Instructions to use Extrabass/checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Extrabass/checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Extrabass/checkpoint")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Extrabass/checkpoint") model = AutoModelForSequenceClassification.from_pretrained("Extrabass/checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +7 -7
- model.safetensors +1 -1
- runs/Apr17_22-39-23_ml/events.out.tfevents.1713364763.ml.559919.0 +3 -0
- runs/Apr17_22-42-58_ml/events.out.tfevents.1713364979.ml.564118.0 +3 -0
- runs/Apr17_22-45-45_ml/events.out.tfevents.1713365145.ml.567414.0 +3 -0
- runs/Apr17_22-48-35_ml/events.out.tfevents.1713365316.ml.570664.0 +3 -0
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 1.0
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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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- learning_rate:
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- eval_batch_size:
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 1.0 |
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| No log | 2.0 |
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### Framework versions
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This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0022
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- Accuracy: 1.0
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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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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 1.0 | 123 | 0.0089 | 1.0 |
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| No log | 2.0 | 246 | 0.0028 | 1.0 |
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| No log | 3.0 | 369 | 0.0022 | 1.0 |
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### Framework versions
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model.safetensors
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