Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use harshitaskh/test_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshitaskh/test_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harshitaskh/test_trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harshitaskh/test_trainer") model = AutoModelForSequenceClassification.from_pretrained("harshitaskh/test_trainer") - Notebooks
- Google Colab
- Kaggle
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README.md
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# test_trainer
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the
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It achieves the following results on the evaluation set:
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- Loss: 0.0000
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- Accuracy: 1.0
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# test_trainer
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the Fakenews dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0000
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- Accuracy: 1.0
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