Model Api Pt

Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']
- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

<class 'transformers.models.bert.modeling_bert.BertModel'>
<class 'transformers.models.gpt2.modeling_gpt2.GPT2Model'>

Some weights of the model checkpoint at facebook/bart-base were not used when initializing BartModel: ['final_logits_bias']
- This IS expected if you are initializing BartModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BartModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

<class 'transformers.models.bart.modeling_bart.BartModel'>

<class 'transformers.models.bert.configuration_bert.BertConfig'>
<class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'>
<class 'transformers.models.bart.configuration_bart.BartConfig'>

<class 'transformers.models.bert.configuration_bert.BertConfig'>

<class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'>

<class 'transformers.models.bart.configuration_bart.BartConfig'>

BertConfig {
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.7.0.dev0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}