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--- |
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base_model: allenai/scibert_scivocab_cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: scibert_all_deep |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# scibert_all_deep |
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This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8270 |
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- Precision: 0.6648 |
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- Recall: 0.7172 |
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- F1: 0.6900 |
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- Accuracy: 0.8207 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 363 | 0.5559 | 0.6191 | 0.6867 | 0.6511 | 0.8131 | |
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| 0.6741 | 2.0 | 726 | 0.5344 | 0.6271 | 0.7101 | 0.6660 | 0.8203 | |
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| 0.3917 | 3.0 | 1089 | 0.5548 | 0.6558 | 0.7064 | 0.6801 | 0.8205 | |
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| 0.3917 | 4.0 | 1452 | 0.5835 | 0.6717 | 0.7110 | 0.6908 | 0.8246 | |
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| 0.271 | 5.0 | 1815 | 0.6643 | 0.6524 | 0.7255 | 0.6870 | 0.8196 | |
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| 0.188 | 6.0 | 2178 | 0.7021 | 0.6724 | 0.7067 | 0.6892 | 0.8222 | |
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| 0.1437 | 7.0 | 2541 | 0.7594 | 0.6555 | 0.7180 | 0.6853 | 0.8191 | |
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| 0.1437 | 8.0 | 2904 | 0.7916 | 0.6664 | 0.7109 | 0.6879 | 0.8194 | |
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| 0.114 | 9.0 | 3267 | 0.8123 | 0.6582 | 0.7225 | 0.6888 | 0.8203 | |
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| 0.0943 | 10.0 | 3630 | 0.8270 | 0.6648 | 0.7172 | 0.6900 | 0.8207 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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