semvec/pypi-packages huggingface dataset:
pyyjson 0.2.0
yet another json libs using yyjson
License: MIT
Requires Python: >=3.3

# pyyjson

Yet another Json Parser for Python
- based on yyjson bindings from @TkTech's ([py_yyjson](https://github.com/ibireme/yyjson))
- original c library: [yyjson](https://github.com/ibireme/yyjson)
- benchmarks are based on [ultrajson](https://github.com/ultrajson/ultrajson)


### Supports
it supports standard `json` lib's
- dumps
- loads
- dump
- load

functions


### loads examples
```python
>>> import pyyjson
>>> pyyjson.loads("[1,2,3]")
[1, 2, 3]
>>> pyyjson.loads('[{"a":"b"}, 3, 4]')
[{'a': 'b'}, 3, 4]
```

### dumps example
```python
>>> pyyjson.dumps([{'a': 'b'}, 3, 4])
'[{"a":"b"},3,4]'
```

### load example

`load` take either string or file object. If string is given, it is assumed that it's a path to json file


```python
>>> import pyyjson
>>> pyyjson.load("simple_json.json") # in "tests/" directory
{'a': 1, 'b': 2, 'c': 3}
```

### dump example

`dump` take either string or file object. If string is given, it is assumed that it's a path to save the file

```python
>>> import pyyjson
>>> pyyjson.dump({'a': 1, 'b': 2, 'c': 3}, "simple_json.json")
```

### Benchmarks
each elements in cols denotes "calls/sec". Test suite is adapted from ujson's benchmark format.

|         content         |        |  json | ujson | orjson | pyyjson(mine) |
|:-----------------------:|:------:|:-----:|:-----:|:------:|:-------------:|
|    Arr of 256 doubles   | ENCODE |  10119 | 35361 | 170829 |44925        |
|    Arr of 256 dobules   | DECODE | 28684 | 54593 | 170383 |     155127    |
| Arr of 256 UTF-8 string | ENCODE |  5247 |  7344 |  45684 |      8554     |
| Arr of 256 UTF-8 String | DECODE |  2838 |  5223 |  7248  |      6424     |
|    Arr of 256 strings   | ENCODE | 36624 | 59568 | 216707 |     87624     |
|    Arr of 256 strings   | DECODE | 66842 | 66960 |  100252 |     98242     |
|  Medium complex object  | ENCODE |  9948 | 22344 |  80465 |     30888     |
|  Medium complex object  | DECODE | 17183 | 28733 |  45008 |     29267     |
transformers-helper 0.0.7
Transformers helper
License: MIT

Supported models 

<pre>
import transformers_helper

#ANN
transformers_helper.models.ANNConfig
transformers_helper.models.ANNForTabularRegression
transformers_helper.models.AnnForTabularBinaryClassification
transformers_helper.models.AnnForTabularClassification
transformers_helper.models.TabularFeatureExtractor

#CNN
transformers_helper.models.CNNConfig
transformers_helper.models.CNNForImageClassification
transformers_helper.models.GrayscaleImageProcessor
transformers_helper.models.CNNForKeyPointDetection

#RNN
transformers_helper.models.RNNConfig
transformers_helper.models.RNNForAudioClassification
transformers_helper.models.RNNForTimeSeriesRegression

#Embedded RNN
transformers_helper.models.EmbeddedRNNConfig
transformers_helper.models.EmbeddedRNNForSequenceClassification
transformers_helper.models.EmbeddedRNNForFixedLengthTranslation
transformers_helper.models.PretrainedEmbeddedRNNForSequenceClassification
transformers_helper.models.TorchtextTokenizer

#Embedded 1DCNN
transformers_helper.models.Embedded1DCNNConfig
transformers_helper.models.Embedded1DCNNForSequenceClassification

#Faster RCNN
transformers_helper.models.FasterRCNNConfig
transformers_helper.models.FasterRCNNForObjectDetection
transformers_helper.models.FasterRCNNImageProcessor
</pre>

<pre>
import transformers_helper

#Custom Bert
transformers_helper.models.CustomBertConfig
transformers_helper.models.CustomBertForSequenceClassification

#Custom Wav2Vec2
transformers_helper.models.CustomWav2Vec2FeatureExtractor
</pre>

Supported pipelines

<pre>
import transformers_helper

transformers_helper.pipelines.FixedLengthTranslationPipeline
transformers_helper.pipelines.TabularBinaryClassificationPipeline
transformers_helper.pipelines.TabularClassificationPipeline
transformers_helper.pipelines.TabularRegressionPipeline
</pre>
<pre>
import transformers_helper

transformers_helper.pipelines.CustomImageClassificationPipeline
</pre>
TinyShakespeareLoader 1.0.7
This is a PyTorch DataLoader for the TinyShakespeare dataset.
License: MIT
Requires Python: >=3.8


# TinyShakespeareLoader

## A PyTorch DataLoader for the TinyShakespeare Dataset

If you followed Andrej Karparthy's tutorial on GPT, you will notice he used the TinyShakespeare dataset, but not with the PyTorch DataLoader.
This repository fills that gap.

The TinyShakespeare dataset is a small dataset of Shakespeare's plays, with each line as a separate sample. To install this package, simply run:

```console

    pip install TinyShakespeareLoader

```

Then, to use it, simply import it and use it as a PyTorch DataLoader:

```python
    from TinyShakespeareLoader.hamlet import get_data


    data = get_data()

    train_dataloader, test_dataloader = data["train_dataloader"], data["test_dataloader"]

    for batch in train_dataloader:
        print(batch)

```