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--- |
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license: apache-2.0 |
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base_model: bert-base-multilingual-cased |
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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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model-index: |
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- name: bert-base-multilingual-cased-IDMGSP-danish |
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results: [] |
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datasets: |
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- ernlavr/IDMGSP-danish |
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language: |
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- da |
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library_name: transformers |
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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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# bert-base-multilingual-cased-IDMGSP-danish |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the on the [ernlavr/IDMGSP-danish](https://huggingface.co/datasets/ernlavr/IDMGSP-danish) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0123 |
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- Accuracy: {'accuracy': 0.8289043068464459} |
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- F1: {'f1': 0.842473183078221} |
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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: 0.0001 |
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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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- lr_scheduler_warmup_steps: 500 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:| |
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| 0.4692 | 1.0 | 480 | 0.3779 | {'accuracy': 0.8519439717240477} | {'f1': 0.84845236500067} | |
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| 0.3267 | 2.0 | 960 | 0.5350 | {'accuracy': 0.7896321508050792} | {'f1': 0.8138538167496815} | |
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| 0.5149 | 3.0 | 1440 | 0.7051 | {'accuracy': 0.7510145306977353} | {'f1': 0.7911267296288161} | |
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| 0.2823 | 4.0 | 1920 | 0.6520 | {'accuracy': 0.7317711742374656} | {'f1': 0.7837010450754776} | |
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| 0.2107 | 5.0 | 2400 | 0.3335 | {'accuracy': 0.8785181306453724} | {'f1': 0.8759689922480619} | |
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| 0.1868 | 6.0 | 2880 | 0.8269 | {'accuracy': 0.8175153815944496} | {'f1': 0.8349123638086214} | |
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| 0.0969 | 7.0 | 3360 | 0.4585 | {'accuracy': 0.877470873150936} | {'f1': 0.872200983069361} | |
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| 0.1116 | 8.0 | 3840 | 1.0309 | {'accuracy': 0.7993192826286163} | {'f1': 0.8236106316879531} | |
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| 0.0386 | 9.0 | 4320 | 0.9517 | {'accuracy': 0.8294279355936641} | {'f1': 0.8426898466739103} | |
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| 0.0204 | 10.0 | 4800 | 1.0123 | {'accuracy': 0.8289043068464459} | {'f1': 0.842473183078221} | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
