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hing-roberta-finetuned-non-code-mixed-DS
IIIT-L/hing-roberta-finetuned-non-code-mixed-DS
hing-roberta-finetuned-non-code-mixed-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 cc-by-4.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- cc-by-4.0
- Model type
- xlm-roberta
- Access
- Public
- Created
- Sep 10, 2022
- Updated
- Sep 10, 2022
- SHA
- b86d01f1
Try a prompt
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. -->
hing-roberta-finetuned-non-code-mixed-DS
This model is a fine-tuned version of l3cube-pune/hing-roberta on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1286
- Accuracy: 0.6656
- Precision: 0.6575
- Recall: 0.6554
- F1: 0.6556
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: 2.824279936868144e-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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.8233 | 2.0 | 926 | 0.8104 | 0.6656 | 0.6607 | 0.6537 | 0.6555 |
| 0.3924 | 3.99 | 1852 | 1.1286 | 0.6656 | 0.6575 | 0.6554 | 0.6556 |
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
- cc-by-4.0
- Created
- Sep 10, 2022
- Updated
- Sep 10, 2022