Instructions to use Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer") model = AutoModelForSequenceClassification.from_pretrained("Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
xmlRoBert-Balanced-trimmed-10epoch-Trainer
This model is a fine-tuned version of ai4bharat/indic-bert on the None dataset.
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.14.1
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Model tree for Harsha9044/xmlRoBert-Balanced-trimmed-10epoch-Trainer
Base model
ai4bharat/indic-bert