Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use heavyhelium/roberta-large-touche-base-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heavyhelium/roberta-large-touche-base-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heavyhelium/roberta-large-touche-base-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("heavyhelium/roberta-large-touche-base-binary") model = AutoModelForSequenceClassification.from_pretrained("heavyhelium/roberta-large-touche-base-binary") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +7 -7
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README.md
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- Macro F1: 0.
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- Fallacy F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Fallacy F1 |
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### Framework versions
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This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7071
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- Accuracy: 0.645
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- Macro F1: 0.6430
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- Fallacy F1: 0.6698
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | Fallacy F1 |
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| 1.3678 | 1.0 | 93 | 0.6908 | 0.515 | 0.3884 | 0.6667 |
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| 1.2771 | 2.0 | 186 | 0.6375 | 0.65 | 0.6500 | 0.6465 |
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| 1.3786 | 3.0 | 279 | 0.7071 | 0.645 | 0.6430 | 0.6698 |
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### Framework versions
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model.safetensors
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