End of training

README.md

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+---
+base_model: AIRI-Institute/gena-lm-bert-base-t2t
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- accuracy
+model-index:
+- name: gut_1024-finetuned-lora-bert-base-t2t
+  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. -->
+
+# gut_1024-finetuned-lora-bert-base-t2t
+
+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: 0.4700
+- F1: 0.8448
+- Mcc Score: 0.5728
+- Accuracy: 0.7943
+
+## 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: 0.0005
+- train_batch_size: 8
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Mcc Score | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
+| 0.6922        | 0.02  | 100  | 0.6644          | 0.7478 | 0.0       | 0.5971   |
+| 0.6582        | 0.04  | 200  | 0.6766          | 0.4699 | 0.2578    | 0.5570   |
+| 0.6578        | 0.05  | 300  | 0.5801          | 0.8210 | 0.4886    | 0.7508   |
+| 0.5793        | 0.07  | 400  | 0.5814          | 0.8013 | 0.4141    | 0.7082   |
+| 0.5933        | 0.09  | 500  | 0.5877          | 0.7408 | 0.4494    | 0.7183   |
+| 0.5616        | 0.11  | 600  | 0.5000          | 0.8229 | 0.5282    | 0.7766   |
+| 0.5168        | 0.12  | 700  | 0.5027          | 0.8347 | 0.5540    | 0.7884   |
+| 0.4788        | 0.14  | 800  | 0.5284          | 0.7922 | 0.5012    | 0.7572   |
+| 0.5255        | 0.16  | 900  | 0.4859          | 0.8445 | 0.5696    | 0.7901   |
+| 0.5404        | 0.18  | 1000 | 0.4700          | 0.8448 | 0.5728    | 0.7943   |
+
+
+### Framework versions
+
+- Transformers 4.38.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.1
+- Tokenizers 0.15.2

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": "4.38.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

model.safetensors

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runs/Mar01_13-40-09_87d63e90cc1c/events.out.tfevents.1709300410.87d63e90cc1c.577.0

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special_tokens_map.json

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+{
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+    "lstrip": false,
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+    "single_word": false
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+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+  "pad_token": "[PAD]",
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+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]"
+}

training_args.bin

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