Instructions to use HiAmNear/xlm-roberta-base-ViFE-task-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiAmNear/xlm-roberta-base-ViFE-task-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HiAmNear/xlm-roberta-base-ViFE-task-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiAmNear/xlm-roberta-base-ViFE-task-1") model = AutoModelForSequenceClassification.from_pretrained("HiAmNear/xlm-roberta-base-ViFE-task-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from HiAmNear/xlm-roberta-base-ViFE-task-1: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/HiAmNear/xlm-roberta-base-ViFE-task-1/resolve/main/tokenizer.json
- Command line
-
hf download hf://HiAmNear/xlm-roberta-base-ViFE-task-1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/HiAmNear/xlm-roberta-base-ViFE-task-1/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- bf94f5a565e4ffced6dc7f8af9bf526af5eb9f5df4ac37e60f08c9193e511eb7
- Size of remote file:
- 17.1 MB
- SHA256:
- d2feefc433dea947a9987aabe1d5bce46f14cb86b7b3399e7c04e82c1939e28c
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