Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use Drigoro/roberta_original with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/roberta_original with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Drigoro/roberta_original")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Drigoro/roberta_original") model = AutoModelForSequenceClassification.from_pretrained("Drigoro/roberta_original", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 5
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 [roberta-base](https://huggingface.co/roberta-base) 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: 1.
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- Train Accuracy: 0.
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- Epoch:
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## Model description
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| 1.0124 | 1.2975 | 0.5564 | 2 |
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| 0.8764 | 1.3194 | 0.5600 | 3 |
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| 0.7594 | 1.3889 | 0.5493 | 4 |
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### Framework versions
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.6675
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- Validation Loss: 1.4436
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- Train Accuracy: 0.5436
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- Epoch: 5
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## Model description
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| 1.0124 | 1.2975 | 0.5564 | 2 |
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| 0.8764 | 1.3194 | 0.5600 | 3 |
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| 0.7594 | 1.3889 | 0.5493 | 4 |
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| 0.6675 | 1.4436 | 0.5436 | 5 |
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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size 498890560
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