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xlm-roberta-base-finetuned-autext24
andricValdez/xlm-roberta-base-finetuned-autext24
xlm-roberta-base-finetuned-autext24 is a text classification model from andricValdez. 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 7, 2024
- Updated
- Jun 7, 2024
- SHA
- 93895e40
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Base models
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. -->
xlm-roberta-base-finetuned-autext24
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3836
- Accuracy: 0.9517
- F1: 0.9515
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 4785 | 0.2788 | 0.9180 | 0.9172 |
| 0.1496 | 2.0 | 9570 | 0.4590 | 0.9123 | 0.9113 |
| 0.1496 | 3.0 | 14355 | 0.3858 | 0.9373 | 0.9369 |
| 0.0482 | 4.0 | 19140 | 0.3224 | 0.9546 | 0.9545 |
| 0.0482 | 5.0 | 23925 | 0.3836 | 0.9517 | 0.9515 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jun 7, 2024
- Updated
- Jun 7, 2024