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--- |
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license: mit |
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base_model: avsolatorio/GIST-large-Embedding-v0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: my-clf |
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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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# my-clf |
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This model is a fine-tuned version of [avsolatorio/GIST-large-Embedding-v0](https://huggingface.co/avsolatorio/GIST-large-Embedding-v0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2391 |
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- F1: 0.5650 |
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- Roc Auc: 0.7487 |
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- Accuracy: 0.1228 |
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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: 5e-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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| No log | 1.0 | 50 | 0.3095 | 0.1123 | 0.5442 | 0.0351 | |
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| No log | 2.0 | 100 | 0.2862 | 0.2744 | 0.6015 | 0.0702 | |
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| No log | 3.0 | 150 | 0.2642 | 0.3740 | 0.6488 | 0.0877 | |
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| No log | 4.0 | 200 | 0.2563 | 0.4429 | 0.6792 | 0.0526 | |
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| No log | 5.0 | 250 | 0.2492 | 0.5030 | 0.7178 | 0.0877 | |
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| No log | 6.0 | 300 | 0.2323 | 0.5296 | 0.7199 | 0.1228 | |
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| No log | 7.0 | 350 | 0.2372 | 0.5433 | 0.7326 | 0.1053 | |
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| No log | 8.0 | 400 | 0.2326 | 0.5371 | 0.7279 | 0.1053 | |
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| No log | 9.0 | 450 | 0.2346 | 0.5587 | 0.7382 | 0.1404 | |
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| 0.1673 | 10.0 | 500 | 0.2393 | 0.5819 | 0.7534 | 0.1053 | |
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| 0.1673 | 11.0 | 550 | 0.2370 | 0.5656 | 0.7471 | 0.1053 | |
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| 0.1673 | 12.0 | 600 | 0.2374 | 0.5680 | 0.7479 | 0.1404 | |
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| 0.1673 | 13.0 | 650 | 0.2392 | 0.5680 | 0.7500 | 0.1228 | |
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| 0.1673 | 14.0 | 700 | 0.2398 | 0.5650 | 0.7487 | 0.1228 | |
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| 0.1673 | 15.0 | 750 | 0.2391 | 0.5650 | 0.7487 | 0.1228 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.2 |
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