Instructions to use LexFerrinson/FirulaiModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LexFerrinson/FirulaiModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="LexFerrinson/FirulaiModel")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("LexFerrinson/FirulaiModel") model = AutoModelForTokenClassification.from_pretrained("LexFerrinson/FirulaiModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 04de21e
Training in progress epoch 0
Browse files- README.md +9 -14
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.
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- Train Precision: 0.
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- Train Recall:
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- Train F1: 0.
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- Train Accuracy: 0.
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- Epoch:
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps':
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 0.8416 | 0.7234 | 0.0 | 0.0 | 0.0 | 0.9051 | 1 |
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| 0.7323 | 0.6383 | 0.0 | 0.0 | 0.0 | 0.9082 | 2 |
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| 0.6500 | 0.5786 | 0.0 | 0.0 | 0.0 | 0.9082 | 3 |
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| 0.5944 | 0.5410 | 0.0 | 0.0 | 0.0 | 0.9082 | 4 |
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| 0.5493 | 0.5230 | 0.0 | 0.0 | 0.0 | 0.9082 | 5 |
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### Framework versions
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0617
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- Validation Loss: 0.0648
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- Train Precision: 0.9062
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- Train Recall: 1.0
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- Train F1: 0.9508
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- Train Accuracy: 0.9905
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- Epoch: 0
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 600, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 0.0617 | 0.0648 | 0.9062 | 1.0 | 0.9508 | 0.9905 | 0 |
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### Framework versions
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tf_model.h5
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size 265587984
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