Text Classification
Transformers
ONNX
Safetensors
Transformers.js
English
roberta
fake-news-detection
misinformation
fact-checking
liar
Eval Results (legacy)
text-embeddings-inference
Instructions to use emonHasan01/liar-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emonHasan01/liar-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emonHasan01/liar-roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("emonHasan01/liar-roberta") model = AutoModelForSequenceClassification.from_pretrained("emonHasan01/liar-roberta", device_map="auto") - Transformers.js
How to use emonHasan01/liar-roberta with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-classification', 'emonHasan01/liar-roberta'); - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from emonHasan01/liar-roberta: direct link, hf CLI and curl.
- Browser
- Download file 3.56 MB
-
https://huggingface.co/emonHasan01/liar-roberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://emonHasan01/liar-roberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/emonHasan01/liar-roberta/resolve/main/tokenizer.json
3.56 MB
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