A 32M-parameter classifier that reads one line of code, config, log or prose and scores whether the line holds a literal credential: a password, API key, access token, private-key header, connection string with a password, or an Authorization header value. It runs on a laptop CPU in about 4 ms per line, so you can score every line of a trace, log or diff before you share it. Use it next to a rule-based scanner, not in place of one. On 1,264 human-written lines it finds about 4 in 5 credentials and is right about 9 times in 10 when it flags a line. It also misses about 1 credential in 5, so it must never be the only check before you publish a file. predictonnx.py applies the temperature…
Open weights
mit
32M parameters
7,999 tokens
transformers
A static-embedding intent classifier for the Thalovant hub. It maps an utterance to one of 214 labels of the form:, exactly as ovos-m2v-pipeline registers them. The labels are the fleet's skills and the other intents the hubs register (the persona and reading pipelines, wikihow), from the fleet corpus and nothing else. There is no "none of these" label: a sentence about something no skill does still gets the nearest skill's label, often with high confidence. Base: Jarbas/m2v-256-distiluse-base-multilingual-cased-v2, fine-tuned with model2vec's classifier trainer. Measured on sentences the classifier never saw: 93188 held-out rows whose text, in any language, and whose intent line are both…
Open weights
apache-2.0
31M parameters
model2vec
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
23M parameters
512 tokens
transformers
Baseline test accuracy: 84.49%; Fine-tuned test accuracy: 99.21%.
Open weights
23M parameters
512 tokens
transformers
This is my fine-tuned model for Homework 1. The model classifies HC3 answers as: - 0: human - 1: ChatGPT sentence-transformers/all-MiniLM-L6-v2 I used the fixed dataset splits provided in the notebook.
Open weights
23M parameters
512 tokens
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Open weights
23M parameters
512 tokens
transformers
