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
- Xet hash:
- 9fc6f5dcf0c8e8cc9debd89914598078fd215f64074970d84daf95fa93b21229
- Size of remote file:
- 1.62 MB
- SHA256:
- 19d2d13005f81c8c45f9a146214be13037f4c66a1621e8b3f01b0ad59bc5eaaf
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