Search public pages, research tools, and SAVRN solutions.
aiframe-v3 is an open-weight model for text classification from Neil H, released under MIT License. It has 512-token context. Its published files total 95.8 MB.
Small English classifier behind the AI Frame browser extension, which marks paragraphs that read like AI while you browse. It runs entirely in the browser (transformers.js, ONNX int8); no text leaves the device. Labels: ai and human.
By Neil H, published under mit, revision 33d84f8b20bb.
Small English classifier behind the AI Frame browser extension, which marks paragraphs that read like AI while you browse. It runs entirely in the browser (transformers.js, ONNX int8); no text leaves the device. Labels: ai and human. "Reads like AI" is a signal about style, not a claim about who wrote a text. Trained with soft labels from desklib/ai-text-detector-v1.01 by Desklib (DeBERTa v3 large), released under the MIT License, alongside the hard labels (teacher weight 0.5). Thank you to Desklib for publishing it. Its licence notice: - RAID (Dugan et al., 2024), train split, domains news, reviews, reddit, wiki and books, no adversarial attacks, balanced human and AI, each document cut to…
Small English classifier behind the AI Frame browser extension, which marks paragraphs that read like AI while you browse. It runs entirely in the browser (transformers.js, ONNX int8); no text leaves the device. Labels: ai and human. "Reads like AI" is a signal about style, not a claim about who wrote a text.
Trained with soft labels from desklib/ai-text-detector-v1.01 by Desklib (DeBERTa v3 large), released under the MIT License, alongside the hard labels (teacher weight 0.5). Thank you to Desklib for publishing it. Its licence notice:
desklib/ai-text-detector-v1.01, by Desklib, MIT License.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The teacher was itself trained on RAID, so its scores on the held out RAID windows below may be optimistic.
4005 windows, AUROC 0.9742.
| Threshold | AI caught | Human text flagged |
|---|---|---|
| 0.50 | 96.4% | 22.0% |
| 0.60 | 95.5% | 17.0% |
| 0.70 | 94.5% | 13.6% |
| 0.80 | 93.0% | 10.1% |
| 0.90 | 90.7% | 6.3% |
| 0.95 | 87.7% | 3.8% |
PyTorch before quantisation: AUROC 0.9712. Quantisation: ort_u8 (onnxruntime dynamic int8, unsigned weights, per tensor), largest difference from PyTorch on the sample texts 0.015.
Share of AI caught when the threshold is set so that 1%, 2% or 5% of human windows are flagged, and AUROC, on the same held out windows.
| Model | AUROC | caught at 1% | caught at 2% | caught at 5% |
|---|---|---|---|---|
| Teacher: desklib/ai-text-detector-v1.01 | 0.9698 | 80.4% | 83.5% | 87.7% |
| Student (distilled), int8 ONNX as shipped | 0.9742 | 78.8% | 83.6% | 89.2% |
| Student (distilled), PyTorch | 0.9712 | 75.5% | 81.7% | 88.4% |
| Student (baseline, no teacher), int8 ONNX | 0.9751 | 73.5% | 84.2% | 89.5% |
| Shorla/aiframe-v1, int8 ONNX | 0.9765 | 79.9% | 85.4% | 90.0% |
In the AI Frame extension, the model location is https://huggingface.co/Shorla/aiframe-v3. Files: config.json, tokenizer.json, tokenizer_config.json, special_tokens_map.json, onnx/model_quantized.onnx.
10 files, 95.8 MB in total. The weights are 1 file totalling 87.1 MB in onnx.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| onnx/model_quantized.onnx | Weights | 87.1 MB | 55e780635d03 |
| benchmark.json | Configuration | 3.2 KB | — |
| calibration.json | Configuration | 1.2 KB | — |
| config.json | Configuration | 987 B | — |
| results.json | Configuration | 3.4 KB | — |
| special_tokens_map.json | Configuration | 970 B | — |
| README.md | Documentation | 3.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 8.6 MB | — |
| tokenizer_config.json | Tokenizer | 1.4 KB | — |
Released by Neil H through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 87.1 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. aiframe-v3 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.
512 tokens, from the maximum position embeddings in its published configuration.
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…