Instructions to use yossir/ber2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yossir/ber2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yossir/ber2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yossir/ber2") model = AutoModelForSequenceClassification.from_pretrained("yossir/ber2") - Notebooks
- Google Colab
- Kaggle
ber2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0472
- F1: 0.0
- Roc Auc: 0.5
- Accuracy: 0.0
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| No log | 1.0 | 121 | 0.2033 | 0.0039 | 0.5009 | 0.0 |
| No log | 2.0 | 242 | 0.0886 | 0.0 | 0.5 | 0.0 |
| No log | 3.0 | 363 | 0.0604 | 0.0 | 0.5 | 0.0 |
| No log | 4.0 | 484 | 0.0501 | 0.0 | 0.5 | 0.0 |
| 0.1703 | 5.0 | 605 | 0.0472 | 0.0 | 0.5 | 0.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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