Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
scientific-papers
taxonomy
classification
science-of-science
probabilities
qwen3_5
Instructions to use maximerivest/paper-classifier-2B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maximerivest/paper-classifier-2B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="maximerivest/paper-classifier-2B-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maximerivest/paper-classifier-2B-v2") model = AutoModelForCausalLM.from_pretrained("maximerivest/paper-classifier-2B-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 510 Bytes
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"stored_parameters": 2390384448,
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"dtype": "bfloat16",
"architecture": "Qwen3_5ForCausalLM",
"format": "list-first-filler-v1",
"library_versions_tested": {
"transformers": "5.3.0",
"torch": "2.11.0",
"vllm": "0.29.0"
},
"note": "The size tier follows the upstream model name; exported weights include an untied vocabulary-output matrix."
}
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