Instructions to use ctu-aic/FERNET-C5-csfever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctu-aic/FERNET-C5-csfever with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ctu-aic/FERNET-C5-csfever")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ctu-aic/FERNET-C5-csfever") model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/FERNET-C5-csfever", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 619 Bytes
4381237 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"mask_token": "[MASK]",
"name_or_path": "/mnt/data/factcheck/nli_models/lrev_aug22/paper/csfever/fav-kky/FERNET-C5_bs10_ep5_wr0.2",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/ullriher/.cache/huggingface/transformers/7ce72edcead39c025f05d8baeb2ccea5dd4391dd39a1f7946029c85e15147291.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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