Instructions to use metythorn/khmer-xlm-roberta-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metythorn/khmer-xlm-roberta-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="metythorn/khmer-xlm-roberta-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("metythorn/khmer-xlm-roberta-small") model = AutoModelForMaskedLM.from_pretrained("metythorn/khmer-xlm-roberta-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add comprehensive model card
Browse files
README.md
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---
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language:
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- km
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- pytorch
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- transformers
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widget:
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- text: ខ្ញុំចង់<mask>ភាសាខ្មែរ
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- text: ប្រទេសកម្ពុជាមាន<mask>ខេត្ត
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- text: រាជធានីភ្នំពេញគឺជ<mask>របស់ប្រទេសកម្ពុជា
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metrics:
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- perplexity
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base_model: xlm-roberta-base
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- **Training Data**: Khmer & English dataset with 31M examples with total 6Billion characters
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- **Parameters**: 163M trainable parameters
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- **Training Steps**: 1,122,978
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- **Final Checkpoint**: Step
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## Training Details
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- **Total Optimization Steps**: 14,509,200
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- **Learning Rate**: ~2e-5 (with scheduler)
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- **Hardware**: Training on single server with 4GPUs
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- **Training time**: I trained this model for
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## Training Metrics
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- **Final Training Loss**:
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- **Final Learning Rate**:
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- **Final Gradient Norm**:
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- **Training Epoch**:
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## Usage
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---
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language:
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- km
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- pytorch
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- transformers
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widget:
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- text: "ខ្ញុំចង់<mask>ភាសាខ្មែរ"
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- text: "ប្រទេសកម្ពុជាមាន<mask>ខេត្ត"
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- text: "រាជធានីភ្នំពេញគឺជ<mask>របស់ប្រទេសកម្ពុជា"
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metrics:
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- perplexity
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base_model: xlm-roberta-base
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- **Training Data**: Khmer & English dataset with 31M examples with total 6Billion characters
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- **Parameters**: 163M trainable parameters
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- **Training Steps**: 1,122,978
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- **Final Checkpoint**: Step 2237500
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## Training Details
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- **Total Optimization Steps**: 14,509,200
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- **Learning Rate**: ~2e-5 (with scheduler)
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- **Hardware**: Training on single server with 4GPUs
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- **Training time**: I trained this model for 11 Days
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## Training Metrics
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- **Final Training Loss**: 0.5613
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- **Final Learning Rate**: 1.72e-05
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- **Final Gradient Norm**: 2.1777
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- **Training Epoch**: 14.16
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## Usage
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