End of training

README.md

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+---
+library_name: transformers
+license: bsd-3-clause
+base_model: LongSafari/hyenadna-large-1m-seqlen-hf
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- accuracy
+model-index:
+- name: hyenadna-large-1m-seqlen-hf_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
+  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. -->
+
+# hyenadna-large-1m-seqlen-hf_ft_BioS2_1kbpHG19_DHSs_H3K27AC_one_shot
+
+This model is a fine-tuned version of [LongSafari/hyenadna-large-1m-seqlen-hf](https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5096
+- F1 Score: 0.7077
+- Precision: 0.7188
+- Recall: 0.6970
+- Accuracy: 0.6780
+- Auc: 0.7756
+- Prc: 0.8539
+
+## 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    |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:------:|:------:|
+| 0.3955        | 8.3333  | 500  | 0.7579          | 0.7273   | 0.7273    | 0.7273 | 0.6949   | 0.8124 | 0.8780 |
+| 0.0445        | 16.6667 | 1000 | 1.5096          | 0.7077   | 0.7188    | 0.6970 | 0.6780   | 0.7756 | 0.8539 |
+
+
+### Framework versions
+
+- Transformers 4.46.0.dev0
+- Pytorch 2.4.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.20.0

config.json

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+{
+  "_name_or_path": "LongSafari/hyenadna-large-1m-seqlen-hf",
+  "activation_freq": 10,
+  "architectures": [
+    "HyenaDNAForSequenceClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "LongSafari/hyenadna-large-1m-seqlen-hf--configuration_hyena.HyenaConfig",
+    "AutoModel": "LongSafari/hyenadna-large-1m-seqlen-hf--modeling_hyena.HyenaDNAModel",
+    "AutoModelForCausalLM": "LongSafari/hyenadna-large-1m-seqlen-hf--modeling_hyena.HyenaDNAForCausalLM",
+    "AutoModelForSequenceClassification": "LongSafari/hyenadna-large-1m-seqlen-hf--modeling_hyena.HyenaDNAForSequenceClassification"
+  },
+  "d_inner": 1024,
+  "d_model": 256,
+  "emb_dim": 5,
+  "embed_dropout": 0.1,
+  "filter_order": 64,
+  "hyena_dropout": 0.0,
+  "hyena_filter_dropout": 0.0,
+  "hyena_order": 2,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_seq_len": 1000002,
+  "model_type": "hyenadna",
+  "n_layer": 8,
+  "num_inner_mlps": 2,
+  "pad_token_id": 4,
+  "pad_vocab_size_multiple": 8,
+  "problem_type": "single_label_classification",
+  "short_filter_order": 3,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "train_freq": true,
+  "transformers_version": "4.46.0.dev0",
+  "use_bias": true,
+  "use_flash_attention": false,
+  "vocab_size": 12
+}

pytorch_model.bin

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+size 218288026

special_tokens_map.json

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

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "auto_map": {
+    "AutoTokenizer": [
+      "LongSafari/hyenadna-large-1m-seqlen-hf--tokenization_hyena.HyenaDNATokenizer",
+      null
+    ]
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "model_max_length": 1000002,
+  "pad_token": "[PAD]",
+  "padding_side": "left",
+  "sep_token": "[SEP]",
+  "tokenizer_class": "HyenaDNATokenizer",
+  "unk_token": "[UNK]"
+}