Text Classification
Transformers
Safetensors
multilingual
deberta-v2
custom_code
text-embeddings-inference
Instructions to use utter-project/EuroFilter-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utter-project/EuroFilter-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="utter-project/EuroFilter-v1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("utter-project/EuroFilter-v1", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("utter-project/EuroFilter-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Ricardo Rei commited on
Commit ·
352467e
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Parent(s): 44e6fb6
README update
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README.md
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language:
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- multilingual
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license: apache
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---
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## Running Model:
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To run inference you must install
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```
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---
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pipeline_tag: classification
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language:
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- multilingual
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license: apache 2.0
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library_name: transformers
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---
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# Model Description
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This model was build by translating the fine-Edu annotations into 15 languages using the best translation LLM: Tower LLM 70B.
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The translation model excels at translating entire documents and thus its the perfect fit to translate the texts we will use to train our classifier.
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The classifier is trained for English, German, Spanish, Japanese, Chinese, Russian, Hindi, Czech, Ukrainian, Icelandic, Portuguese, French, Dutch, Italian and Korean. Since its build on top of [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) it should be able to generalize across other languages.
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## Running Model:
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To run inference you must install
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```
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