Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use cruiser/bert_model_kaggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cruiser/bert_model_kaggle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cruiser/bert_model_kaggle")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cruiser/bert_model_kaggle") model = AutoModelForSequenceClassification.from_pretrained("cruiser/bert_model_kaggle") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 4
Browse files
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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: 1.0986
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- Train Accuracy: 0.
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- Validation Loss: 1.0986
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- Validation Accuracy: 0.3814
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- Epoch:
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## Model description
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| 1.0990 | 0.3370 | 1.0986 | 0.3823 | 1 |
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| 1.0996 | 0.3631 | 1.0986 | 0.3814 | 2 |
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| 1.0986 | 0.3556 | 1.0986 | 0.3814 | 3 |
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-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: 1.0986
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- Train Accuracy: 0.3554
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- Validation Loss: 1.0986
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- Validation Accuracy: 0.3814
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- Epoch: 4
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## Model description
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| 1.0990 | 0.3370 | 1.0986 | 0.3823 | 1 |
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| 1.0996 | 0.3631 | 1.0986 | 0.3814 | 2 |
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| 1.0986 | 0.3556 | 1.0986 | 0.3814 | 3 |
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| 1.0986 | 0.3554 | 1.0986 | 0.3814 | 4 |
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### Framework versions
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