Zero-Shot Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
text-classification
tensorflow
nli
natural-language-inference
Eval Results (legacy)
Instructions to use nahiar/zero-shot-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/zero-shot-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="nahiar/zero-shot-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/zero-shot-classification") model = AutoModelForSequenceClassification.from_pretrained("nahiar/zero-shot-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 426 Bytes
94028e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"model_max_length": 512,
"tokenizer_class": "XLMRobertaTokenizer",
"do_lower_case": false,
"bos_token": "<s>",
"eos_token": "</s>",
"sep_token": "</s>",
"cls_token": "<s>",
"unk_token": "<unk>",
"pad_token": "<pad>",
"mask_token": "<mask>",
"special_tokens_map_file": null,
"name_or_path": "xlm-roberta-large",
"tokenize_chinese_chars": true,
"strip_accents": null,
"do_basic_tokenize": true
}
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