reset remote

README.md

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----
-tags:
-- generated_from_trainer
-metrics:
-- f1
-- accuracy
-model-index:
-- name: learn2therm
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# learn2therm
-
-This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.6942
-- F1: 0.0
-- Accuracy: 0.5125
-- Matthew: -0.0308
-- Cfm: [1025, 2, 973, 0]
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 2
-- gradient_accumulation_steps: 25
-- total_train_batch_size: 1600
-- total_eval_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 1
-- mixed_precision_training: Native AMP
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.26.0
-- Pytorch 2.0.1
-- Datasets 2.12.0
-- Tokenizers 0.13.3

config.json

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-{
-  "_name_or_path": "Rostlab/prot_bert",
-  "architectures": [
-    "BertForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "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": "single_label_classification",
-  "torch_dtype": "float32",
-  "transformers_version": "4.26.0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30
-}

pytorch_model.bin

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-size 1680220533

special_tokens_map.json

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-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}

tokenizer.json

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-{
-  "version": "1.0",
-  "truncation": {
-    "direction": "Right",
-    "max_length": 512,
-    "strategy": "LongestFirst",
-    "stride": 0
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-  "padding": {
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-      "Fixed": 512
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-    "pad_token": "[PAD]"
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-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
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-    {
-      "id": 1,
-      "content": "[UNK]",
-      "single_word": false,
-      "lstrip": false,
-      "rstrip": false,
-      "normalized": false,
-      "special": true
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-      "normalized": false,
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-  "normalizer": {
-    "type": "BertNormalizer",
-    "clean_text": true,
-    "handle_chinese_chars": true,
-    "strip_accents": null,
-    "lowercase": false
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-  "pre_tokenizer": {
-    "type": "BertPreTokenizer"
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-  "post_processor": {
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tokenizer_config.json

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-{
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-  "do_basic_tokenize": true,
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-  "mask_token": "[MASK]",
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-  "name_or_path": "Rostlab/prot_bert",
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
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-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]"
-}

training_args.bin

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vocab.txt

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-[PAD]
-[UNK]
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-[MASK]
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