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_BioS74_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_BioS74_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: 0.5006
+- F1 Score: 0.8354
+- Precision: 0.7947
+- Recall: 0.8805
+- Accuracy: 0.8183
+- Auc: 0.8820
+- Prc: 0.8475
+
+## 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    |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
+| 0.7035        | 0.1314 | 500  | 0.6627          | 0.7293   | 0.5961    | 0.9392 | 0.6350   | 0.7403 | 0.7568 |
+| 0.6574        | 0.2629 | 1000 | 0.5876          | 0.7377   | 0.7815    | 0.6986 | 0.7399   | 0.8086 | 0.8004 |
+| 0.5656        | 0.3943 | 1500 | 0.5193          | 0.7822   | 0.7711    | 0.7936 | 0.7686   | 0.8457 | 0.8246 |
+| 0.5073        | 0.5258 | 2000 | 0.4790          | 0.8163   | 0.7550    | 0.8885 | 0.7907   | 0.8535 | 0.8218 |
+| 0.4742        | 0.6572 | 2500 | 0.4942          | 0.8198   | 0.7632    | 0.8855 | 0.7962   | 0.8618 | 0.8403 |
+| 0.4972        | 0.7886 | 3000 | 0.4656          | 0.8266   | 0.7614    | 0.9041 | 0.8015   | 0.8676 | 0.8439 |
+| 0.4617        | 0.9201 | 3500 | 0.4728          | 0.8308   | 0.7787    | 0.8905 | 0.8101   | 0.8728 | 0.8534 |
+| 0.4659        | 1.0515 | 4000 | 0.5274          | 0.7788   | 0.8388    | 0.7268 | 0.7839   | 0.8769 | 0.8621 |
+| 0.4604        | 1.1830 | 4500 | 0.4604          | 0.8263   | 0.7852    | 0.8719 | 0.8080   | 0.8802 | 0.8660 |
+| 0.4592        | 1.3144 | 5000 | 0.4634          | 0.8151   | 0.8144    | 0.8157 | 0.8062   | 0.8860 | 0.8719 |
+| 0.451         | 1.4458 | 5500 | 0.4865          | 0.8174   | 0.8165    | 0.8182 | 0.8086   | 0.8856 | 0.8731 |
+| 0.4372        | 1.5773 | 6000 | 0.4993          | 0.8333   | 0.7882    | 0.8840 | 0.8149   | 0.8783 | 0.8563 |
+| 0.4654        | 1.7087 | 6500 | 0.4827          | 0.8290   | 0.7884    | 0.8739 | 0.8112   | 0.8852 | 0.8694 |
+| 0.4419        | 1.8402 | 7000 | 0.4670          | 0.8358   | 0.7951    | 0.8810 | 0.8188   | 0.8886 | 0.8696 |
+| 0.4501        | 1.9716 | 7500 | 0.4731          | 0.8337   | 0.7810    | 0.8940 | 0.8133   | 0.8859 | 0.8601 |
+| 0.4351        | 2.1030 | 8000 | 0.4699          | 0.8326   | 0.7845    | 0.8870 | 0.8133   | 0.8722 | 0.8249 |
+| 0.4337        | 2.2345 | 8500 | 0.4711          | 0.8275   | 0.8123    | 0.8433 | 0.8159   | 0.8846 | 0.8509 |
+| 0.4368        | 2.3659 | 9000 | 0.5160          | 0.7900   | 0.8514    | 0.7368 | 0.7949   | 0.8936 | 0.8752 |
+| 0.4077        | 2.4974 | 9500 | 0.5006          | 0.8354   | 0.7947    | 0.8805 | 0.8183   | 0.8820 | 0.8475 |
+
+
+### 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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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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+    "single_word": false
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+  "pad_token": {
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+    "single_word": false
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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,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "lstrip": 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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