Instructions to use sugamjais/xtreme-er with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugamjais/xtreme-er with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sugamjais/xtreme-er")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sugamjais/xtreme-er") model = AutoModelForSequenceClassification.from_pretrained("sugamjais/xtreme-er", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train.csv from sugamjais/xtreme-er: direct link, hf CLI and curl.
- Browser
- Download file 12.3 MB
-
https://huggingface.co/sugamjais/xtreme-er/resolve/main/train.csv
- Command line
-
hf download hf://sugamjais/xtreme-er/train.csv
-
curl -L -o train.csv https://huggingface.co/sugamjais/xtreme-er/resolve/main/train.csv
12.3 MB
- Xet hash:
- 5e452f1fcf725e80b23325e4c0d4c4fd21397bb6d6ab37f2d5ba6c782a80247b
- Size of remote file:
- 12.3 MB
- SHA256:
- 76afc1b7a3db52de186bb768d67a3cad7c3671215ec35c6e672c6812b62c55ce
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