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multilingual_english_SEQUENCE_classification_model
Cabooose/multilingual_english_SEQUENCE_classification_model
multilingual_english_SEQUENCE_classification_model is a text classification model from Cabooose. 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
- Oct 20, 2023
- Updated
- Oct 20, 2023
- SHA
- a164f2f3
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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. -->
multilingual_english_SEQUENCE_classification_model
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3810
- Precision: 0.8173
- Recall: 0.8768
- F1: 0.8460
- Accuracy: 0.8404
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 462 | 0.3811 | 0.8293 | 0.8242 | 0.8267 | 0.8273 |
| 0.4941 | 2.0 | 924 | 0.3810 | 0.8173 | 0.8768 | 0.8460 | 0.8404 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 20, 2023
- Updated
- Oct 20, 2023