Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use tKah/TextClassification-RoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tKah/TextClassification-RoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tKah/TextClassification-RoBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tKah/TextClassification-RoBERTa") model = AutoModelForSequenceClassification.from_pretrained("tKah/TextClassification-RoBERTa") - Notebooks
- Google Colab
- Kaggle
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# TextClassification-RoBERTa
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1953
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- Validation Loss: 0.5320
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# TextClassification-RoBERTa
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on GLUE dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.1953
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- Validation Loss: 0.5320
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