Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use papluca/xlm-roberta-base-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papluca/xlm-roberta-base-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection") model = AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-language-detection
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0103
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- Accuracy: 0.9977
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- F1: 0.9977
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## Model description
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More information needed
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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results: []
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# xlm-roberta-base-language-detection
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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## Intended uses & limitations
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You can directly use this model as a language detector, i.e. for sequence classification tasks. Currently, it supports the following 20 languages:
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`arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)`
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## Training and evaluation data
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It achieves the following results on the evaluation set:
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- Loss: 0.0103
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- Accuracy: 0.9977
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- F1: 0.9977
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## Training procedure
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