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).
hw1-hc3-detector is an open-weight model for text classification from Singh, released under Apache License 2.0. It has 23M parameters and a 512-token context. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
A fine-tuned sentence-transformers/all-MiniLM-L6-v2 classifier that labels an answer as human-written (0) or ChatGPT-generated (1), trained on the HC3 dataset (English, pinned revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3).
Parameters23M
Context512
Weights90.9 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve hw1-hc3-detector (23M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.0 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
hw1-hc3-detector on every accelerator the SAVRN Index prices, at every precision
Model Card
By Singh, published under apache-2.0, revision ce49070261a6.
A fine-tuned sentence-transformers/all-MiniLM-L6-v2 classifier that labels an answer as human-written (0) or ChatGPT-generated (1), trained on the HC3 dataset (English, pinned revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3). - Full fine-tuning with a sequence classification head (2 labels) - 10 epochs, AdamW, learning rate 2e-5, no scheduler - I chose this model from five runs by comparing test accuracy, so 99.27% is probably slightly optimistic.
Read Singh's full model card
HW1 HC3 AI-Text Detector
A fine-tuned sentence-transformers/all-MiniLM-L6-v2 classifier that labels an answer as human-written (0) or ChatGPT-generated (1), trained on the HC3 dataset (English, pinned revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3).
Results (held-out test set, 4,668 answers)
| Model | Test accuracy |
|---|---|
Baseline: frozen all-MiniLM-L6-v2 embeddings + logistic regression |
0.8449 (84.49%) |
| Fine-tuned model (this repo) | 0.9927 (99.27%) |
Training details
- Full fine-tuning with a sequence classification head (2 labels)
- 10 epochs, AdamW, learning rate 2e-5, no scheduler
- Batch size 32, max sequence length 256, seed 42
Limitations
- I chose this model from five runs by comparing test accuracy, so 99.27% is probably slightly optimistic.
| Model | Description | Test accuracy |
|---|---|---|
| Frozen baseline | Frozen all-MiniLM-L6-v2 embeddings with logistic regression |
84.49% |
| Model 1 | Fine-tuned for 5 epochs, learning rate 2e-5, no scheduler |
99.01% |
| Model 2 | Fine-tuned for 15 epochs, learning rate 5e-6, linear scheduler |
97.86% |
| Model 3 | Fine-tuned for 5 epochs, learning rate 2e-5, linear scheduler |
98.44% |
| Model 4 | Fine-tuned for 5 epochs, learning rate 1e-5, no scheduler |
98.54% |
| Model 5 (this repo) | Fine-tuned for 10 epochs, learning rate 2e-5, no scheduler |
99.27% |
Configuration
- Architecture
- BertForSequenceClassification
- Context length (tokens)
- 512
- Layers
- 6
- Hidden size
- 384
- Feed-forward size
- 1,536
- Attention heads
- 12
- Vocabulary size
- 30,522
- Model type
- bert
Identity and Version
- Repository
- dipalis2/hw1-hc3-detector
- Publisher
- Singh
- Task
- Text classification
- Modality
- Text
- Library
- Not stated by the source
- Parameters
- 23M parameters
- Languages
- en
- Revision
- ce49070261a69a9b25488efa595d65b8efcef690
- First published
- 2026-10-01
- Last updated
- 2026-10-02
Files and Weights
6 files, 91.6 MB in total. The weights are 1 file totalling 90.9 MB in safetensors.
Weights1 file · 90.9 MB
Configuration1 file · 814 B
Tokenizer2 files · 712.1 KB
Documentation1 file · 1.6 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 90.9 MB | 6852b851cae9 |
| config.json | Configuration | 814 B | — |
| README.md | Documentation | 1.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.5 KB | — |
| tokenizer_config.json | Tokenizer | 594 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 90.9 MB
Download from Singh
Released by Singh through its official repository on Hugging Face. Read the license.
Built From
- Derived from sentence-transformers/all-MiniLM-L6-v2
- Trained on (disclosed) Hello-SimpleAI/HC3
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 90.9 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About hw1-hc3-detector
How much GPU memory does hw1-hc3-detector need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (23M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run hw1-hc3-detector on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use hw1-hc3-detector commercially?
Yes. hw1-hc3-detector is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is hw1-hc3-detector's context length?
512 tokens, from the maximum position embeddings in its published configuration.
Similar Models
Baseline test accuracy: 84.49%; Fine-tuned test accuracy: 99.21%.
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.
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).
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).
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).