Instructions to use paritc/multilabelclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paritc/multilabelclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paritc/multilabelclassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paritc/multilabelclassification") model = AutoModelForSequenceClassification.from_pretrained("paritc/multilabelclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from paritc/multilabelclassification: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
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https://huggingface.co/paritc/multilabelclassification/resolve/main/README.md
- Command line
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hf download hf://paritc/multilabelclassification/README.md
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curl -L -o README.md https://huggingface.co/paritc/multilabelclassification/resolve/main/README.md
208 Bytes
metadata
license: unknown
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
datasets:
- SemEvalWorkshop/sem_eval_2018_task_1
tags:
- EPOCHm