Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Sharpaxis/RoBERTa_AI_text_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharpaxis/RoBERTa_AI_text_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sharpaxis/RoBERTa_AI_text_detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sharpaxis/RoBERTa_AI_text_detection") model = AutoModelForSequenceClassification.from_pretrained("Sharpaxis/RoBERTa_AI_text_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model-index:
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- name: RoBERTa_AI_text_detection
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# RoBERTa_AI_text_detection
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an
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It achieves the following results on the evaluation set:
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- Loss: 0.1004
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- F1: 0.9794
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- Transformers 4.45.1
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- Pytorch 2.4.0
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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model-index:
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- name: RoBERTa_AI_text_detection
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results: []
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language:
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- en
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# RoBERTa_AI_text_detection
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an AI vs Human text classification dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1004
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- F1: 0.9794
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- Transformers 4.45.1
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- Pytorch 2.4.0
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- Datasets 3.0.1
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- Tokenizers 0.20.0
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