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bert-base-multilingual-cased-IDMGSP-danish
ernlavr/bert-base-multilingual-cased-IDMGSP-danish
bert-base-multilingual-cased-IDMGSP-danish is a text classification model from ernlavr. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Nov 18, 2023
- Updated
- Nov 21, 2023
- SHA
- 1e0334ea
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-base-multilingual-cased-IDMGSP-danish
This model is a fine-tuned version of bert-base-multilingual-cased on the on the ernlavr/IDMGSP-danish dataset. It achieves the following results on the evaluation set:
- Loss: 1.0123
- Accuracy: {'accuracy': 0.8289043068464459}
- F1: {'f1': 0.842473183078221}
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.4692 | 1.0 | 480 | 0.3779 | {'accuracy': 0.8519439717240477} | {'f1': 0.84845236500067} |
| 0.3267 | 2.0 | 960 | 0.5350 | {'accuracy': 0.7896321508050792} | {'f1': 0.8138538167496815} |
| 0.5149 | 3.0 | 1440 | 0.7051 | {'accuracy': 0.7510145306977353} | {'f1': 0.7911267296288161} |
| 0.2823 | 4.0 | 1920 | 0.6520 | {'accuracy': 0.7317711742374656} | {'f1': 0.7837010450754776} |
| 0.2107 | 5.0 | 2400 | 0.3335 | {'accuracy': 0.8785181306453724} | {'f1': 0.8759689922480619} |
| 0.1868 | 6.0 | 2880 | 0.8269 | {'accuracy': 0.8175153815944496} | {'f1': 0.8349123638086214} |
| 0.0969 | 7.0 | 3360 | 0.4585 | {'accuracy': 0.877470873150936} | {'f1': 0.872200983069361} |
| 0.1116 | 8.0 | 3840 | 1.0309 | {'accuracy': 0.7993192826286163} | {'f1': 0.8236106316879531} |
| 0.0386 | 9.0 | 4320 | 0.9517 | {'accuracy': 0.8294279355936641} | {'f1': 0.8426898466739103} |
| 0.0204 | 10.0 | 4800 | 1.0123 | {'accuracy': 0.8289043068464459} | {'f1': 0.842473183078221} |
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Languages
- da
- Created
- Nov 18, 2023
- Updated
- Nov 21, 2023