Instructions to use nadika/nepali_complaints_classification_muril3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadika/nepali_complaints_classification_muril3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadika/nepali_complaints_classification_muril3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadika/nepali_complaints_classification_muril3") model = AutoModelForSequenceClassification.from_pretrained("nadika/nepali_complaints_classification_muril3") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +76 -0
- model.safetensors +1 -1
README.md
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---
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license: apache-2.0
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base_model: google/muril-base-cased
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tags:
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- generated_from_trainer
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model-index:
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- name: nepali_complaints_classification_muril3
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# nepali_complaints_classification_muril3
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This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2575
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 50
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 2.3973 | 0.25 | 500 | 2.0247 |
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| 1.7073 | 0.5 | 1000 | 1.3814 |
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| 1.1586 | 0.75 | 1500 | 0.9054 |
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| 0.8099 | 1.0 | 2000 | 0.6431 |
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| 0.5456 | 1.25 | 2500 | 0.4845 |
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| 0.434 | 1.5 | 3000 | 0.4157 |
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| 0.3643 | 1.75 | 3500 | 0.3814 |
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| 0.3144 | 2.01 | 4000 | 0.3432 |
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| 0.2616 | 2.26 | 4500 | 0.3156 |
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| 0.2418 | 2.51 | 5000 | 0.2952 |
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| 0.2256 | 2.76 | 5500 | 0.2805 |
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| 0.2157 | 3.01 | 6000 | 0.2908 |
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| 0.1749 | 3.26 | 6500 | 0.2847 |
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| 0.1626 | 3.51 | 7000 | 0.2734 |
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| 0.1522 | 3.76 | 7500 | 0.2658 |
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| 0.1443 | 4.01 | 8000 | 0.2560 |
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| 0.1196 | 4.26 | 8500 | 0.2580 |
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| 0.1138 | 4.51 | 9000 | 0.2618 |
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| 0.1119 | 4.76 | 9500 | 0.2575 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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-
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size 950291512
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version https://git-lfs.github.com/spec/v1
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size 950291512
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