it was the first structure to reach a height of 300 metres in paris in 1930. "the eiffel tower surpassed the washington monument to become the tallest structure in the world. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft).Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.", It was the first structure to reach a height of 300 metres. Its base is square, measuring 125 metres (410 ft) on each side.During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. > _start_token_id = tokenizer.cls_token_id > model = om_encoder_decoder_pretrained( "bert-base-uncased", "bert-base-uncased") > tokenizer = om_pretrained( "bert-base-uncased") If there are only pytorchĬheckpoints for a particular encoder-decoder model, a workaround is:Ĭopied > from transformers import BertTokenizer, EncoderDecoderModel Passing from_pt=True to this method will throw an exception. om_pretrained() currently doesn’t support initializing the model from a Loading a PyTorch checkpoint into TFEncoderDecoderModel. pg & e said it scheduled the blackouts to last through at least midday tomorrow. nearly 800, 000 customers were expected to be affected by high winds amid dry conditions. the aim is to reduce the risk of wildfires. Nearly 800 thousand customers were affected by the shutoffs. > generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens= True) > # autoregressively generate summary (uses greedy decoding by default) > generated_ids = model.generate(input_ids) > input_ids = tokenizer(ARTICLE_TO_SUMMARIZE, return_tensors= "pt").input_ids "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.". The aim is to reduce the risk of wildfires. "PG&E stated it scheduled the blackouts in response to forecasts for high winds ". > # let's perform inference on a long piece of text > ARTICLE_TO_SUMMARIZE = ( > tokenizer = om_pretrained( "patrickvonplaten/bert2bert_cnn_daily_mail") > # load a fine-tuned seq2seq model and corresponding tokenizer > model = om_pretrained( "patrickvonplaten/bert2bert_cnn_daily_mail") To do so, the EncoderDecoderModel class provides a om_encoder_decoder_pretrained() method.Ĭopied > from transformers import AutoTokenizer, EncoderDecoderModel Initializing EncoderDecoderModel from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post. decoder of BART, can be used as the decoder.ĭepending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. BERT, pretrained causal language models, e.g. BERT, can serve as the encoder and both pretrained auto-encoding models, e.g. Note that any pretrained auto-encoding model, e.g. > model = EncoderDecoderModel(config=config) Initialising EncoderDecoderModel from a pretrained encoder and a pretrained decoder.ĮncoderDecoderModel can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. > config = om_encoder_decoder_configs(config_encoder, config_decoder) In the following example, we show how to do this using the default BertModel configuration for the encoder and the default BertForCausalLM configuration for the decoder.Ĭopied > from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel Randomly initializing EncoderDecoderModel from model configurations.ĮncoderDecoderModel can be randomly initialized from an encoder and a decoder config. Sascha Rothe, Shashi Narayan, Aliaksei Severyn.Īfter such an EncoderDecoderModel has been trained/fine-tuned, it can be saved/loaded just likeĪny other models (see the examples for more information).Īn application of this architecture could be to leverage two pretrained BertModel as the encoderĪnd decoder for a summarization model as was shown in: Text Summarization with Pretrained Encoders by Yang Liu and Mirella Lapata. Was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks Pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any
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