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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- silicone |
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metrics: |
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- accuracy |
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model-index: |
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- name: spanbert-base-cased |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: silicone |
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type: silicone |
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config: swda |
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split: test |
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args: swda |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7114959469417833 |
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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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# spanbert-base-cased |
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This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the silicone dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0346 |
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- Accuracy: 0.7115 |
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- Micro-precision: 0.7115 |
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- Micro-recall: 0.7115 |
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- Micro-f1: 0.7115 |
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- Macro-precision: 0.2484 |
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- Macro-recall: 0.2508 |
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- Macro-f1: 0.2412 |
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- Weighted-precision: 0.6569 |
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- Weighted-recall: 0.7115 |
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- Weighted-f1: 0.6741 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:| |
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| 1.043 | 1.0 | 2980 | 1.0346 | 0.7115 | 0.7115 | 0.7115 | 0.7115 | 0.2484 | 0.2508 | 0.2412 | 0.6569 | 0.7115 | 0.6741 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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