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english-ai-detector is an open-weight model for text classification from Ydh0822, released under other. Its published files total 777.6 MB.
An English human/AI detector for local Python, CLI and browser use, with calibrated probabilities, explicit abstention and exploratory source rankings. AI includes direct generation and substantive AI rewriting, including a human original rewritten by DIPPER.
An English human/AI detector for local Python, CLI and browser use, with calibrated probabilities, explicit abstention and exploratory source rankings. AI includes direct generation and substantive AI rewriting, including a human original rewritten by DIPPER. Results are not an AI-written word percentage, proof of authorship or authenticated model identity. The frozen binary model passed all preregistered point criteria on four necessary final cohorts and both required rewrite-origin views. This is a measured-scope result, not universal accuracy or a population/per-domain 1% false-positive guarantee. Family and nominal-version assertions are disabled; both acceptedfamily and acceptedmodel…
Excerpt from the card by Ydh0822, licensed other.
4 files, 777.6 MB in total.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| LICENSE-MODEL.md | Documentation | 6.3 KB | — |
| README.md | Documentation | 12.8 KB | — |
| english_ai_detector_v2_bundle.zip | Other | 777.6 MB | 8d040a57f387 |
| .gitattributes | Repository | 1.5 KB | — |
Released by Ydh0822 through its official repository on Hugging Face. Read the license.
other, as its publisher declares it. Read the license text before commercial use.
This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. 0 -> Negative; 1 -> Neutral; 2 -> Positive This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.
FinBERT is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. More technical details on FinBERT: Click Link This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using FinBERT for financial tone analysis, give it a try. If you use the model in your academic work, please cite the following paper: Huang, Allen H.…
https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).
This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). This model has been integrated into the TweetNLP library.
The huggingface models currently give different results to the detoxify library (see issue here). For the most up to date models we recommend using the models from https://github.com/unitaryai/detoxify Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary, where we are working to stop harmful content online by interpreting visual content in context. Score not directly comparable since it is obtained on the validation set provided and not on the test set. To update when the test labels are made available. It is also noteworthy to mention…
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class: 1) anger 2) disgust 3) fear 4) joy 5) neutral 6) sadness 7) surprise The model is a fine-tuned checkpoint of DistilRoBERTa-base. For a 'non-distilled' emotion model, please refer to the model card of the RoBERTa-large version. a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab: b) Run emotion model on multiple examples and full datasets (e.g.,.csv files) on Google Colab: Please reach out to [email protected] if you have any…