Create Embeddings_PyT_TF.py

Embeddings_PyT_TF.py

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+Using Pytorch:
+
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained('bert-base-uncased')
+input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
+outputs = model(input_ids)
+last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+Using Tensorflow:
+
+import tensorflow as tf
+from transformers import BertTokenizer, TFBertModel
+
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = TFBertModel.from_pretrained('bert-base-uncased')
+input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
+outputs = model(input_ids)
+last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple