Instructions to use cesaredteam/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cesaredteam/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cesaredteam/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cesaredteam/results") model = AutoModelForSequenceClassification.from_pretrained("cesaredteam/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
edteamhf/toxic-edteam-social
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README.md
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: results
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results: []
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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6899
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- Accuracy: 0.8983
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- Precision: 0.4828
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- Recall: 0.9180
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- F1: 0.6328
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## Model description
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: results
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results: []
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# results
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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## Model description
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model.safetensors
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