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xlm-roberta-large-finetuned-TRAC-DS
IIIT-L/xlm-roberta-large-finetuned-TRAC-DS
xlm-roberta-large-finetuned-TRAC-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 14, 2022
- Updated
- Sep 14, 2022
- SHA
- 548cc6de
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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-TRAC-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: 1.0992
- Accuracy: 0.3342
- Precision: 0.1114
- Recall: 0.3333
- F1: 0.1670
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: 4.1187640010910775e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.1358 | 0.25 | 612 | 1.1003 | 0.4436 | 0.1479 | 0.3333 | 0.2049 |
| 1.1199 | 0.5 | 1224 | 1.1130 | 0.4436 | 0.1479 | 0.3333 | 0.2049 |
| 1.1221 | 0.75 | 1836 | 1.0992 | 0.3342 | 0.1114 | 0.3333 | 0.1670 |
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 14, 2022
- Updated
- Sep 14, 2022