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
Raihan Hidayatullah Djunaedi
Update README.md to enhance model documentation and examples for zero-shot classification
94028e0 | { | |
| "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 | |
| } | |