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This model uses SOTA models to detect AI generated content. Label_0 represents Fake Label_1 represents Real View our website: AI Content Detector
Low-bit quantizations (Q4_K_M, IQ3_XXS, IQ2_M, IQ2_XS) of Cloudflare/clef-flash in the native `clef` GGUF architecture. Serve with `llama-server` b11415 or later (`/v1/systemone`, llama.cpp#29831). Run through `llama-server` b11415 (`/v1/systemone`, Vulkan, AMD Radeon 8060S), one request at a time, with the same 600 items for every model: 200 each from SST-2 (validation), AG News (test) and BoolQ (validation), sampled with a fixed seed. Agreement and KL are against `Clef-Flash-Q8_0.gguf` from ggml-org, not against bf16. Livesport's per-tensor layout applied step by step to IQ2_M (all with the head at Q8_0), same 600 items, against the same Q8_0 baseline.
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This is a Spacy multilingual (Catalan & Spanish) anonimization model, for use with BSC's AnonymizationPipeline at: The anonymization pipeline is a library for performing sensitive data identification and ultimately anonymization of the detected data in Spanish and Catalan user generated plain text. The model can detect the following entities: `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP`
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The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.
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The model is a sequence classification model that predicts quality ratings across 4 critera as outputs. The quality ratings (which are unnormalized) can be accessed via the 4 logits in the output of the model. They are in the following order: * Logit 0: Writing Style * Logit 1: Required Expertise * Logit 2: Facts and Trivia * Logit 3: Educational Value **Note:** The model was only fine-tuned on sequences of up to 512 tokens, and should not be used for longer documents. Instead, compute the quality ratings for windows of up to 512 token and average them weighted by the window length. In the paper, we document various types of bias that are present in the quality ratings from the QuRater model (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper). Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases. Note that the quality ratings do not measure the social or literary value of a text and should *not* be used for textual or demographic studies.
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This model is a fine-tuned BERT designed to classify text as either AI-generated or human-written. The model was trained on data from the Kaggle LLM Detect competition using variable-length text inputs ranging from 5 to 100 words. The fine-tuned model achieves high accuracy in identifying the source of the text, making it a valuable tool for detecting AI-generated content. - **Developed by:** Pritam - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Finetuned from model:** BERT (base-uncased) - **Repository:** Hugging Face Model Card - **Demo:** Streamlit Interface
This model is a fine-tuned version of PubMedBERT on the MNLI dataset first and then on the MedNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9501 - Accuracy: 0.8667 The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows: The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0
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