# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("titangmz/PNC_test_v2")
model = AutoModelForSequenceClassification.from_pretrained("titangmz/PNC_test_v2", device_map="auto")Quick Links
PNC_test_v2
This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an unknown 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 0.7661 | 0.4 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for titangmz/PNC_test_v2
Base model
google-bert/bert-base-multilingual-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="titangmz/PNC_test_v2")