I have a pandas data frame, containing two columns: sentences and annotations:
Col 0 | Sentence | Annotation |
---|---|---|
1 | [This, is, sentence] | [l1, l2, l3] |
2 | [This, is, sentence, too] | [l1, l2, l3, l4] |
There are several things I need to do:
split to features and labels
split into train-val-test data
vectorize train data using:
vectorize_layer = tf.keras.layers.TextVectorization(
max_tokens=maxlen,
standardize='lower',
split='whitespace',
ngrams=(1,3),
output_mode='tf-idf',
pad_to_max_tokens=True,)
I haven't worked with tensors before so I am a little confused about how to order the steps above and access the information from the tensors. Specifically, at what point do I have to split into features and labels, and how to access one or the other? Then, should I split into features and labels before splitting to train-val-test (I want to make it right and not use sklearn's train_test_split when I work with tensorflow) or it is the opposite?
You can split your dataset before creating a model. After splitting you need to tokenize your sentences using
tensorflow.keras.preprocessing.text.Tokenizer((num_words = vocab_size, oov_token=oov_tok)
After tokenizing you need to add padding to the sentence using
training_padded = pad_sequences(training_sequences, maxlen=max_length, truncating = trunc_type)
Then you can train your model with the data. For more details please refer to this working code example. Thank You.