This library is developed and maintained by Kern AI. Push to the Branch ( git push origin feature/AmazingFeature)Īnd please don't forget to leave a ⭐ if you like the work! Licenseĭistributed under the MIT License.Commit your Changes ( git commit -m 'Add some AmazingFeature').Create your Feature Branch ( git checkout -b feature/AmazingFeature).You can also simply open an issue with the tag "enhancement". If you have a suggestion that would make this better, please fork the repo and create a pull request. Any contributions you make are greatly appreciated. run( data, data) ContributingĬontributions are what make the open source community such an amazing place to learn, inspire, and create. # executing this will call the refinery API with batches of size 32 callback. Return named_outputs callback = ModelCallback( # postprocessing shifts the model outputs into a format accepted by our API def postprocessing_fn( outputs, ** kwargs): # you can build initialization functions that set states of objects you use in the pipeline def initialize_fn( inputs, labels, ** kwargs): linear_model import LogisticRegression data = build_classification_dataset( client, "headline", "_clickbait", "distilbert-base-uncased" 0)Ĭlf. sklearn import build_classification_dataset from sklearn. inference import ModelCallback from refinery. run( test_loader, index) HuggingFace CallbackĬollect the dataset and train your custom transformer model as follows:įrom refinery. torch import TorchCallback callback = TorchCallback( Running_loss = 0.0 # with this model trained, you can use the callback from refinery. # forward propagation outputs = clf( inputs) Inputs, labels = data # set optimizer to zero grad to remove previous epoch gradients optimizer. Running_loss = 0.0 for i, data in enumerate( train_loader, 0): nn as nn import numpy as np import torch # number of features (len of X cols) input_dim = 768 # number of hidden layers hidden_layers = 20 # number of classes (unique of y) output_dim = 2 class Network( nn. # build your custom model and train it here - example: import torch. torch import build_classification_dataset train_loader, test_loader, encoder, index = build_classification_dataset(Ĭlient, "headline", "_clickbait", "distilbert-base-uncased" We've built an adapter with which you can easily create the required Rasa training data directly from refinery.įrom refinery. Refinery is perfect to be used for building chatbots with Rasa. save_model( "path/to/model") Rasa Adapter Small_train_dataset = tokenized_datasets. Training_args = TrainingArguments( output_dir = "test_trainer", evaluation_strategy = "epoch") compute( predictions = predictions, references = labels) Logits, labels = eval_pred predictions = np. Training_args = TrainingArguments( output_dir = "test_trainer") from_pretrained( "distilbert-base-uncased", num_labels = 2) Model = AutoModelForSequenceClassification. Return tokenizer( examples, padding = "max_length", truncation = True) from_pretrained( "distilbert-base-uncased") Import numpy as np from datasets import load_metric tokenizer = AutoTokenizer. select( range( 1000))ĪutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |