Text Classification
Transformers
PyTorch
Safetensors
xlm-roberta
sequence-classification
xlm-roberta-base
faq
questions
text-embeddings-inference
Instructions to use timpal0l/xlm-roberta-base-faq-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timpal0l/xlm-roberta-base-faq-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="timpal0l/xlm-roberta-base-faq-extractor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("timpal0l/xlm-roberta-base-faq-extractor") model = AutoModelForSequenceClassification.from_pretrained("timpal0l/xlm-roberta-base-faq-extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timpal0l/xlm-roberta-base-faq-extractor: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/timpal0l/xlm-roberta-base-faq-extractor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timpal0l/xlm-roberta-base-faq-extractor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timpal0l/xlm-roberta-base-faq-extractor/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 67e483f440a705a4bbfc874c5ec1e3cc01c7647ba4342fbb5327a4e1dc218d5f
- Size of remote file:
- 1.11 GB
- SHA256:
- 0252e96539ed8fd66dff2fb19aa2c10282ee8cf40212b8d9caabef7cf60304dd
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