first version

README.md

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+# ProtBert-BFD finetuned on Rosetta 20AA dataset
+
+This model is finetuned to predict Rosetta fold energy using a dataset of 100k 20AA sequences.
+
+The starting pretrained model is from ProtTrans.
+It was trained on protein sequences using a masked language modeling (MLM) objective. It was introduced in
+[this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in
+[this repository](https://github.com/agemagician/ProtTrans).

config.json

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+{
+  "_name_or_path": "Rostlab/prot_bert_bfd",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
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+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 40000,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 30,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "regression",
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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training_args.bin

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vocab.txt

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+[PAD]
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