Text Classification
Transformers
PyTorch
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use SMG0/Model4_arabertv2_base_T1_WS_A100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SMG0/Model4_arabertv2_base_T1_WS_A100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SMG0/Model4_arabertv2_base_T1_WS_A100")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SMG0/Model4_arabertv2_base_T1_WS_A100") model = AutoModelForSequenceClassification.from_pretrained("SMG0/Model4_arabertv2_base_T1_WS_A100") - Notebooks
- Google Colab
- Kaggle
Model4_arabertv2_base_T1_WS_A100
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02-twitter on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1569
- F1: 0.8398
- F1 Macro: 0.7742
- Roc Auc: 0.9021
- Accuracy: 0.8073
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Macro | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0988 | 1.0 | 507 | 0.1569 | 0.8398 | 0.7742 | 0.9021 | 0.8073 |
| 0.056 | 2.0 | 1014 | 0.2023 | 0.8267 | 0.7610 | 0.8938 | 0.7912 |
| 0.0358 | 3.0 | 1521 | 0.1959 | 0.8529 | 0.7889 | 0.9089 | 0.8275 |
| 0.0204 | 4.0 | 2028 | 0.2132 | 0.8496 | 0.7971 | 0.9079 | 0.8226 |
| 0.0157 | 5.0 | 2535 | 0.2288 | 0.8434 | 0.7789 | 0.9047 | 0.8163 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for SMG0/Model4_arabertv2_base_T1_WS_A100
Base model
aubmindlab/bert-base-arabertv02-twitter