Text Classification
Transformers
Safetensors
xlm-roberta
multilabel-classification
multilingual
easy-to-read
text-simplification
sentence-pair-classification
multilingual-e5
accessibility
custom_code
text-embeddings-inference
Instructions to use hannah-khallaf/e2r-strategy-multilingual-e5-large-bce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hannah-khallaf/e2r-strategy-multilingual-e5-large-bce with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hannah-khallaf/e2r-strategy-multilingual-e5-large-bce", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hannah-khallaf/e2r-strategy-multilingual-e5-large-bce", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("hannah-khallaf/e2r-strategy-multilingual-e5-large-bce", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 190 Bytes
7b6efa2 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"input_mode": "pair",
"standard_prefix": "query: ",
"rewrite_prefix": "query: ",
"max_length": 512,
"truncation": true,
"sentence_order": [
"standard",
"rewrite"
]
}
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