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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: uniBERT.distilBERT.3 |
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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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# uniBERT.distilBERT.3 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7293 |
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- Accuracy: (0.4772117962466488,) |
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- F1: (0.4693132851587397,) |
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- Precision: (0.5442146665454255,) |
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- Recall: 0.4772 |
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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: 16 |
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- eval_batch_size: 64 |
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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 | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:----------------------:|:----------------------:|:------:| |
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| 2.9412 | 1.0 | 210 | 2.7539 | (0.19302949061662197,) | (0.1603618689364134,) | (0.1948027475862202,) | 0.1930 | |
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| 2.1163 | 2.0 | 420 | 2.1123 | (0.30831099195710454,) | (0.2886576454762518,) | (0.34306886958718397,) | 0.3083 | |
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| 1.5605 | 3.0 | 630 | 1.9332 | (0.35924932975871315,) | (0.3408370919837017,) | (0.4437777540758143,) | 0.3592 | |
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| 1.2042 | 4.0 | 840 | 1.7857 | (0.4262734584450402,) | (0.4114641689723971,) | (0.5423496541710991,) | 0.4263 | |
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| 0.9317 | 5.0 | 1050 | 1.7584 | (0.4262734584450402,) | (0.41517951297641825,) | (0.5270241126881486,) | 0.4263 | |
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| 0.7497 | 6.0 | 1260 | 1.7334 | (0.46380697050938335,) | (0.4616472194616382,) | (0.5561767348377945,) | 0.4638 | |
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| 0.6484 | 7.0 | 1470 | 1.7148 | (0.48257372654155495,) | (0.47743545933365816,) | (0.5507046992541056,) | 0.4826 | |
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| 0.5396 | 8.0 | 1680 | 1.7341 | (0.47989276139410186,) | (0.4727261312495505,) | (0.5529949943706518,) | 0.4799 | |
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| 0.4599 | 9.0 | 1890 | 1.7252 | (0.4772117962466488,) | (0.47031578431963555,) | (0.5474190199916943,) | 0.4772 | |
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| 0.427 | 10.0 | 2100 | 1.7293 | (0.4772117962466488,) | (0.4693132851587397,) | (0.5442146665454255,) | 0.4772 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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