End of training

README.md

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+---
+base_model: AIRI-Institute/gena-lm-bert-base-t2t-multi
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: gena-lm-bert-base-t2t-multi_ft_BioS45_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-multi_ft_BioS45_1kbpHG19_DHSs_H3K27AC
+
+This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t-multi](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t-multi) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4693
+- F1 Score: 0.8489
+- Precision: 0.8151
+- Recall: 0.8855
+- Accuracy: 0.8355
+- Auc: 0.8880
+- Prc: 0.8543
+
+## 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    |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
+| 0.7045        | 0.2103 | 500  | 0.6716          | 0.3608   | 0.8731    | 0.2274 | 0.5797   | 0.7448 | 0.7630 |
+| 0.6504        | 0.4207 | 1000 | 0.6005          | 0.7373   | 0.7184    | 0.7573 | 0.7186   | 0.7845 | 0.7959 |
+| 0.5759        | 0.6310 | 1500 | 0.5370          | 0.7482   | 0.7872    | 0.7129 | 0.7497   | 0.7960 | 0.7969 |
+| 0.5182        | 0.8414 | 2000 | 0.5214          | 0.8035   | 0.7299    | 0.8935 | 0.7720   | 0.8064 | 0.7516 |
+| 0.4665        | 1.0517 | 2500 | 0.4835          | 0.8199   | 0.8304    | 0.8097 | 0.8145   | 0.8676 | 0.8310 |
+| 0.463         | 1.2621 | 3000 | 0.4728          | 0.8318   | 0.7679    | 0.9073 | 0.8086   | 0.8709 | 0.8363 |
+| 0.441         | 1.4724 | 3500 | 0.4638          | 0.8316   | 0.8067    | 0.8581 | 0.8187   | 0.8770 | 0.8401 |
+| 0.4178        | 1.6828 | 4000 | 0.4333          | 0.8358   | 0.8040    | 0.8702 | 0.8216   | 0.8940 | 0.8833 |
+| 0.4165        | 1.8931 | 4500 | 0.4512          | 0.8387   | 0.8095    | 0.8702 | 0.8254   | 0.8851 | 0.8599 |
+| 0.4082        | 2.1035 | 5000 | 0.4773          | 0.8361   | 0.8288    | 0.8435 | 0.8275   | 0.8801 | 0.8592 |
+| 0.4006        | 2.3138 | 5500 | 0.4735          | 0.8453   | 0.8066    | 0.8879 | 0.8305   | 0.8766 | 0.8257 |
+| 0.4053        | 2.5242 | 6000 | 0.4654          | 0.8500   | 0.8033    | 0.9024 | 0.8338   | 0.8930 | 0.8661 |
+| 0.4101        | 2.7345 | 6500 | 0.4794          | 0.8493   | 0.8059    | 0.8976 | 0.8338   | 0.8637 | 0.8114 |
+| 0.4299        | 2.9449 | 7000 | 0.5050          | 0.8069   | 0.8732    | 0.75   | 0.8128   | 0.9019 | 0.8977 |
+| 0.3828        | 3.1552 | 7500 | 0.6362          | 0.7813   | 0.8957    | 0.6927 | 0.7976   | 0.8789 | 0.8808 |
+| 0.4132        | 3.3656 | 8000 | 0.4565          | 0.8484   | 0.8130    | 0.8871 | 0.8347   | 0.9009 | 0.8765 |
+| 0.383         | 3.5759 | 8500 | 0.4693          | 0.8489   | 0.8151    | 0.8855 | 0.8355   | 0.8880 | 0.8543 |
+
+
+### 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-multi",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "auto_map": {
+    "AutoModel": "AIRI-Institute/gena-lm-bert-base-t2t-multi--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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+size 442503040

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": {
+    "content": "[MASK]",
+    "lstrip": false,
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+    "single_word": false
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+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "1": {
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+      "special": true
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+      "special": true
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+    "3": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+    "4": {
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+      "lstrip": false,
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+    "5": {
+      "content": "-",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "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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