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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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- 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: finer-139-xtremedistil-l12-h384 |
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results: [] |
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datasets: |
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- nlpaueb/finer-139 |
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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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# finer-139-xtremedistil-l12-h384 |
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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 [finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0133 |
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- Precision: 0.6104 |
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- Recall: 0.6581 |
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- F1: 0.6334 |
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- Accuracy: 0.9961 |
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## Model description |
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Base model: microsoft/xtremedistil-l12-h384-uncased |
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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: 256 |
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- eval_batch_size: 512 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 512 |
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- total_eval_batch_size: 1024 |
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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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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 5.0 |
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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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| 0.0438 | 1.0 | 1759 | 0.0389 | 0.4777 | 0.1593 | 0.2389 | 0.9937 | |
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| 0.0266 | 2.0 | 3518 | 0.0234 | 0.5432 | 0.4129 | 0.4692 | 0.9949 | |
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| 0.0186 | 3.0 | 5277 | 0.0165 | 0.5980 | 0.5516 | 0.5739 | 0.9957 | |
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| 0.0154 | 4.0 | 7036 | 0.0143 | 0.5932 | 0.6447 | 0.6179 | 0.9959 | |
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| 0.0137 | 5.0 | 8795 | 0.0133 | 0.6104 | 0.6581 | 0.6334 | 0.9961 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0a0+b5021ba |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
