Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use prahlad/rotten_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prahlad/rotten_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prahlad/rotten_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prahlad/rotten_model") model = AutoModelForSequenceClassification.from_pretrained("prahlad/rotten_model") - Notebooks
- Google Colab
- Kaggle
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# prahlad/rotten_model
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on
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It achieves the following results on the evaluation set:
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- Train Loss: 0.4876
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- Train Accuracy: 0.7620
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# prahlad/rotten_model
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on rotten_tomatoes movie review dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.4876
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- Train Accuracy: 0.7620
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