Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Mahmoud-Ghareeb/relation_between_two_sentences with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud-Ghareeb/relation_between_two_sentences with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud-Ghareeb/relation_between_two_sentences")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud-Ghareeb/relation_between_two_sentences") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud-Ghareeb/relation_between_two_sentences", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): f1e8f0d
Training in progress epoch 9
Browse files- README.md +4 -3
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 1.1074
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- Validation Loss: 1.
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- Train Accuracy: 0.
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- Epoch:
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## Model description
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| 1.1060 | 1.0988 | 0.3402 | 6 |
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| 1.1073 | 1.1132 | 0.3411 | 7 |
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| 1.1074 | 1.0997 | 0.3411 | 8 |
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### Framework versions
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 1.1074
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- Validation Loss: 1.0986
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- Train Accuracy: 0.3419
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- Epoch: 9
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## Model description
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| 1.1060 | 1.0988 | 0.3402 | 6 |
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| 1.1073 | 1.1132 | 0.3411 | 7 |
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| 1.1074 | 1.0997 | 0.3411 | 8 |
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| 1.1074 | 1.0986 | 0.3419 | 9 |
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### Framework versions
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tf_model.h5
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