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xlm-roberta-large-finetuned-ours-DS
IIIT-L/xlm-roberta-large-finetuned-ours-DS
xlm-roberta-large-finetuned-ours-DS is a text classification model from IIIT-L. 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
- Sep 19, 2022
- Updated
- Sep 19, 2022
- SHA
- 00d42590
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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. -->
xlm-roberta-large-finetuned-ours-DS
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9568
- Accuracy: 0.71
- Precision: 0.6689
- Recall: 0.6607
- F1: 0.6637
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: 1.6820964947491663e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9953 | 1.99 | 199 | 0.7955 | 0.66 | 0.7533 | 0.5971 | 0.5352 |
| 0.6638 | 3.98 | 398 | 0.8043 | 0.735 | 0.7068 | 0.6782 | 0.6846 |
| 0.3457 | 5.97 | 597 | 0.9568 | 0.71 | 0.6689 | 0.6607 | 0.6637 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Sep 19, 2022
- Updated
- Sep 19, 2022