Hi,
First of all, a big thanks to the Hugging face team for bringing AutoNLP to us. A really amazing tool. I had the chance to try it out today and it looks very promising and interesting. I was trying to find a best model for a binary text classification task and the config of the best model is shown below:
{
  "_name_or_path": "AutoNLP",
  "_num_labels": 2,
  "architectures": [
    "BertForSequenceClassification"
  ],
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "id2label": {
    "0": "negative",
    "1": "non-negative"
  },
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "label2id": {
    "negative": 0,
    "non-negative": 1
  },
  "layer_norm_eps": 1e-12,
  "max_length": 96,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "pad_token_id": 0,
  "padding": "max_length",
  "position_embedding_type": "absolute",
  "problem_type": "single_label_classification",
  "transformers_version": "4.8.0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 30522
}

If I want to start from the scratch and replicate this best model, will I get the same results? From the config file, it seems the best model uses a Bert-large model, drop-out of 0.1, gelu activation. Where can I find the optimizer that was used/ learning rate/batch size and other things to replicate the code?
Say, for example, I am using the code from this Collab notebook, how can I replicate the best model from scratch?
Thanks again!!