Instructions to use nadika/complaints_classification_nepali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/complaints_classification_nepali with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/complaints_classification_nepali")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/complaints_classification_nepali") model = AutoModelForSequenceClassification.from_pretrained("nadika/complaints_classification_nepali", device_map="auto") - Notebooks
- Google Colab
- Kaggle
complaints_classification_nepali
This model is a fine-tuned version of google/muril-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2169
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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4 | 1.0 | 2244 | 0.4112 |
| 0.2184 | 2.0 | 4488 | 0.2461 |
| 0.1209 | 3.0 | 6732 | 0.2169 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 3
Model tree for nadika/complaints_classification_nepali
Base model
google/muril-base-cased