End of training

README.md

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+---
+base_model: AIRI-Institute/gena-lm-bert-base-t2t
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
+  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. -->
+
+# gena-lm-bert-base-t2t_ft_BioS2_1kbpHG19_DHSs_H3K27AC
+
+This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7056
+- F1 Score: 0.1876
+- Precision: 0.5195
+- Recall: 0.1145
+- Accuracy: 0.4764
+- Auc: 0.4935
+- Prc: 0.5260
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | F1 Score | Precision | Recall | Accuracy | Auc    | Prc    |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
+| 0.7004        | 0.0840 | 500  | 0.7056          | 0.1876   | 0.5195    | 0.1145 | 0.4764   | 0.4935 | 0.5260 |
+| 0.6952        | 0.1680 | 1000 | 0.7056          | 0.1876   | 0.5195    | 0.1145 | 0.4764   | 0.4935 | 0.5260 |
+| 0.6977        | 0.2520 | 1500 | 0.7056          | 0.1876   | 0.5195    | 0.1145 | 0.4764   | 0.4935 | 0.5260 |
+| 0.7008        | 0.3360 | 2000 | 0.7056          | 0.1876   | 0.5195    | 0.1145 | 0.4764   | 0.4935 | 0.5260 |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.3.0+cu121
+- Datasets 2.18.0
+- Tokenizers 0.19.0

config.json

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+{
+  "_name_or_path": "AIRI-Institute/gena-lm-bert-base-t2t",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "AIRI-Institute/gena-lm-bert-base-t2t--modeling_bert.BertForMaskedLM"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "last_layer_norm": false,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 3,
+  "position_embedding_type": "absolute",
+  "pre_layer_norm": true,
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

model.safetensors

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special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
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+    "lstrip": false,
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+    "single_word": false
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+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]"
+}

training_args.bin

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