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")# 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 tokenizer.json from aquiro1994/naics-github-classifier-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/tokenizer.json
- Command line
-
hf download hf://aquiro1994/naics-github-classifier-multilingual/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/aquiro1994/naics-github-classifier-multilingual/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- f3f777b70ff4f14386777fb0e97286ffe9ed185d17b6fe316b7c55a8442e5318
- Size of remote file:
- 17.1 MB
- SHA256:
- 45885ab5cd9f8d0becc7075cecb177574d1cf4d086a891cac2b745f681d2b3c3
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