Instructions to use Rocketknight1/model-card-callback-test-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rocketknight1/model-card-callback-test-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rocketknight1/model-card-callback-test-new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/model-card-callback-test-new") model = AutoModelForSequenceClassification.from_pretrained("Rocketknight1/model-card-callback-test-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): e918fc5
End of training
Browse files- README.md +5 -4
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) 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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- Train Accuracy:
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- Validation Loss: 0.
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- Validation Accuracy: 1.0
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- Epoch:
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## Model description
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
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|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
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| 0.4856 | 0.5156 | 0.0154 | 1.0 | 0 |
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### Framework versions
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0081
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- Train Accuracy: 1.0
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- Validation Loss: 0.0001
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- Validation Accuracy: 1.0
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- Epoch: 1
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## Model description
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
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|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
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| 0.4856 | 0.5156 | 0.0154 | 1.0 | 0 |
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| 0.0081 | 1.0 | 0.0001 | 1.0 | 1 |
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### Framework versions
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tf_model.h5
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