Text Classification
Transformers
Safetensors
English
Chinese
modernbert
reranker
cross-encoder
agent
decision-making
zero-shot-classification
fast-decider
text-embeddings-inference
Instructions to use mkzero/FastDecider-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkzero/FastDecider-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mkzero/FastDecider-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mkzero/FastDecider-149M") model = AutoModelForSequenceClassification.from_pretrained("mkzero/FastDecider-149M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download fast_decider.bundle from mkzero/FastDecider-149M: direct link, hf CLI and curl.
- Browser
- Download file 1.62 MB
-
https://huggingface.co/mkzero/FastDecider-149M/resolve/main/fast_decider.bundle
- Command line
-
hf download hf://mkzero/FastDecider-149M/fast_decider.bundle
-
curl -L -o fast_decider.bundle https://huggingface.co/mkzero/FastDecider-149M/resolve/main/fast_decider.bundle
1.62 MB
- Xet hash:
- 9fc6f5dcf0c8e8cc9debd89914598078fd215f64074970d84daf95fa93b21229
- Size of remote file:
- 1.62 MB
- SHA256:
- 19d2d13005f81c8c45f9a146214be13037f4c66a1621e8b3f01b0ad59bc5eaaf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.