Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Aayushi137/hindi-sentiment-muril with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aayushi137/hindi-sentiment-muril with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aayushi137/hindi-sentiment-muril")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aayushi137/hindi-sentiment-muril") model = AutoModelForSequenceClassification.from_pretrained("Aayushi137/hindi-sentiment-muril", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hindi-sentiment-muril
This model is a fine-tuned version of google/muril-base-cased on the indic_sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 0.5094
- Accuracy: 0.9103
- F1: 0.9103
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.6552 | 0.7885 | 0.7815 |
| No log | 2.0 | 126 | 0.5413 | 0.9167 | 0.9167 |
| No log | 3.0 | 189 | 0.5094 | 0.9103 | 0.9103 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.11.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
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Model tree for Aayushi137/hindi-sentiment-muril
Base model
google/muril-base-casedEvaluation results
- Accuracy on indic_sentimentvalidation set self-reported0.910
- F1 on indic_sentimentvalidation set self-reported0.910