Text Classification
Transformers
Safetensors
xlm-roberta
naics
industry-classification
github
bge-m3
text-embeddings-inference
Instructions to use aquiro1994/naics-github-classifier-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aquiro1994/naics-github-classifier-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aquiro1994/naics-github-classifier-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aquiro1994/naics-github-classifier-multilingual") model = AutoModelForSequenceClassification.from_pretrained("aquiro1994/naics-github-classifier-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
BGE-M3 fine-tuned for NAICS classification, multilingual counterpart of the RoBERTa model
d0b38c1 verified Download model.safetensors from aquiro1994/naics-github-classifier-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 2.27 GB
-
https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/model.safetensors
- Command line
-
hf download hf://aquiro1994/naics-github-classifier-multilingual/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/model.safetensors
2.27 GB
- Xet hash:
- da4c995599d5143e01f3717ac70be2bf93af00b26b708fa9b643d56d3562ab21
- Size of remote file:
- 2.27 GB
- SHA256:
- 9925d8c8aea1d8012f74c8bd074137e853d5b63305a5fe56af9049864127409b
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