Downloads · 30 days
3
16% of all-time downloads
prepregrammar
PrabalAryal/prepregrammar
prepregrammar is a machine learning model from PrabalAryal. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
probably proofread and complete it, then remove this comment. --
All-time downloads
19
Public
Repo size
2.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.h52.2 GB · 100%
At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- mt5
- Access
- Public
- Created
- Mar 11, 2025
- Updated
- Mar 11, 2025
- SHA
- 5d79ed06
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
prepregrammar
This model is a fine-tuned version of PrabalAryal/NepaliGECmodel1 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1570
- Train Accuracy: 0.4940
- Validation Loss: 0.1311
- Validation Accuracy: 0.4962
- Epoch: 2
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:
- optimizer: {'name': 'Adam', 'weight_decay': 1e-05, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.2240 | 0.4895 | 0.1638 | 0.4939 | 0 |
| 0.1747 | 0.4924 | 0.1425 | 0.4953 | 1 |
| 0.1570 | 0.4940 | 0.1311 | 0.4962 | 2 |
Framework versions
- Transformers 4.47.0
- TensorFlow 2.17.1
- Datasets 3.3.1
- Tokenizers 0.21.0
- Library
- transformers
- Type
- mt5
- License
- apache-2.0
- Created
- Mar 11, 2025
- Updated
- Mar 11, 2025