# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("poooj/BertClassificationTestMalayalam")
model = AutoModelForSequenceClassification.from_pretrained("poooj/BertClassificationTestMalayalam")Quick Links
BertClassificationTestMalayalam
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7209
- Accuracy: 0.5932
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 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 37 | 0.6134 | 0.7288 |
| No log | 2.0 | 74 | 0.6139 | 0.6102 |
| No log | 3.0 | 111 | 0.6213 | 0.6949 |
| No log | 4.0 | 148 | 0.7209 | 0.5932 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for poooj/BertClassificationTestMalayalam
Base model
google-bert/bert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="poooj/BertClassificationTestMalayalam")