Instructions to use sarraj19/metaphor_classifier_tamil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarraj19/metaphor_classifier_tamil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sarraj19/metaphor_classifier_tamil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sarraj19/metaphor_classifier_tamil") model = AutoModelForSequenceClassification.from_pretrained("sarraj19/metaphor_classifier_tamil") - Notebooks
- Google Colab
- Kaggle
metaphor_classifier_tamil
This model is a fine-tuned version of google/muril-base-cased 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: 5e-05
- train_batch_size: 4
- 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: 6
Training results
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Downloads last month
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Model tree for sarraj19/metaphor_classifier_tamil
Base model
google/muril-base-cased