Text Classification
Transformers
PyTorch
xlm-roberta
classification
Generated from Trainer
text-embeddings-inference
Instructions to use Atram11/ClassificationLanguage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Atram11/ClassificationLanguage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Atram11/ClassificationLanguage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Atram11/ClassificationLanguage") model = AutoModelForSequenceClassification.from_pretrained("Atram11/ClassificationLanguage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 541 Bytes
5f0170e | 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 | {
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"eos_token": "</s>",
"label": 51,
"mask_token": {
"__type": "AddedToken",
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"max_length": 512,
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"stride": 0,
"tokenizer_class": "XLMRobertaTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "<unk>"
}
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