[ot][spam]gpt-4 completions

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

{"time": "2023-03-18T17:52:54.425808", "lm": "spellbook", "url":
"https://dashboard.scale.com/spellbook/api/v2/deploy/", "input":
"Modify the initial portion of a huggingface decoder transformer such
that it accepts a sentence encoding in addition to token embeddings.",
"output": "To modify the initial portion of a Hugging Face decoder
transformer to accept a sentence encoding in addition to token
embeddings, you can create a custom class that inherits from the
original decoder class and then override the necessary methods. Here's
an example using the `BertDecoder` class from the `transformers`
library:\n\n```python\nfrom transformers import BertConfig,
BertModel\nfrom transformers.models.bert.modeling_bert import
BertDecoder\n\nclass CustomBertDecoder(BertDecoder):\n    def
__init__(self, config):\n        super().__init__(config)\n\n    def
forward(\n        self,\n        input_ids=None,\n
sentence_encoding=None,\n        attention_mask=None,\n
token_type_ids=None,\n        position_ids=None,\n
head_mask=None,\n        inputs_embeds=None,\n
encoder_hidden_states=None,\n        encoder_attention_mask=None,\n
    past_key_values=None,\n        use_cache=None,\n
output_attentions=None,\n        output_hidden_states=None,\n
return_dict=None,\n    ):\n        # Add sentence encoding to the
token embeddings\n        if inputs_embeds is None:\n
inputs_embeds = self.word_embeddings(input_ids)\n        if
sentence_encoding is not None:\n            inputs_embeds =
inputs_embeds + sentence_encoding.unsqueeze(1)\n\n        return
super().forward(\n            attention_mask=attention_mask,\n
   token_type_ids=token_type_ids,\n
position_ids=position_ids,\n            head_mask=head_mask,\n
   inputs_embeds=inputs_embeds,\n
encoder_hidden_states=encoder_hidden_states,\n
encoder_attention_mask=encoder_attention_mask,\n
past_key_values=past_key_values,\n            use_cache=use_cache,\n
         output_attentions=output_attentions,\n
output_hidden_states=output_hidden_states,\n
return_dict=return_dict,\n        )\n\n# Example usage\nconfig =
BertConfig()\nmodel = BertModel(config)\ncustom_decoder =
CustomBertDecoder(config)\n\n# Get sentence encoding from the
encoder\nencoder_output = model(input_ids)\nsentence_encoding =
encoder_output.last_hidden_state[:, 0]\n\n# Pass sentence encoding to
the custom decoder\ndecoder_output =
custom_decoder(input_ids=input_ids,
sentence_encoding=sentence_encoding)\n```\n\nIn this example, we
create a `CustomBertDecoder` class that inherits from `BertDecoder`.
We override the `forward` method to accept an additional
`sentence_encoding` argument. We then add the sentence encoding to the
token embeddings before passing them to the original `forward` method
of the `BertDecoder` class.\n\nThis example assumes that the sentence
encoding is a vector that can be added to the token embeddings. You
may need to adjust the dimensions or operations depending on your
specific use case."}