semvec/pypi-packages huggingface dataset:
slot-filling-ZC 0.2.4
train(train_file,test_file,vocab_intent_file,vocab_slot_file)

Intent and Slot Filling Bi-Model

train(train_file = "data/train_dev",
      test_file = "data/test",
      vocab_intent_file = "data/vocab.intent",
      vocab_slot_file = "data/vocab.slot",
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
      total_epoch = 50,
       max_len = 50,
       batch = 16,
       learning_rate = 0.001,
       DROPOUT = 0.2,
       embedding_size = 300,
       lstm_hidden_size = 200)

5 files would be generated after training:
model_intent_best.pt
model_slot_best.pt
word_dict.pickle
slot_dict.pickle
intent_dict.pickle

predict(sentence="find nonstop flights from salt lake city to new york on saturday april ninth.",
            data_path="/Users/chenzichu/Desktop/slot-filling/package/slot_ZC",
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
            max_len = 50,
            batch = 16,
            DROPOUT = 0.2,
            embedding_size = 300,
            lstm_hidden_size = 200)
tf-multilabelloss 0.0.5
it's implimentation of preceptron
Requires Python: >=3.7

# tf-multilabelloss
Create a multilabelloss which can help as when we working on multilabel classification model. 
meaning of multilabel classification is that:- 

* develop a single model that will provide binary classification predictions for each of the num_class

* In other words it will predict 'positive' or 'negative' for all class.


how to use tf-multilabelloss

```python
from multi_label_loss.multilabelloss import MultilabelLoss
predictions = Dense(len(num_class), activation="sigmoid")(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.compile(optimizer='adam', loss=MultilabelLoss(num_class),metrics=['binary_accuracy'])
```
## installation
```bash
pip install tf-multilabelloss
```