Text Classification
Transformers
Safetensors
Bosnian
Croatian
Serbian
xlm-roberta
language-identification
bosnian
croatian
serbian
south-slavic
text-embeddings-inference
Instructions to use absltnull/RoBERTo-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use absltnull/RoBERTo-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="absltnull/RoBERTo-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("absltnull/RoBERTo-v2") model = AutoModelForSequenceClassification.from_pretrained("absltnull/RoBERTo-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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language:
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license: mit
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library_name: transformers
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tags:
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base_model: absltnull/RoBERTo
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pipeline_tag: text-classification
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---
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# RoBERTo-v2 :: bs / hr / sr Language Identifier
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```
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## TL;DR
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Also, if I haven't made it obvious yet... don't use this with Cyrillic. The model was trained to be good at Latin. That's about it. Cya.
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language:
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- bs
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- hr
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- sr
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license: mit
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library_name: transformers
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tags:
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- text-classification
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- language-identification
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- xlm-roberta
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- bosnian
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- croatian
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- serbian
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- south-slavic
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base_model: absltnull/RoBERTo
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pipeline_tag: text-classification
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datasets:
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- absltnull/bcs-lang-id
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---
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# RoBERTo-v2 :: bs / hr / sr Language Identifier
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```
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## TL;DR
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Also, if I haven't made it obvious yet... don't use this with Cyrillic. The model was trained to be good at Latin. That's about it. Cya.
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