Text Classification
Transformers
Safetensors
English
modernbert
fill-mask
decision-model
routing
classification
verification
brier-score
calibration
bidirectional
Eval Results (legacy)
Instructions to use mpnikhil/dev-0.4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpnikhil/dev-0.4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpnikhil/dev-0.4b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mpnikhil/dev-0.4b") model = AutoModelForMaskedLM.from_pretrained("mpnikhil/dev-0.4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download assets/benchmark_scorecard.png from mpnikhil/dev-0.4b: direct link, hf CLI and curl.
- Browser
- Download file 136 kB
-
https://huggingface.co/mpnikhil/dev-0.4b/resolve/main/assets/benchmark_scorecard.png
- Command line
-
hf download hf://mpnikhil/dev-0.4b/assets/benchmark_scorecard.png
-
curl -L -o benchmark_scorecard.png https://huggingface.co/mpnikhil/dev-0.4b/resolve/main/assets/benchmark_scorecard.png
136 kB

- Xet hash:
- 33f85bdadaa006f6ffa1416226524206af15c9707694599bf55f5f5ce85b3782
- Size of remote file:
- 136 kB
- SHA256:
- bc6ff3631f294bcba457e80d8cbffc95a7674c362fd8e6d06ddc3a8e644e4402
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.