Saving best model of SciBERT_AsymmetricLoss_25K_bs64_P1_N1 to hub

README.md

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+---
+base_model: allenai/scibert_scivocab_uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: SciBERT_AsymmetricLoss_25K_bs64_P1_N1
+  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. -->
+
+# SciBERT_AsymmetricLoss_25K_bs64_P1_N1
+
+This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 67.0896
+- Accuracy: 0.9945
+- Precision: 0.7586
+- Recall: 0.6438
+- F1: 0.6965
+- Hamming: 0.0055
+
+## 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: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 25000
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 83.6475       | 0.16  | 5000  | 79.3653         | 0.9938   | 0.7361    | 0.5667 | 0.6404 | 0.0062  |
+| 75.8712       | 0.32  | 10000 | 72.7250         | 0.9942   | 0.7513    | 0.6068 | 0.6714 | 0.0058  |
+| 72.4202       | 0.47  | 15000 | 69.4174         | 0.9944   | 0.7568    | 0.6237 | 0.6838 | 0.0056  |
+| 70.0693       | 0.63  | 20000 | 67.8098         | 0.9945   | 0.7561    | 0.6385 | 0.6923 | 0.0055  |
+| 68.9765       | 0.79  | 25000 | 67.0896         | 0.9945   | 0.7586    | 0.6438 | 0.6965 | 0.0055  |
+
+
+### Framework versions
+
+- Transformers 4.35.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.7.1
+- Tokenizers 0.14.1

all_results.json

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+    "train_loss": 112.3134290625,
+    "train_runtime": 37604.7048,
+    "train_samples_per_second": 42.548,
+    "train_steps_per_second": 0.665
+}

config.json

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+  ],
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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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