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botfilter is an open-weight model for text classification from Hraness, released under MIT License. Its published files total 23.9 MB.
Botfilter is a browser extension that scores English text for “likely AI-written” as a calibrated probability, on the device. A score can be wrong and should not be used as proof of authorship.
By Hraness, published under mit, revision 38d1d32c357a.
Botfilter is a browser extension that scores English text for “likely AI-written” as a calibrated probability, on the device. A score can be wrong and should not be used as proof of authorship. Each releases/ / directory contains the ONNX model, the WordPiece vocabulary, the calibration settings, and a lockfile with SHA-256 checksums. Use all files from the same version. The extension’s inference implementation defines preprocessing, window aggregation, and calibration; these files do not provide a Transformers pipeline. Human text: openly licensed news articles from Common Pile (only documents whose metadata records CC BY 4.0), Foodista food blog posts (CC BY 3.0), UK Parliament Hansard…
Botfilter is a browser extension that scores English text for “likely AI-written” as a calibrated probability, on the device. A score can be wrong and should not be used as proof of authorship.
Each releases/<version>/ directory contains the ONNX model, the WordPiece vocabulary, the calibration settings, and a lockfile with SHA-256 checksums. Use all files from the same version. The extension’s inference implementation defines preprocessing, window aggregation, and calibration; these files do not provide a Transformers pipeline.
nreimers/MiniLM-L6-H384-uncased (MIT), fine-tuned as a two-class classifier. 22.7M parameters, weight-only int8, 23.6 MB.calibration.json holds the temperature that turns the margin into a probability, and margin thresholds for three settings, Fewer, Balanced, and More, for texts of 25–80 words and longer texts. Texts under 25 words are not scored.Human text: openly licensed news articles from Common Pile (only documents whose metadata records CC BY 4.0), Foodista food blog posts (CC BY 3.0), UK Parliament Hansard debates, and messages crowdworkers wrote in Anthropic's HH-RLHF dataset (MIT). AI text: 6,443 generations from eight Apache-2.0 or MIT open models run by the project (Qwen3, Phi-4, Mistral 7B and Small 24B, OLMo 2, SmolLM3) and seven API models (GPT-6 Sol, GPT-5.5, GPT-5.6 Terra, Claude Sonnet 5, Claude Opus 5.5, Claude Haiku 4.5, Mistral Medium 3.5), written in the same registers as the human text.
Sealed test split, Balanced setting, measured once after the thresholds were set:
| Text | Flagged as likely AI-written |
|---|---|
| Human news, blog, parliamentary, and chat text | 0.1–0.4% |
| Human Reddit posts, never trained on | 0.7% |
| Human work email, never trained on | 1.3% |
| Human GitHub discussion and web pages, never trained on | 2.0–2.2% |
| AI text from the eight open models | 94% |
| AI text from Granite 3.3 and Gemini 3.8 Flash, never trained on | 97% and 88% |
| AI text from Claude Sonnet 5, Opus 5.5, Haiku 4.5 | 82% |
| AI text from GPT-6 Sol, GPT-5.5, GPT-5.6 Terra | 65% |
AI-written posts under 80 words are harder: 82% of short AI social posts are flagged at Balanced. Human posts from X and LinkedIn were not available with suitable rights, so false-positive rates there are unmeasured. Text that a person and a model both edited is often missed. Non-English text is out of scope.
Contains Parliamentary information licensed under the Open Parliament Licence v3.0. Trained on CC BY 4.0 news articles from the Common Pile news collection and CC BY 3.0 posts by Foodista contributors, and on Anthropic HH-RLHF (MIT).
6 files, 23.9 MB in total. The weights are 1 file totalling 23.6 MB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| releases/minilm-l6-v4-20260928/botfilter.int8.onnx | Weights | 23.6 MB | 246b83ee0647 |
| releases/minilm-l6-v4-20260928/calibration.json | Configuration | 320 B | — |
| releases/minilm-l6-v4-20260928/model.lock.json | Configuration | 649 B | — |
| README.md | Documentation | 3.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| releases/minilm-l6-v4-20260928/vocab.txt | Tokenizer | 231.5 KB | — |
Released by Hraness through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 23.6 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. botfilter is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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…