Text Classification
Transformers
PyTorch
TensorFlow
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use eleldar/language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eleldar/language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eleldar/language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eleldar/language-detection") model = AutoModelForSequenceClassification.from_pretrained("eleldar/language-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "drive/MyDrive/Colab Notebooks/HuggingFace_course/HF_course_community_event/xlm-roberta-base-finetuned-language-detection", "tokenizer_class": "XLMRobertaTokenizer"} |