Saving best model of SciBERT_TwoWayLoss_25K_bs64_P10_N5 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_TwoWayLoss_25K_bs64_P10_N5
+  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_TwoWayLoss_25K_bs64_P10_N5
+
+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: 15.1250
+- Accuracy: 0.7066
+- Precision: 0.0321
+- Recall: 0.9982
+- F1: 0.0622
+- Hamming: 0.2934
+
+## 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 |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 28.5732       | 0.16  | 5000  | 26.4288         | 0.6945   | 0.0307    | 0.9910 | 0.0595 | 0.3055  |
+| 19.8755       | 0.32  | 10000 | 18.9620         | 0.7010   | 0.0315    | 0.9959 | 0.0610 | 0.2990  |
+| 17.1294       | 0.47  | 15000 | 16.5587         | 0.7021   | 0.0316    | 0.9970 | 0.0613 | 0.2979  |
+| 15.8209       | 0.63  | 20000 | 15.4919         | 0.7053   | 0.0320    | 0.9982 | 0.0620 | 0.2947  |
+| 15.4304       | 0.79  | 25000 | 15.1250         | 0.7066   | 0.0321    | 0.9982 | 0.0622 | 0.2934  |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.2.0.dev20231002
+- Datasets 2.7.1
+- Tokenizers 0.13.3

all_results.json

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+    "train_loss": 22.11730390625,
+    "train_runtime": 25819.8145,
+    "train_samples_per_second": 61.968,
+    "train_steps_per_second": 0.968
+}

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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