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--- |
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license: mit |
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base_model: microsoft/xtremedistil-l12-h384-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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model-index: |
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- name: xtremedistil-l12-h384-uncased-zeroshot-v1.1-none |
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results: [] |
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pipeline_tag: zero-shot-classification |
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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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# xtremedistil-l12-h384-uncased-zeroshot-v1.1-none |
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A slightly larger sibling to https://hf.co/MoritzLaurer/xtremedistil-l6-h256-zeroshot-v1.1-all-33 |
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## Model description |
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This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2063 |
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- F1 Macro: 0.5570 |
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- F1 Micro: 0.6385 |
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- Accuracy Balanced: 0.6104 |
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- Accuracy: 0.6385 |
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- Precision Macro: 0.5705 |
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- Recall Macro: 0.6104 |
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- Precision Micro: 0.6385 |
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- Recall Micro: 0.6385 |
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## Training and evaluation data |
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See https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 80085 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.04 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.2756 | 0.32 | 5000 | 0.4155 | 0.8146 | 0.8255 | 0.8215 | 0.8255 | 0.8101 | 0.8215 | 0.8255 | 0.8255 | |
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| 0.2395 | 0.65 | 10000 | 0.4166 | 0.8182 | 0.8303 | 0.8222 | 0.8303 | 0.8151 | 0.8222 | 0.8303 | 0.8303 | |
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| 0.2464 | 0.97 | 15000 | 0.4114 | 0.8204 | 0.8325 | 0.8239 | 0.8325 | 0.8175 | 0.8239 | 0.8325 | 0.8325 | |
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| 0.2105 | 1.3 | 20000 | 0.4051 | 0.8236 | 0.8363 | 0.8254 | 0.8363 | 0.8219 | 0.8254 | 0.8363 | 0.8363 | |
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| 0.2267 | 1.62 | 25000 | 0.4030 | 0.8244 | 0.8373 | 0.8257 | 0.8373 | 0.8231 | 0.8257 | 0.8373 | 0.8373 | |
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| 0.2312 | 1.95 | 30000 | 0.4088 | 0.8233 | 0.836 | 0.8250 | 0.836 | 0.8217 | 0.8250 | 0.836 | 0.836 | |
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| 0.2241 | 2.27 | 35000 | 0.4061 | 0.8257 | 0.8375 | 0.8291 | 0.8375 | 0.8229 | 0.8291 | 0.8375 | 0.8375 | |
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| 0.2183 | 2.6 | 40000 | 0.4043 | 0.8259 | 0.838 | 0.8285 | 0.838 | 0.8235 | 0.8285 | 0.838 | 0.838 | |
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| 0.2285 | 2.92 | 45000 | 0.4041 | 0.8241 | 0.8365 | 0.8263 | 0.8365 | 0.8220 | 0.8263 | 0.8365 | 0.8365 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
