| emb_type | str | None | Embedding modes contain None, 'const' or 'variable'. |
| emb_dim | int | 300 | Embedding dimension (or feature dimension). |
| n_class | int | 2 | Number of target classes. |
| n_hidden | int | 50 | Number of hidden nodes, or output channels of CNN. |
| learning_rate | float | 0.01 | Learning rate. |
| l2_reg | float | 1e-6 | L2 regular. |
| batch_size | int | 32 | Number of samples for one batch. |
| iter_times | int | 30 | Number of iterations. |
| display_step | int | 2 | The number of iterations between each output of the result. |
| drop_prob | float | 0.1 | Dropout ratio. |
| eval_metric | str | 'accuracy' | Evaluation metrics contain 'accuracy', 'macro', 'class1', etc. |
<br>
### Usage
```python
# import our modules
from dnnnlp.model import RNNModel
from dnnnlp.exec import default_args, Classify
# load the embedding matrix
emb_mat = np.array((-1, 300))
# load the train data
train_x = np.array((800, 50))
train_y = np.array((800,))
train_mask = np.array((800, 50))
# load the test data
test_x = np.array((200, 50))
test_y = np.array((200,))
test_mask = np.array((200, 50))
# get the default arguments
args = default_args()
# modify part of the arguments
args.space_turbo = False
args.n_hidden = 100
args.batch_size = 32
```
* Classification
```python
# initilize a model
model = RNNModel(args, emb_mat, bi_direction=False, rnn_type='GRU', use_attention=True)
# initilize a classifier
nn = Classify(model, args, train_x, train_y, train_mask, test_x, test_y, test_mask)
# do training and testing
evals = nn.train_test(device_id=0)
```
* Run several times and get the average score.
````python
# initilize a model
model = CNNModel(args, emb_mat, kernel_widths=[2, 3, 4])
# initilize a classifier
nn = Classify(model, args, train_x, train_y, train_mask)
# run the model several times
avg_evals = average_several_run(nn.train_test, args, n_times=8, n_paral=4, fold=5)
````
* Parameters' grid search.
````python
# initilize a model
model = TransformerModel(args, n_layer=12, n_head=8)
# initilize a classifier
nn = Classify(model, args, train_x, train_y, train_mask, test_x, test_y, test_mask)
# set searching params
params_search = {'learning_rate': [0.1, 0.01], 'n_hidden': [50, 100]}
# run grid search
max_evals = grid_search(nn, nn.train_test, args, params_search)
````
* Sequence labeling
```python
from dnnnlp.model import RNNCRFModel
from dnnnlp.exec import default_args, SequenceLabeling
# load the train data
train_x = np.array((1000, 50))
train_y = np.array((1000, 50))
train_mask = np.array((1000, 50))
# initilize a model
model = RNNCRFModel(args)
# initilize a labeler
nn = SequenceLabeling(model, args, train_x, train_y, train_mask)
# do cross validation
nn.cross_validation(fold=5)
```
<br>
### History
**version 1.1**
* Add `CRFLayer`: packaging CRF for both training and testing.
* Add `RNNCRFModel`: a integrated RNN-CRF sequence labeling model.
* Add `SequenceLabeling`: a sequence labeling execution module that inherits from Classify.
* Fix errors in judging whether a tensor is None.
**version 1.0**
* Rename project `dnn` to `dnnnlp`.
* Remove file `base`, add file `utils`.
* Optimize and rename `SoftmaxLayer` and `SoftAttentionLayer`.
* Rewrite and rename `EmbeddingLayer`, `CNNLayer` and `RNNLayer`.
* Rewrite `MultiheadAttentionLayer`: a packaging attention layer based on `nn.MultiheadAttention`.
* Rewrite `TransformerLayer`: support new `MultiheadAttentionLayer`.
* Optimize and rename `CNNModel`, `RNNModel` and `TransformerModel`.
* Optimize and rename `Classify`: a highly applicable classification execution module.
* Rewrite `average_several_run` and `grid_search`: support multi-GPU parallel.
* Support pytorch 1.2.0.
**version 0.12**
* Update `RNN_layer`: fully support for tanh, LSTM and GRU.
* Fix errors in some mask operations.
* Support pytorch 1.1.0.
Old version [0.12.3](https://github.com/NUSTM/pytorch-dnnnlp/tree/8d2d6c4e432076e13020ae54954aa419f3bb9bce).
**version 0.11**
* Provides an acceleration method by using more GPU memories.
* Fix the problem of memory consumption caused by abnormal data reading.
* Add `multi_head_attention_layer`: packaging multi-head attention for Transformer.
* Add `Transformer_layer` and `Transformer_model`: packaging Transformer layer and model written by ourself.
* Support data disruption for training.
**version 0.10**
* Split the code into four files: `base`, `layer`, `model`, `exec`.
* Add `CNN_layer` and `CNN_model`: packaging CNN layer and model.
* Support multi-GPU parallel for each model.
**version 0.9**
* Fix the problem of output format.
* Fix the statistical errors in cross-validation part of `LSTM_classify`.
* Rename: `LSTM_model` to `RNN_layer`, `self_attention` to `self_attention_layer`.
* Add `softmax_layer`: a packaging fully-connected layer.
**version 0.8**
* Adjust the applicability of functions in `LSTM_classify` to avoid rewriting in `LSTM_sequence`.
* Optimize the way of parameter transfer.
* A more complete evaluation mechanism.
**version 0.7**
* Add `LSTM_sequence`: a sequence labeling module for `LSTM_model`.
* Fix the nan-value problem in hierarchical classification.
* Support pytorch 1.0.0.
**version 0.6**
* Update `LSTM_classify`: support hierarchical classification.
* The `GRU_model` is merged into the `LSTM_model`.
* Adapt to CPU operation.
**version 0.5**
* Split the running part of `LSTM_classify` to reduce the rewrite of custom models.
* Add control for visual output.
* Create function `average_several_run`: support to get the average score after several training and testing.
* Create function `grid_search`: support parameters' grid search.
**version 0.4**
* Add `GRU_model`: a packaging GRU model based on `nn.GRU`.
* Support L2 regular.
**version 0.3**
* Add `self_attention`: provides attention mechanism support.
* Update `LSTM_classify`: adapts to complex custom models.
**version 0.2**
* Support mode selection of embedding.
* Default usage of `nn.Dropout`.
* Create function `default_args` to provide default hyperparameters.
**version 0.1**
* Initilization of project `dnn`: based on pytorch 0.4.1.
* Add `LSTM_model`: a packaging LSTM model based on `nn.LSTM`.
* Add `LSTM_classify`: a classification module for LSTM model, which supports train-test and corss-validation.
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| >=0.21.0 | scikit-learn |
| >=1.2.0 | torch |
| >=0.7.2 | torchcrf |
نحوه نصب
نصب پکیج whl dnnnlp-1.1.4:
pip install dnnnlp-1.1.4.whl
نصب پکیج tar.gz dnnnlp-1.1.4:
pip install dnnnlp-1.1.4.tar.gz