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
File size: 795 Bytes
08c47e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"add_cross_attention": false,
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "REAL",
"1": "FAKE"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"FAKE": 1,
"REAL": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"tie_word_embeddings": true,
"transformers_version": "5.19.0",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 50265
}
|