Instructions to use nadika/nepali_complaints_classification_muril with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/nepali_complaints_classification_muril with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/nepali_complaints_classification_muril")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/nepali_complaints_classification_muril") model = AutoModelForSequenceClassification.from_pretrained("nadika/nepali_complaints_classification_muril") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| base_model: google/muril-base-cased | |
| widget: | |
| - text: 'मेयरले उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन, दबाब झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग। बाह्रबिसेका मेयरको मनोमानी- दोहोरीमा रमाइलो गरेको बिलसमेत नगरपालिकाबाटै भुक्तानी गर्न दबाब मेयरले उपभोक्ता समितिसँग ३० प्रतिशत कमिसन लिएपछि विकासको काम गुणस्तरहीन, दबाब झेल्न नसकेर प्रशासकीय अधिकृतको भागाभाग। ' | |
| model-index: | |
| - name: nepali_complaints_classification_muril | |
| results: [] | |
| <!-- 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. --> | |
| # nepali_complaints_classification_muril | |
| This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2424 | |
| ## 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: 2e-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: 1000 | |
| - num_epochs: 4 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.916 | 1.0 | 2244 | 0.7103 | | |
| | 0.2725 | 2.0 | 4488 | 0.3206 | | |
| | 0.1678 | 3.0 | 6732 | 0.2685 | | |
| | 0.204 | 4.0 | 8976 | 0.2424 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |