Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use reddgr/tl-test-learn-prompt-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reddgr/tl-test-learn-prompt-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="reddgr/tl-test-learn-prompt-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("reddgr/tl-test-learn-prompt-classifier") model = AutoModelForSequenceClassification.from_pretrained("reddgr/tl-test-learn-prompt-classifier") - Notebooks
- Google Colab
- Kaggle
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# tl-test-learn-prompt-classifier
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0794
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- Train Accuracy: 1.0
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# tl-test-learn-prompt-classifier
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [reddgr/tl-test-learn-prompts](https://huggingface.co/datasets/reddgr/tl-test-learn-prompts) dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0794
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- Train Accuracy: 1.0
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