This model is a fine-tuned version of microsoft/deberta-v3-base designed to detect AI-generated text. This highlights the necessity of domain-specific training for AI detection. Detectors trained on legacy models (2021 era) cannot reliably detect modern SOTA models (2024 era) due to the closing gap in perplexity and burstiness. classifier = pipeline("text-classification", model="vraj33/ai-text-detector-deberta") text = "The quick brown fox..." result = classifier(text) print(result)
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View details ↗Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on `indonlu`'s `SmSA` dataset consisting of Indonesian comments and reviews. After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%. Hugging Face's `Trainer` class from the Transformers library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
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View details ↗- Problem type: Multi-class Classification - Model ID: 492513457 - CO2 Emissions (in grams): 5.527544460835904 - Loss: 0.07609463483095169 - Accuracy: 0.9735624586913417 - Macro F1: 0.9736173135739408 - Micro F1: 0.9735624586913417 - Weighted F1: 0.9736173135739408 - Macro Precision: 0.9737771415197378 - Micro Precision: 0.9735624586913417 - Weighted Precision: 0.9737771415197378 - Macro Recall: 0.9735624586913417 - Micro Recall: 0.9735624586913417 - Weighted Recall: 0.9735624586913417
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View details ↗**Detect AI-generated text from ChatGPT, GPT-5, Claude, Gemini, Llama and other LLMs — free, open weights, runs on CPU.** **tropa-mini** is the open-weights AI detector by wasitaigenerated.com, the small sibling of the **tropa-2** model behind the hosted API. In our benchmark of every notable open-source AI text detector on public datasets, tropa-mini comes out as the **strongest open-weights AI text detector available** — best ROC-AUC and the highest detection rate at a fixed 0.5 % false-positive rate across raw, humanized and frontier-model text (full results below). For long documents, split into ≤768-token chunks (sentence-aligned) and average chunk scores weighted by length. A practical decision threshold at a 0.5 % false-positive operating point is `ai_score ≥ 0.976` (see `serving_head.json`).
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View details ↗📢 `WhereIsAI/UAE-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.** **If you use it for academic papers, you could cite us via 👉 citation info.** - GitHub: https://github.com/SeanLee97/AnglE. - Preprint Paper: AnglE-optimized Text Embeddings - Conference Paper: AoE: Angle-optimized Embeddings for Semantic Textual Similarity (ACL24) - **📘 Documentation**: https://angle.readthedocs.io/en/latest/index.html - 📅 May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference - 📅 Dec 4, 2023 | 🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the MTEB Leaderboard with an average score of 64.64!
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View details ↗A model fine-tuned for sentiment analysis based on vinai/phobert-base. Dataset: 30K e-commerce reviews
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