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README.md

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----
-license: other
----
-
-## Model Documentation in progress
+---
+tags:
+- generated_from_trainer
+datasets:
+- epi_classify4_gard
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: results
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: epi_classify4_gard
+      type: epi_classify4_gard
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.875
+    - name: Recall
+      type: recall
+      value: 0.9032258064516129
+    - name: F1
+      type: f1
+      value: 0.8888888888888888
+    - name: Accuracy
+      type: accuracy
+      value: 0.986
+---
+
+<!-- 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. -->
+
+# results
+
+This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the epi_classify4_gard dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0541
+- Precision: 0.875
+- Recall: 0.9032
+- F1: 0.8889
+- Accuracy: 0.986
+
+## 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: 3e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 2
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 4.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.12.5
+- Pytorch 1.9.0+cu102
+- Datasets 1.12.1
+- Tokenizers 0.10.3

all_results.json

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+{
+    "epoch": 4.0,
+    "eval_accuracy": 0.986,
+    "eval_f1": 0.8888888888888888,
+    "eval_loss": 0.05408545956015587,
+    "eval_precision": 0.875,
+    "eval_recall": 0.9032258064516129,
+    "eval_runtime": 5.1836,
+    "eval_samples": 500,
+    "eval_samples_per_second": 96.457,
+    "eval_steps_per_second": 12.154,
+    "train_loss": 0.06538131501939562,
+    "train_runtime": 127.4981,
+    "train_samples": 1000,
+    "train_samples_per_second": 31.373,
+    "train_steps_per_second": 1.976
+}

eval_results.json

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+{
+    "epoch": 4.0,
+    "eval_accuracy": 0.986,
+    "eval_f1": 0.8888888888888888,
+    "eval_loss": 0.05408545956015587,
+    "eval_precision": 0.875,
+    "eval_recall": 0.9032258064516129,
+    "eval_runtime": 5.1836,
+    "eval_samples": 500,
+    "eval_samples_per_second": 96.457,
+    "eval_steps_per_second": 12.154
+}

eval_results.txt

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+eval_loss = 0.05408545956015587
+eval_precision = 0.875
+eval_recall = 0.9032258064516129
+eval_f1 = 0.8888888888888888
+eval_accuracy = 0.986
+eval_runtime = 5.1836
+eval_samples_per_second = 96.457
+eval_steps_per_second = 12.154
+epoch = 4.0
+eval_samples = 500

predict_results.txt

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+precision = 1.0
+recall = 0.6
+f1 = 0.7499999999999999
+accuracy = 0.8163265306122449
+
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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "dmis-lab/biobert-base-cased-v1.2", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}

train_results.json

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+{
+    "epoch": 4.0,
+    "train_loss": 0.06538131501939562,
+    "train_runtime": 127.4981,
+    "train_samples": 1000,
+    "train_samples_per_second": 31.373,
+    "train_steps_per_second": 1.976
+}

trainer_state.json

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+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 4.0,
+  "global_step": 252,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 4.0,
+      "step": 252,
+      "total_flos": 1014634340562720.0,
+      "train_loss": 0.06538131501939562,
+      "train_runtime": 127.4981,
+      "train_samples_per_second": 31.373,
+      "train_steps_per_second": 1.976
+    }
+  ],
+  "max_steps": 252,
+  "num_train_epochs": 4,
+  "total_flos": 1014634340562720.0,
+  "trial_name": null,
+  "trial_params": null
+}