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multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2
vg055/multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2
multilingual-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2 is a text classification model from vg055. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024
- SHA
- b10e6d2d
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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-e5-large-finetuned-IberAuTexTification2024-7030-task2-v2
This model is a fine-tuned version of intfloat/multilingual-e5-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5374
- F1: 0.8460
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.5894 | 1.0 | 2571 | 0.6887 | 0.7453 |
| 0.3772 | 2.0 | 5142 | 0.5798 | 0.8074 |
| 0.2181 | 3.0 | 7713 | 0.5374 | 0.8460 |
| 0.1032 | 4.0 | 10284 | 0.8335 | 0.8390 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jun 10, 2024
- Updated
- Jun 10, 2024