# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DevxAman/results")
model = AutoModelForSequenceClassification.from_pretrained("DevxAman/results")Quick Links
results
This model is a fine-tuned version of google/muril-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3232
- Accuracy: 0.8957
- F1: 0.8913
- Precision: 0.8856
- Recall: 0.8970
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.4738 | 1.0 | 275 | 0.4001 | 0.8793 | 0.8768 | 0.8537 | 0.9013 |
| 0.3356 | 2.0 | 550 | 0.3046 | 0.9039 | 0.8967 | 0.9189 | 0.8755 |
| 0.2047 | 3.0 | 825 | 0.3029 | 0.9059 | 0.9013 | 0.9013 | 0.9013 |
| 0.2142 | 4.0 | 1100 | 0.3232 | 0.8957 | 0.8913 | 0.8856 | 0.8970 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for DevxAman/results
Base model
google/muril-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DevxAman/results")