semvec/pypi-packages huggingface dataset:
tfscc3d 1.0.1
Sequence categorial feature columns allows 3D "ID tensor" for TensorFlow
License: MIT

# tfscc3d

Replacements for tf.contrib.feature_column.sequence_categorical_column_with_hash_bucket and tf.contrib.feature_column.sequence_categorical_column_with_vocabulary_list that supports 3D shape sequence inputs.


## Usage

Same as original sequence feature columns.
tfembedhub 1.2.1
TensorFlow Hub module producer for text embedding lookup
License: MIT

# tfembedhub

Convert embeddings vectors (.txt format) into TensorFlowHub embedding lookup module.

## How to

1. Save lookup keys and embeddings values in text file.

Keys should be in first column. Other columns treated as embedding values. Any space-like characters allowed as columns separator.

Non-existing keys will refer to "<UNQ>" key embeddings. You may provide embedding values for that, otherwise it will be initialized with zeros.

```
key1 1. 2. 3.
key2 4. 5. 6.
<UNQ> 0. -1. 0.
```

2. Sonvert saved embeddings into TF Hub Module with "tfembedhub-convert" command.
```bash
tfembedhub-convert vectors.txt vectors-hub/
```

3. Use embedding hub via columns in your estimator.
```python
from tfembedhub text_embedding_column, sequence_text_embedding_column

my_words_embedding = sequence_text_embedding_column(
    key='sparse_key_from_features',
    module_spec='path/to/my/hub'
)

# Then pass my_words_embedding to estimator "columns" list.
```
tfseqestimator 2.2.1
Sequence estimators for Tensorflow
License: MIT

# tfseqestimator

Sequence estimators for TensorFlow.


## Available estimators

- FullSequenceClassifier: one class for a whole sequence
- FullSequenceRegressor: one value for a whole sequence
- SequenceItemsClassifier: one class for each sequence item
- SequenceItemsRegressor: one value for each sequence item


Usage
-----

```python
from tfseqestimator import FullSequenceClassifier, RnnType
import tensorflow.contrib.feature_column as contrib_features

token_sequence = contrib_features.sequence_categorical_column_with_hash_bucket(...)
token_emb = contrib_features.embedding_column(categorical_column=token_sequence, ...)

estimator = FullSequenceClassifier(
    sequence_feature_columns=[token_emb],
    rnn_type=RnnType.REGULAR_STACKED_LSTM,
    rnn_layers=[32, 16]
)

# Input builders
def input_fn_train: # returns x, y
  pass
estimator.train(input_fn=input_fn_train, steps=100)

def input_fn_eval: # returns x, y
  pass
metrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)

def input_fn_predict: # returns x, None
  pass
predictions = estimator.predict(input_fn=input_fn_predict)
```