Instructions to use OliverHeine/google_mobilebert-uncased_fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/google_mobilebert-uncased_fold_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/google_mobilebert-uncased_fold_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/google_mobilebert-uncased_fold_1") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/google_mobilebert-uncased_fold_1") - Notebooks
- Google Colab
- Kaggle
google_mobilebert-uncased fold 1
Browse files
README.md
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Accuracy: 0.
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- F1: 0.
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- Precision: 0.
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- Recall: 0.
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## Model description
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1
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### Framework versions
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1152
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- Accuracy: 0.9599
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- F1: 0.9558
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- Precision: 0.9651
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- Recall: 0.9468
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## Model description
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 1.3193 | 1.0 | 15481 | 0.1280 | 0.9516 | 0.9469 | 0.9543 | 0.9395 |
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| 0.0943 | 2.0 | 30962 | 0.1130 | 0.9580 | 0.9537 | 0.9634 | 0.9443 |
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| 0.0854 | 3.0 | 46443 | 0.1152 | 0.9599 | 0.9558 | 0.9651 | 0.9468 |
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### Framework versions
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